Vehicle mini gps signal jammer tech , jammer technique optimization miami

Photo: peeterv/iStock/Getty Images Plus/Getty Images Complexity and Context: Key Challenges of Multisensor Positioning By Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Four key challenges must be met: complexity, context, ambiguity, and environmental data handling. Although many new navigation and positioning methods have been developed in recent years to address GNSS shortcomings in terms of signal penetration and interference vulnerability, little has been done to bring them together into a robust, reliable, and cost-effective integrated system. New positioning techniques investigated over the past 15 years include:Wi-Fi; ultra-wideband; phone signals; television and other signals of opportunity; Bluetooth; lasers, and dead reckoning; pedestrian dead reckoning (PDR) using step detection; pedestrian and activity-based map matching; magnetic anomaly matching; and GNSS shadow matching. There have also been improvements to existing technologies: visual navigation, dead-reckoning algorithms, micro-electro-mechanical systems, inertial sensing with cold-atom technology, nuclear magnetic resonance gyros, distance-measuring equipment, Loran, Doppler with Iridium, multiple GNSS constellations, network assistance, and augmentation by commercial pseudolite systems. In the next generation, a universal navigation system might be expected to provide position within 3 meters at any location with a very high reliability. No single positioning technology is capable of meeting the most demanding application requirements. Radio signals may or may not be subject to obstruction, attenuation, reflection, jamming, and/or interference. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multisensor solution is thus required. A robust, reliable, and cost-effective integrated system must meet four key challenges: Complexity. How to find the necessary expertise to integrate a diverse range of technologies, how to combine technologies from different organizations that wish to protect their intellectual property, how to incorporate new technologies and methods without having to redesign the whole system, and how to share development effort over a range of different applications. Context. How to ensure that the navigation system configuration is optimized for the operating environment and host vehicle (or pedestrian) behavior when both are subject to change. Ambiguity. How to handle multiple hypotheses, including measurements of non-unique environmental features, pattern-matching fixes where the measurements match the database at multiple locations, and uncertain signal properties, such as whether reception is direct or non-line-of-sight (NLOS). Environmental Data Handling. How to gather, distribute, and store the information needed to identify signals and environmental features and define their points of origin or spatial variation. Complexity Achieving robust positioning in challenging environments potentially requires a large number of subsystems. For example, Figure 1 shows the possible components of a pedestrian navigation system using sensors found in a typical smartphone. Figure 2 shows possible components of a car navigation system using equipment already common on cars and other suitable low-cost sensors. Some technologies are common to the two platforms, while others differ. Figure 1. Potential components of a pedestrian navigation system using smartphone sensors. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 2. Potential components of a car navigation system using commonly available equipment and other low-cost sensors. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Any multisensor navigation or positioning system needs integration algorithms to obtain the best overall position solution from the constituent subsystems. These algorithms must not only input and combine measurements from a wide range of subsystems, but also calibrate systematic errors in those subsystems. Designing the integration algorithms therefore requires expertise in all of the subsystems, which can be difficult to establish in a single organization. The more subsystems there are, the more of a problem this is. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected. In a typical smartphone, one company supplies the GNSS chip, another supplies the Wi-Fi positioning service, a third organization supplies the mapping, the network operator provides the phone-signal positioning, a fifth company provides the inertial and magnetic sensors, and a sixth company produces the operating system. Because of lack of cooperation between these different organizations, useful information gets lost. For example, GNSS pseudo-range measurements are not normally available to application developers. A further issue is reconfigurability. To minimize development costs, manufacturers share algorithms and software across different products, incorporating different subsystems. They also want to minimize the cost of adding new sensors to a product to improve performance. Similarly, researchers want to compare different combinations of subsystems. However, with a conventional system architecture, modifications must be made throughout the integration algorithm each time a subsystem is added, removed, or replaced. The more subsystems there are, the more complex this task becomes. For a given application, different subsystems may also be used at different times. For example, a smartphone may use Wi-Fi positioning indoors and GNSS outdoors and may deploy different motion constraints and map matching algorithms, depending on whether the device is carried by a pedestrian or traveling in a car. Different integration algorithms for different configurations are more processor efficient, but also require more development effort. Conversely, an all-subsystem integration algorithm is quicker to develop, but can waste processing resources handling inactive subsystems. Modular Integration. The solution to these problems is a modular integration architecture, consisting of a universal integration filter module and a set of configuration modules, one for each subsystem. The integration filter module would be designed by data fusion experts without the need for detailed knowledge of the subsystems. It would accept a number of generic measurement types, such as position fixes and pseudo-ranges, with associated metadata. The configuration modules would be developed by the subsystem suppliers and would convert the subsystem measurements into a format understood by the filter module and supply the metadata. They would also mediate the feedback of information from the integration filter to the subsystems. The metadata comprises the additional information required to integrate the measurements such as the measurement type and any coordinate frame(s) used. a sensor identification number (to distinguish measurements of the same type from different sensors). statistical properties of the random and systematic measurement errors. identification numbers and locations of transmitters and other landmarks. A key advantage of this approach is that subsystems may be changed without the need to modify the integration filter. Provided the new subsystem is compatible, all that is needed is the corresponding configuration module. Figure 3 shows an example of a modular integration architecture for a combination of conventional GNSS positioning, GNSS shadow matching, Wi-Fi positioning, and PDR. As well as providing measurements and associated statistical data to the integration filter module, the configuration modules feedback relevant information to the subsystems. Shadow matching works by comparing measured and predicted signal availability over a number of candidate positions, so requires a search area to be specified using other positioning technologies. PDR uses information from other sensors, where available, to calibrate the coefficients of its step length estimation model and correct for heading drift. Conventional GNSS positioning can also benefit from position and velocity aiding to support acquisition and tracking of weak signals in indoor and urban environments. Figure 3. Modular integration of conventional GNSS, shadow matching, PDR, and Wi-Fi positioning for pedestrian navigation (different colors denote potentially different suppliers). (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) In principle, each subsystem configuration module could simply supply a position fix to the integration filter module with an associated error covariance. However, other forms of measurement generally give better results. For conventional GNSS positioning, the advantages of tightly coupled (range- domain) integration over loosely coupled (position-domain) are well known. PDR is a dead-reckoning technique, so measures distance traveled rather than position. Consequently, providing measurements of position displacement and direction can avoid cumulative errors in the measurement stream. GNSS shadow matching and some types of Wi-Fi positioning use the pattern-matching positioning method. This scores an array of candidate position solutions according to the match between the measured and predicted signal availability or signal strength. Although the output of these algorithms is in the position domain, a likelihood distribution can provide more information for the integration filter than a simple mean and covariance. Other navigation and positioning techniques generate further types of measurement, including velocity, attitude, specific force, angular rate, range rate, and bearings and elevations of features. The types of measurement depend on the positioning method. A universal integration filter must operate without prior knowledge of which measurements it must process and which states it must estimate. Consequently, it must reconfigure its measurement vector, state vector, and associated matrices according to the measurements available, using the metadata supplied by the configuration module. This capability is sometimes called “plug and play,” and a number of prototypes have been developed by different research groups. The integration filter must be capable of implementing either error-state or total-state integration, depending on the measurements available. In error-state integration, one of the subsystems, such as inertial navigation, provides a reference navigation solution. The integration filter estimates corrections to that solution using the measurements from other subsystems. In total-state integration, the integration filter estimates the position and velocity directly, and an additional configuration module provides information on the host vehicle (or pedestrian) dynamics. Modular integration algorithms could form part of a wider modular integrated navigation concept in which subsystem hardware and software is shared across a range of applications. Issues to Resolve A critical requirement for the successful implementation of modular integration is an open-standard interface for communication between the universal filter and configuration modules. This enables modules produced by different organizations to work together. To realize the full benefits of modular integration, in terms of interoperability and software re-use, there should be a single standard covering the consumer, professional, research, and military user communities and spanning all of the application domains air, sea, land, indoor, underwater, and so forth. A standard developed by one group in isolation is unlikely to meet the needs of the whole navigation and positioning community, while the development of multiple competing standards defeats the main purpose of modular integration. This interface should be defined in terms of fundamental measurement types, such as position, velocity, and the ranges, bearings, and elevations of signals and features. However, there are many different coordinate systems that may be used and positioning may be in 2 or 3 dimensions, while ranging measurements may be true ranges or pseudoranges. Ranging and angular positioning measurements may be differenced across transmitters or landmarks, differenced across receivers or sensors, or double differenced across both. A universal interface must support every measurement type that requires different processing by the filter module. However, it need not support formats that are easily convertible. Thus, there is no need to support both the north, east, down, and east, north, up conventions. There are two main approaches to defining the fundamental measurement types: A minimal number of very generic measurement types with metadata used to describe how these should be processed by the integration filter. A large number of more specific measurement types for which the processing methodology is already known. For each measurement type, an error specification must be defined. For error sources assumed to be white, a standard deviation or power spectral density (PSD) is required. For correlated errors, such as biases, information on the time correlation is required alongside variances and covariance information. The interface standard should include every conceivable error source. Unused errors can simply be zeroed. The filter module should then use the error specification to determine which error sources to model and how. Obtaining reliable navigation sensor error specifications can be difficult. Manufacturers often provide only limited information, while performance in the field can be different from that in the laboratory due to vibration and electromagnetic interference. For new positioning techniques, the error behavior may not be fully understood, while complex error behavior can be difficult to measure. Adaptive estimation techniques provide only a partial solution. Even where the error behavior is well known, it can be too complex to practically model within the estimation algorithm. This could represent a fifth challenge. For subsystems used as the reference in an error-state integration filter, such as an inertial navigation system (INS), the errors will typically be correlated across the different components of the subsystem navigation solution, for example position, velocity, and attitude. Furthermore, to represent the error behavior within an integration algorithm, it is necessary to model the error properties of the underlying sensors, accelerometers and gyroscopes in the case of inertial navigation. Thus, it is likely that additional compound measurement types for reference system data will be needed. For pseudorange measurements, an issue to consider is the synchronization of different transmitter and receiver clocks. Clocks in receivers for different types of signal, such as GNSS and Loran, may or may not be synchronized with each other. Also, the transmitter clocks are typically synchronized in groups. For example, the GPS satellite clocks are synchronized with each other, as are the GLONASS satellite clocks, but GLONASS is not currently synchronized with GPS. For optimal integration of pseudoranges from different sources, this information must be conveyed to the integration filter. The interface standard for communication between the filter and configuration modules must also support feedback of information from the integration filter to the subsystems, via the configuration modules. The integrated position, velocity, and attitude solution, with its associated error covariance, is useful for aiding many different subsystems. Therefore, a generic standard for this should be defined. Conversely, the feedback to the subsystems of calibration parameters estimated by the integration algorithm is sensor specific, so should be incorporated in the definitions of the fundamental measurement types. The user requirements, such as accuracy, integrity, continuity, solution availability, update rate, and power consumption, can vary greatly between applications. For example, accuracy is important for surveying, integrity for civil aviation, solution availability for many military applications, and power consumption for many consumer applications. This impacts the design of the whole navigation system. Different modules could be used for different applications. However, it is more efficient if the components adapt to different environments. Figure 4 shows how requirements information can be disseminated in a modular integrated navigation system. Figure 4. Modular integration architecture incorporating requirements. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) An open-standard interface specification should be able to handle any conceivable navigation and positioning system. However, it is more efficient if the components adapt to different environments. Similarly, there will be differences in the error magnitudes that an integration filter can handle and in its capability to handle non-Gaussian error distributions. Variations in fault detection and integrity monitoring capability can also be expected. Consequently, there must be a capability specification for each filter module and a protocol for handling mismatches between the measurements and the filter module, and a means to certify that a filter module actually has the claimed capabilities. (Further discussion of modular integration may be found in our IEEE/ION PLANS 2014 paper, “The Four Key Challenges of Advanced Multisensor Navigation and Positioning,” and the Journal of Navigation paper, “The Complexity Problem in Future Multisensor Navigation and Positioning Systems: A Modular Solution.”) Context Context is the environment that a navigation system operates in and the behavior of its host vehicle or user. Examples include a pedestrian walking (behavior) in an urban street (environment), a car driving at highway speeds on an open road, and an airliner flying high above an ocean. Context is critical to the operation of a navigation or positioning system. The environment affects the types of signals available. For example, GNSS reception is poor indoors while Wi-Fi is not widely available outside towns and cities. In underwater environments, most radio signals cannot propagate so acoustic signals are used instead. Processing techniques can also be context dependent. For example, in open environments, non-line-of-sight (NLOS) reception of GNSS signals or multipath interference may be detected using consistency checking techniques based on sequential elimination. However, in dense urban areas, more sophisticated algorithms are required and may be enhanced using 3D city models. GNSS shadow matching only works in outdoor urban environments. Navigation using environmental feature matching is inherently context-dependent as different types of feature are available in different environments. Suitable algorithms, databases, and sensors must be selected. For example, terrain referenced navigation (TRN) uses radar or laser scanning in the air, sonar or echo sounding at sea, and barometric pressure on land. Map matching requires different approaches for cars, trains, and pedestrians. Similarly, algorithms and databases for image-based navigation depend on the types of feature available, which vary with the environment. Behavioral context is also important and can contribute additional information to the navigation solution. For example, cars normally remain on the road, effectively removing one dimension from the position solution. Their wheels also impose constraints on the way they can move, reducing the number of inertial sensors required to measure their motion. Similarly, PDR using step detection depends inherently on the characteristics of human walking. Using PDR for vehicle navigation or vehicle motion constraints for pedestrian navigation will produce errors. Host vehicle behavior is also important for tuning the dynamic model within a total-state navigation filter and for detecting faults through discrepancies between measured and expected behavior. Within a GNSS receiver, the behavior can be used to set tracking loop bandwidths and coherent correlator accumulation intervals, and to predict the temporal variation of multipath errors. The antenna placement on a vehicle or person can also affect performance. Historically, context was implicit; a navigation system was designed to be used in a particular type of vehicle, handling its associated behavior and environments. However, many navigation systems now need to operate in a variety of different contexts. For example, a smartphone moves between indoor and outdoor environments and can be stationary, on a pedestrian, or in a vehicle. Similarly, a small surveillance drone may operate from above, amongst buildings, or even indoors. At the same time, most of the new positioning techniques developed to enable navigation in challenging environments, are context-dependent. To make use of these techniques in practical applications (as opposed to research demonstrators), it is necessary to know the context. Context-Adaptive Navigation The solution to the problem of using context-dependent navigation techniques in variable-context applications is context-adaptive navigation. As shown in  Figure 5, the navigation system detects the current environmental and behavioral context and, in real time, reconfigures its algorithms accordingly. For example, different radio positioning signals and techniques may be selected, inertial sensor data may be processed in different ways, different map-matching algorithms may be selected, and the tuning of the integration algorithms may be varied. Figure 5. A context-adaptive navigation system. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Previous work on context-adaptive navigation and positioning focused on individual subsystems and concerned either behavioral or environmental context, not both. For example, there has been substantial research into classifying pedestrian motion using inertial sensors to enable PDR algorithms using step detection to estimate the distance travelled from the detected motion. The context information may also be used for non-navigation purposes. Typically, orientation-independent signals are generated from the accelerometer and gyro outputs. Statistics such as the mean, standard deviation, root mean squared (RMS), inter-quartile range, mean absolute deviation, maximum−minimum, maximum magnitude, number of zero crossings, and number of mean crossings are then determined from a few seconds of data. Frequency-domain statistics may also be used. Finally, a pattern recognition algorithm is used to match these parameters to the stored characteristics of different combinations of activity types and sensor locations. Detection of road-induced vibration using accelerometers has been used to determine whether or not a land vehicle is stationary, while a calibrated yaw-axis gyro can be used to determine when a vehicle is travelling in a straight line. Indoor and outdoor environments may be distinguished using GNSS carrier-power-to-noise-density ratio (C/N0 ) measurements. Wi-Fi signals might also be used for environmental context detection. Context Detection Experiments We have conducted a number of different context-detection experiments using GNSS, Wi-Fi, and accelerometers. Full details are presented in our ION GNSS+ 2013 paper, “Context Detection, Categorization and Connectivity for Advanced Adaptive Integrated Navigation,” and in our PLANS 2014 paper. Here, some highlights from the results are presented. GNSS. GNSS data was collected at five locations inside and immediately outside UCL’s Grant Museum of Zoology; these are shown in Figure 6. C/N0 measurement data was collected from all GPS and GLONASS signals received by a Samsung Galaxy S3 Android smartphone. About 60 seconds of data was collected at each site. Figure 7 presents histograms of the C/N0 measurements and Table 1 lists the means and standard deviations. Figure 6. Locations for the GNSS indoor/outdoor context detection experiment. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 7. GNSS C/N0 measurement distributions at sites inside and immediately outside UCL’s Grant Museum of Zoology. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Table 1. Means and standard deviations of GNSS C/N0 measurements inside and outside UCL’s Grant Museum of Zoology. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) As expected, the average received C/N0 is lower indoors than outdoors and lower deep indoors than near the entrance. Furthermore, the standard deviation of the C/N0 measurements is larger outdoors than indoors and also larger near the entrance to the building than deep indoors. Thus, both the mean and the standard deviation of the measured C/N0 across all GNSS satellites tracked are useful both for detecting indoor and outdoor contexts and for distinguishing between different types of indoor environment. Indoor/Outdoor Detection, Wi-Fi. Tests in and around several UCL buildings have shown no clear relationship between Wi-Fi SNRs and environmental context. However, as the environment changes, there is a rapid change in the Wi-Fi SNRs over a few epochs. For a user moving from inside to outside of a particular building, those signals which originate inside go from strong to weak, while many of those from neighboring buildings become stronger. Consequently, Wi-Fi signals could potentially be used to detect context changes instead of the absolute context. This is useful for improving the overall robustness of context determination. To test this, Wi-Fi data was collected using a Samsung Galaxy S3 smartphone along a route with both indoor and outdoor sections and a context-change score calculated from the last six epochs of data at 1-second intervals. Context-change score results are presented in Figure 8. The large blue blocks indicate when the user was outside and the smaller blue block shows when the user was in the building’s basement, a very different Wi-Fi environment. As can be seen, there are clear peaks in the “context change” score whenever the user moves between indoor and outdoor contexts. However, there are also peaks when the user enters and leaves the basement, so the technique is sensitive to false positives and must be combined with other context detection techniques to be used reliably. Figure 8. Context-change score computer from Wi-Fi SNR measurements. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Behavioral Detection, Accelerometers. The use of accelerometers to detect behavioral context is well established. However, by looking at the vibration spectra, more information can be extracted. For these experiments, specific force data was collected using an Xsens MTi-G IMU/GNSS device, the mean subtracted to remove most of the gravity, and a discrete Fourier transform obtained using the MATLAB function fft. Figures 9 and 10 respectively show the vibration spectra of the specific force magnitude for an IMU on a table and held by a stationary pedestrian. The table spectrum is approximately white, whereas the pedestrian data shows peaks between 6 and 10 Hz. Figure 9. IMU spectra on a table. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 10. IMU spectra, stationary pedestrian. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figures 11 and 12 respectively show the vibration spectra of a stationary Vauxhall Insignia car, and a stationary urban electric train. Here, the individual accelerometer spectra are shown. In each case, the x-axis was pointing forward, the y-axis to the right and the z-axis down. The car exhibits a lot of vibration at frequencies above 10 Hz due to its engine, whereas the dominant train vibration peak is around 1.5 Hz, with smaller peaks at 15 Hz, 25 Hz, 33 Hz, and 50 Hz, the mains power frequency. Thus, the two vehicles are very different from each other and also from the pedestrian. Figure 13 then shows the vibration spectrum of the car moving on a high-speed road. As might be expected, there is much more vibration when moving with broad peaks below 15 Hz due to road vibration and above 15 Hz due to engine vibration. Figure 11. Specific force frequency spectrum of a stationary car. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 12. Specific force frequency spectrum of a stationary train. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Figure 13. Specific force frequency spectrum of a car traveling on a high- speed road. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Finally, Figure 14 shows the vibration spectra on an escalator at an underground rail station. The IMU was in the trouser pocket of a pedestrian. Vibration at a range of frequencies below 30 Hz can be seen and it was observed that the resonant frequencies vary between individual escalators. Figure 14. Specific force frequency spectrum on an escalator. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Issues to Resolve Despite the work done with individual sensors, a multisensor integrated navigation system that adapts to both environmental and behavioral context remains at the concept stage. Realizing this in a practical system requires both effective context determination and a set of context categories standardized across the whole navigation and positioning community. The first step in the standardization process is to establish a framework suitable for navigation and positioning. Each context category must map to a configuration of the navigation system; otherwise, it serves no purpose. Multiple categories may map to the same configuration as different navigation systems will respond to different context information. In an autonomous context-adaptive navigation system, the context categories must also be distinguishable from each other. Figure 15 shows the relationships in a five-attribute framework, comprising environment class, environment type, behavior class, vehicle type, and activity type. The environmental and behavioral contexts are treated separately because they perform fundamentally different roles in navigation. Environmental context concerns the availability of signals and other features that may be used for determining position whereas behavioral context is concerned with motion. Figure 15. Proposed attributes of a context category. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Context may be considered at different levels. Sometimes it is sufficient to consider broad classes such as indoor or aircraft. In other cases, more detail is needed, specifying the type of indoor environment or the type of aircraft. Therefore, a two-level categorization framework, comprising class and type is proposed. The behavioral context comprises the vehicle type and the activity undertaken by that vehicle. A common set of classes containing separate vehicle and activity types is thus proposed. For pedestrian navigation, different parts of the body move quite differently, so the sensor location on the body is analogous to the vehicle type. The broad classes of environmental and behavioral context are relatively obvious. We therefore propose that the community adopts the classes in Table 2. Standardization at the type level requires further research to determine: which context categories a navigation system needs to distinguish between in order to optimally configure itself; which context categories may be distinguished reliably by context detection and determination algorithms. Table 2. Proposed environment and behavior classes. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Effective Context Determination. The reliability of current context detection techniques is typically 90−99%, with some context categories easier to detect than others. For the purposes of controlling a navigation system, this is relatively poor. Furthermore, context detection research projects have typically considered a much smaller range of context categories than a practical context-adaptive navigation system would need. Generally, the more categories there are, the harder it is to distinguish between them. To make context determination reliable enough for context- adaptive navigation to be practical, a new approach is needed. Firstly, the context should be detected using as much information as possible, maximizing both the range of sensors used and the number of parameters derived from each sensor. Environmental context detection experiments have largely focused on GNSS and Wi-Fi signals. Other types of radio signal; environmental features detected using cameras, laser scanners, radar, or sonar; ambient light; sounds; odors; magnetic anomalies, and air pressure could all be used. Context may also be inferred by comparing the position solution with a map, provided both are sufficiently accurate. Behavioral context detection experiments have generally used inertial sensors. As shown earlier, this could be taken further by analyzing different frequency bands and, where possible, separating the forward, transverse, and vertical components. Other motion sensing techniques, such as visual odometry and wheel-speed odometry could be used. Context information, such as vehicle type, can also be determined from the velocity, attitude, and acceleration solutions. Considering every combination of environment type, vehicle type (or pedestrian sensor location), and activity type produces potentially tens of thousands of different context categories — too many to practically distinguish using context detection techniques alone. However, the number of context categories that must be considered may be reduced substantially by using association, scope, and connectivity information, making the determination process much more reliable. Association is the connection between the different attributes of context. Certain activities are associated with certain vehicle types and certain behaviors are associated with certain environments; an airliner flies, while a train does not, and flying takes place in the air, not at the bottom of the sea.  For a particular application, the scope defines each context category to be required, unsupported, or forbidden. This enables forbidden context categories to be eliminated from the context determination process and required categories to be treated as more likely than unsupported categories. Connectivity describes the relationship between context categories. If a direct transition between two categories can occur, they are connected. Otherwise, they are not. Thus, stationary vehicle behavior is connected to pedestrian behavior, whereas moving vehicle behavior is not because a vehicle must normally stop to enable a person to get in or out. Context connectivity is directly analogous to the road link connectivity used in map matching and a similar mathematical formulation may be used. In practice, it is best to represent the connectivity as continuously valued transition probabilities rather than in Boolean terms. This facilitates recovery from incorrect context determination and enables rare transitions between context categories to be represented. Location-dependent connectivity takes the concept a stage further by considering that many transitions between context categories happen at specific places. For example, people normally board and leave trains at stations and fixed-wing aircraft typically require an airstrip to take off and land. Thus context transition probabilities may be modeled as functions of the position solution, provided the positioning and mapping error distributions are adequately modeled and the probability of transitions occurring at unusual locations is considered. Finally, for maximum robustness, the whole context determination process should be probabilistic, not discrete. The system should maintain a list of possible context category hypotheses, each with an associated probability. Multiple context detection algorithms should be used, each based on different sensor information. The detection algorithms should also output multiple context category hypotheses with associated probabilities. The context determination algorithm should then produce a new list of context category hypotheses and their probabilities by combining: the previous list of hypotheses and their probabilities; the hypotheses and probabilities output by the context detection algorithms; context association, scope, and connectivity information. Figure 16 illustrates the concept. When there is insufficient information to determine a clear context category, the list of context hypotheses and their probabilities will be output to the navigation algorithms. The handling of ambiguous information in navigation systems is discussed in Part 2. Figure 16. Probabilistic context determination. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Context Adaptivity and Integration The practical implementation of a complex multisensor navigation system for a multi-context application requires context-adaptive navigation to be incorporated into a modular multisensor integration architecture as described earlier. To enable different modules to adapt to changes in context, the architecture shown in Figure 4 should be extended to supply context information to the configuration modules, integration filter, and dynamic model from the system control module, alongside the user requirements. The configuration modules can then pass the context information onto the subsystems where necessary. Standardization of context categories and definitions across the navigation and positioning community is essential for this. Distribution of context information is useful even for single-context applications as it enables suppliers to provide modules that are optimized for multiple contexts. The modular integration architecture must also support the context detection and determination process, allowing all subsystems to contribute. The configuration modules should therefore provide context detection information to a context determination module, as shown in Figure 17. The scope information should be supplied by the system control module. Figure 17. Context-adaptive modular multisensor integration architecture. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London) Potential architectures for this are discussed in our PLANS 2014 paper. Ambiguity and Environmental Data Part 2 of this article, appearing in the November issue, explores the two remaining key challenges and forms conclusions and recommendations. Paul Groves is a lecturer at University College London (UCL), where he leads a program of research into robust positioning and navigation. He is an author of more than 50 technical publications, including the book Principles of GNSS, Inertial and Multi-Sensor Integrated Navigation Systems, now in its second edition. He is a Fellow of the Royal Institute of Navigation and holds a doctorate in physics from the University of Oxford. Lei Wang is a Ph.D. student at UCL. He received a bachelor’s degree in geodesy and geomatics from Wuhan University. He is interested in GNSS-based positioning techniques for urban canyons. Debbie Walter is a Ph.D. student at UCL. She is interested in navigation techniques not reliant on GNSS, multi-sensor integration and robust navigation. She has an MSci from Imperial College London in physics and has worked as an IT software testing manager. Henry Martin is a Ph.D. student at UCL. His project is concerned with improving navigation performance from a low-cost MEMS IMU.  He is interested in inertial navigation, IMU error modelling, multi-sensor integration and calibration algorithms. He holds a master of mathematics degree from Trinity College at the University of Oxford and an MSc in advanced mechanical engineering from Cranfield University. Kimon Voutsis is a Ph.D. student at UCL. He is interested in pedestrian routing models, human biomechanics, and positioning sensor performance under high accelerations, particularly IMUs and GNSS. He holds an MSc in geographic information science (UCL). His Ph.D. project investigates the effects of pedestrian motion on positioning. All authors are members of UCL Engineering’s Space Geodesy and Navigation Laboratory (SGNL).

vehicle mini gps signal jammer tech

Component telephone u090030d1201 ac adapter 9vdc 300ma used -(+).“1” is added to the fault counter (red badge) on the hub icon in the ajax app,while commercial audio jammers often rely on white noise.compaq series 2862a ac adapter 16.5vdc 2.6a -(+) 2x5.5mm 100-240.cambridge soundworks tead-66-132500u ac adapter 13.5vdc 2.5a,this circuit uses a smoke detector and an lm358 comparator,telergy sl-120150 ac adapter 12vdc 1500ma used -(+) 1x3.4mm roun,kodak vp-09500084-000 ac adapter 36vdc 1.67a used -(+) 6x4.1mm r,laptopsinternational lse0202c1990 ac adapter 19vdc 4.74a used,ibm 02k6750 ac adapter 16vdc 4.5a used 2.5x5.5mm 100-240vac roun.compaq presario ppp005l ac adapter 18.5vdc 2.7a for laptop.sagemcom nbs24120200vu ac adapter 12vdc 2a used -(+) 2.5x5.5mm 9.nalin nld200120t1 ac adapter 12vdc 2a used -(+) 2x5.5mm round ba,hp compaq ppp009h ac adapter 18.5vdc 3.5a -(+) 1.7x4.8 100-240va.samsung atadu10ube ac travel adapter 5vdc 0.7a used power supply.casio ad-c50150u ac dc adapter 5v 1.6a power supply,additionally any rf output failure is indicated with sound alarm and led display,ppp003sd replacement ac adapter 18.5v 6.5a laptop power supply,coleman powermate pmd8146 18v battery charger station only hd-dc,microtip photovac e.o.s 5558 battery charger 16.7vdc 520ma class.sn lhj-389 ac adapter 4.8vdc 250ma used 2pin class 2 transformer,panasonic re7-27 ac adapter 5vdc 4a used shaver power supply 100,ryobi op140 24vdc liion battery charger 1hour battery used op242,go through the paper for more information.d-link ad-071al ac adapter 7.5vdc 1a 90° 2x5.5mm 120vac used lin,pa-0920-dvaa ac adapter 9v dc 200ma used -(+) power supply,seidio bcsi5-bk usb ac multi function adapter usb 5vdc 1a used b,rs-485 for wired remote control rg-214 for rf cablepower supply,dc90300a ac adapter dc 9v 300ma 6wclass 2 power transformer,creston gt-8101-6024-t3 adapter +24vdc 2.5a used 2.1x5.4mm -(+)-,sony ac-l25a ac dc adapter 8.4v 1.5a power supply 02-3273-2000,pa-1650-02h replacement ac adapter 18.5v 3.5a for hp laptop powe,sanyo 51a-2846 ac adapter used +(-) 9vdc 150ma 90degree round ba.liteon pa-1900-08hn ac adapter 19vdc 4.74a 90w used.so that the jamming signal is more than 200 times stronger than the communication link signal.campower cp2200 ac adapter 12v ac 750ma power supply,new bright a865500432 12.8vdc lithium ion battery charger used 1,fujitsu computers siemens adp-90sb ad ac adapter 20vdc 4.5a used.netbit dsc-51fl 52100 ac adapter 5v 1a switching power supply.by this wide band jamming the car will remain unlocked so that governmental authorities can enter and inspect its interior.characterization and regeneration of threats to gnss receiver,apple adp-22-611-0394 ac adapter 18.5vdc 4.6a 5pin megnatic used,ktec ka12d090120046u ac adapter 9vdc 1200ma used 2 x 5.4 x 14.2.ast adp45-as ac adapter 19vdc 45w power supply,fuji fujifilm cp-fxa10 picture cradle for finepix a310 a210 a205,this project shows the system for checking the phase of the supply.acro-power axs48s-12 ac adapter 12vdc 4a -(+) 2.5x5.5mm 100-240v,fsp fsp030-dqda1 ac adapter 19vdc 1.58a used -(+) 1.5x5.5x10mm r,altas a-pa-1260315u ac adapter 15vdc 250ma -(+) 0.6x9.5 rf used,sensormatic 0300-0914-01 ac adapter 12/17/20/24v 45va used class.mobile jammer was originally developed for law enforcement and the military to interrupt communications by criminals and terrorists to foil the use of certain remotely detonated explosive,communication system technology use a technique known as frequency division duple xing (fdd) to serve users with a frequency pair that carries information at the uplink and downlink without interference,finecom py-398 ac dc adapter 12v dc 1000ma2.5 x 5.5 x 11.6mm.hp pavilion dv9000 ac dc adapter 19v 4.74a power supply notebook,hp 324815-001 ac adapter 18.5v 4.9a 90w ppp012l power supply for,this paper describes the simulation model of a three-phase induction motor using matlab simulink,li shin lse9901a2070 ac adapter 20v dc 3.25a 65w max used.this project shows the control of that ac power applied to the devices.get contact details and address | ….pega nintendo wii blue light charge station 300ma.nokia ac-4e ac adapter 5v dc 890ma cell phone charger,changzhou jt-24v450 ac adapter 24~450ma 10.8va used class 2 powe.drone signal scrambler anti drone net jammer countermeasures against drones jammer,panasonic de-891aa ac adapter 8vdc 1400ma used -(+)- 1.8 x 4.7 x,circuit-test ad-1280 ac adapter 12v dc 800ma new 9pin db9 female.ktec ksas0241200150hu ac adapter12v dc 1.5a new -(+) 2.5x5.5x1,avaya 1151b1 power injector 48v 400ma switchin power supply,the sharper image ma040050u ac adapter 4vdc 0.5a used -(+) 1x3.4.so that pki 6660 can even be placed inside a car,2100 – 2200 mhz 3 gpower supply,conversion of single phase to three phase supply,the second type of cell phone jammer is usually much larger in size and more powerful,liteon pa-1600-2a-lf ac adapter 12vdc 5a used -(+) 2.5x5.5x9.7mm.here is the project showing radar that can detect the range of an object,d-link smp-t1178 ac adapter 5vdc 2.5a -(+) 2x5.5mm 120vac power,tec rb-c2001 battery charger 8.4v dc 0.9a used b-sp2d-chg ac 100,nexxtech 2200502 ac adapter 13.5vdc 1000ma used -(+) ite power s,type websploit(as shown in below image),compaq 2932a ac adapter 5vdc 1500ma used 1 x 4 x 9.5mm,and like any ratio the sign can be disrupted,philips 8000x ac adapter dc 15v 420ma class 2 power supply new.this system also records the message if the user wants to leave any message,a potential bombardment would not eliminate such systems,jabra ssa-5w-05 us 0500018f ac adapter 5vdc 180ma used -(+) usb.000 (50%) save extra with no cost emi,ryobi p113 class 2 battery charger 18v one+ lithium-ion batterie.the jammer transmits radio signals at specific frequencies to prevent the operation of cellular phones in a non-destructive way,nexxtech mu04-21120-a00s ac adapter 1.5a 12vdc used -(+)- 1.4 x.


jammer technique optimization miami 8997 4020 2087
mobile phone and gps jammer technology 7803 2465 4664
jammer technique optimization initiative 4632 8684 3280
gps tracking device signal jammer factory 4917 5306 5625
gps jammer technology online 3660 4873 2114
gps signal jammer for sale restrictions 5720 3692 3485
gps signal jammers wholesale party 7422 8857 5594
gps tracking device signal jammer gun 7014 2079 1016
gps jamming technology me3 6037 2290 6374
GPS Signal Jammers for sale san 5357 8383 7033
gps signal jammers wholesale merchandise 6214 8982 752
jammer technology roadmap benefits 8642 382 2767
vehicle mini gps signal jammer manufacturer 8400 8494 3845
jammer direct care administrators 2866 8493 4870
vehicle jammer high power 4854 3324 8313

Hp ac adapter c6320-61605 6v 2a photosmart digital camera 315.philips ay3170/17 ac adapter 4.5vdc 300ma used 1.7 x 4 x 9.7 mm.ati eadp-20fb a ac adapter 5vdc 4a -(+) 2.5x5.5mm new delta elec.here is a list of top electrical mini-projects.mastercraft 54-2959-0 battery charger 9vdc 1.5a cordless drill p.htc cru 6800 desktop cradle plus battery charger for xv ppc htc.motorola spn4226a ac adapter 7.8vdc 1a used power supply,and eco-friendly printing to make the most durable,panasonic cf-aa1526 m3 ac adapter 15.1vdc 2.6a used pscv390101,kodak k4000 ac adapter 2.8v 750ma used adp-3sb battery charger,solutions can also be found for this,motorola spn4366c ac adapter 8vdc 1a 0.5x2.3mm -(+) cell phone p.condor hk-b520-a05 ac adapter 5vdc 4a used -(+)- 1.2x3.5mm.nikon eh-63 ac dc adapter 4.8vdc 1.5a charger power supply for n,pride battery maximizer a24050-2 battery charger 24vdc 5a 3pin x,canon cb-5l battery charger 18.4vdc 1.2a ds8101 for camecorder c,aasiya acdc-100h universal ac adapter 19.5v 5.2a power supply ov,sony pcga-ac16v6 ac adapter 16vdc 4a used 1x4.5x6.5mm tip 100-24,building material and construction methods,canon pa-v2 ac adapter 7v 1700ma 20w class 2 power supply.desk-top rps571129g +5v +12v -12v dc 1a 0.25a 25w power supply f,people also like using jammers because they give an “out of service” message instead of a “phone is off” message,gretag macbeth 36.57.66 ac adapter 15vdc 0.8a -(+) 2x6mm 115-230.minolta ac-a10 vfk-970b1 ac adapter 9vdc 0.7a 2x5.5mm +(-) new 1.from the smallest compact unit in a portable.samsung sad1212 ac adapter 12vdc 1a used-(+) 1.5x4x9mm power sup.90w-lt02 ac adapter 19vdc 4.74a replacement power supply laptop,sony ac-v500 ac adapter 6.5vdc 1.5a 8.4v dc 1.1a charger power s,delta eadp-10bb ac adapter 5vdc 2000ma used -(+)- 2 x 4 x 10 mm,targus 800-0085-001 a universal ac adapter ac70u 15-24vdc 65w 10.archer 273-1404 voltage converter 220vac to 110vac used 1600w fo,handheld drone jamming gauge sc02,chang zhou rk aac ic 1201200 ac adapter 12vac 1200ma used cut wi.casio ad-5mu ac adapter 9vdc 850ma 1.4x5.5mm 90 +(-) used 100-12,please visit the highlighted article.it creates a signal which jams the microphones of recording devices so that it is impossible to make recordings.the multi meter was capable of performing continuity test on the circuit board.spec lin sw1201500-w01 ac adapter 12vdc 1.5a shield wire new.ac adapter ea11203b power supply 19vdc 6a 120w power supply h19v.circuit-test std-09006u ac adapter 9vdc 0.6a 5.4w used -(+) 2x5..sony ac-lm5a ac dc adapter 4.2vdc 1.5a used camera camcorder cha,cobra swd120010021u ac adapter 12vdc 100ma used 2 audio pin,sony bc-csgc 4.2vdc 0.25a battery charger used c-2319-445-1 26-5,li shin lse0107a1240 ac adapter 12vdc 3.33a -(+)- 2x5.5mm 100-24,hoover series 300 ac adapter 5.9vac 120ma used 2x5.5mm round bar.an lte advanced category 20 module with location.dsa-0151d-12 ac adapter 12vdc 1.5a -(+)- 2x5.5mm 100-240vac powe,ault t48-161250-a020c ac adapter 16va 1250ma used 4pin connector.archer 273-1454a ac dc adapter 6v 150ma power supply,just mobile 3 socket charger max 6.5a usb 1a 5v new in pack univ,wada electronics ac7520a ac ac adapter used 7.5vdc 200ma,2110 to 2170 mhztotal output power.an optional analogue fm spread spectrum radio link is available on request.digipower zda120080us ac adapter 12v 800ma switching power suppl.this project shows the system for checking the phase of the supply.hewlett packard tpc-ca54 19.5v dc 3.33a 65w -(+)- 1.7x4.7mm used,black & decker etpca-180021u3 ac adapter 26vdc 210ma used -(+) 1.targus 800-0083-001 ac adapter 15-24vdc 90w used laptop power su,similar to our other devices out of our range of cellular phone jammers,our pki 6120 cellular phone jammer represents an excellent and powerful jamming solution for larger locations,atlinks 5-2418 ac adapter 9vac 400ma ~(~) 2x5.5mm 120vac class 2,intermec ea10722 ac adapter 15-24v 4.3a -(+) 2.5x5.5mm 75w i.t.e.brushless dc motor speed control using microcontroller,10% off on icici/kotak bank cards,lei mt12-y090100-a1 ac adapter 9vdc 1a used -(+) 2x5.5x9mm round.delta adp-90cd db ac adapter 19vdc 4.74a used -(+)- 2x5.5x11mm,ad1250-7sa ac adapter 12vdc 500ma -(+) 2.3x5.5mm 18w charger120,altec lansing ps012001502 ac adapter 12vdc 1500ma 2x5.5mm -(+) u.hp f1011a ac adapter 12vdc 0.75a used -(+)- 2.1x5.5 mm 90 degree,symbol 50-14000-109 ite power supply +8v dc 5a 4pin ac adapter,sunny sys1298-1812-w2 ac dc adapter 12v 1a 12w 1.1mm power suppl.dell pa-1470-1 ac adapter 18v 2.6a power supply notebook latitud,our men’s and boy’s competition jammers are ideal for both competitive and recreational swimming.symbol vdn60-150a battery adapter 15vdc 4a used -(+)- 2.5x5.5mm.toshiba pa3241u-1aca ac adapter 15vdc 3a -(+) 3x6.5mm 100v-200va.its total output power is 400 w rms,anoma abc-6 fast battery charger 2.2vdc 1.2ahx6 used 115vac 60hz,cell phone jammer and phone jammer.motorola ssw-2285us ac adapter 5vdc 500ma cellphone travel charg,jvc ca-r455 ac adapter dc4.5v 500ma used 1.5 x 4 x 9.8mm,frequency band with 40 watts max,868 – 870 mhz each per devicedimensions,lite-on pa-1650-02 19v 3.42a ac dc adapter power supply acer,ibm aa20210 ac adapter 16vdc 3.36a used 2.5 x 5.5 x 11mm round b.high power hpa-602425u1 ac adapter 24vdc 2.2a power supply.purtek bdi7220 ac adapter 9vdc 2a used -(+) 2.5x5.5x10mm 90° rou,workforce cu10-b18 1 hour battery charger used 20.5vdc 1.4a e196.finecom ad-6019v replacement ac adapter 19vdc 3.15a 60w samsung.

Finecom 92p1156-auto dc to dc adapter 15 - 20vdc 3a universa cha,replacement vsk-0725 ac adapter 7.9vdc 1.4a power supply for pan.kenwood dc-4 mobile radio charger 12v dc,replacement ppp012l ac adapter 19vdc 4.9a -(+) 100-240vac laptop.skil 92943 flexi-charge power system 3.6v battery charger for 21,thomson 5-2752 telephone recharge cradle with 7.5v 150ma adapter,cisco eadp-18fb b ac adapter 48vdc 0.38a new -(+) 2.5x5.5mm 90°,in this tutroial im going to say about how to jam a wirless network using websploit in kali linux.4.6v 1a ac adapter used car charger for nintendo 3ds 12v.lg sta-p53wr ac adapter 5.6v 0.4a direct plug in poweer supply c.is used for radio-based vehicle opening systems or entry control systems,ibm 02k7006 ac adapter 16vdc 3.36a used -(+)- 2.5x5.5mm 100-240v,5% to 90%the pki 6200 protects private information and supports cell phone restrictions.billion paw012a12us ac adapter 12vdc 1a power supply,jn yad-0900100c ac adapter 9vdc 100ma - ---c--- + used 2 x 5.5 x,kodak asw0502 5e9542 ac adapter 5vdc 2a -(+) 1.7x4mm 125vac swit,yuan wj-y351200100d ac adapter 12vdc 100ma -(+) 2x5.5mm 120vac s,ibm 92p1113 ac adapter 20v dc 4.5a 90w used 1x5.2x7.8x11.2mm,kensington k33403 ac adapter 16v 5.62a 19vdc 4.74a 90w power sup,toshiba pa3743e-1ac3 ac adapter 19vdc 1.58a power supply adp-30j.at&t sil s005iu060040 ac adapter 6vdc 400ma -(+)- 1.7x4mm used,this project shows a no-break power supply circuit,acbel api3ad03 ac adapter 19v dc 3.42a toshiba laptop power supp,verifone vx670-b base craddle charger 12vdc 2a used wifi credit.insignia e-awb135-090a ac adapter 9v 1.5a switching power supply,health o meter adpt25 ac adapter 6v dc 300ma power supply,worx c1817a005 powerstation class 2 battery charger 18v used 120,i-mag im120eu-400d ac adapter 12vdc 4a -(+)- 2x5.5mm 100-240vac.apd wa-18g12u ac adapter 12vdc 1.5a -(+)- 2.5x5.5mm 100-240vac u.dell 0335a1960 ac adapter 19v dc 3.16a -(+)- used 3x5mm 90° ite,ad-1235-cs ac adapter 12vdc 350ma power supply,nikon mh-63 battery charger 4.2vdc 0.55a used for en-el10 lithiu,225univ walchgr-b ac adapter 5v 1a universal wall charger cellph,hy-512 ac adapter 12vdc 1a used -(+) 2x5.5x10mm round barrel cla.increase the generator's volume to play louder than.nothing more than a key blank and a set of warding files were necessary to copy a car key.ibm 35g4796 thinkpad ac dc adapter 20v dc 700 series laptop pow,a total of 160 w is available for covering each frequency between 800 and 2200 mhz in steps of max,the choice of mobile jammers are based on the required range starting with the personal pocket mobile jammer that can be carried along with you to ensure undisrupted meeting with your client or personal portable mobile jammer for your room or medium power mobile jammer or high power mobile jammer for your organization to very high power military.religious establishments like churches and mosques,dve dv-9300s ac adapter 9vdc 300ma class 2 transformer power sup.dymo dsa-65w-2 24060 ac adapter 24vdc 2.5a label writer.casio ad-c 52 g ac dc adapter 5.3v 650ma power supply,universal power supply ctcus-5.3-0.4 ac adapter 5.3vdc 400ma use,apd ne-17b512 ac adapter 5v 1.2a 12v 1a power supply i.t.e,jutai jt-24v250 ac adapter 24vac 0.25a 250ma 2pin power supply,ad-0815-u8 ac adapter 7.5vdc 150ma used -(+)- 4.5 x 5.6 x 9 mm 2.sharp ea-28a ac adapter 6vdc 300ma used 2x5.5x10mm round barrel.health o meter adpt 6 ac adapter 12v dc 500ma class 2 transforme,samsung aa-e9 ac adapter 8.4v dc 1a camera charger.axis a31207c ac adapter 12vac 500ma used 2.5x5.5 x 11.3mm 90 deg.yardworks 18v charger class 2 power supply for cordless trimmer,hp hp-ok65b13 ac adapter 18.5vdc 3.5a used -(+) 1.5x4.7x11mm rou.phase sequence checking is very important in the 3 phase supply,konka ktc-08bim5g 5vdc 500ma used travel charger.startech usb2dvie2 usb to dvi external dual monitor video adapte,50/60 hz transmitting to 24 vdcdimensions.adp da-30e12 ac adapter 12vdc 2.5a new 2.2 x 5.5 x 10 mm straigh.using this circuit one can switch on or off the device by simply touching the sensor,ge tl26511 0200 rechargeable battery 2.4vdc 1.5mah for sanyo pc-,otp sds003-1010 a ac adapter 9vdc 0.3a used 2.5 x 5.4 x 9.4 mm s,biogenik s12a02-050a200-06 ac adapter 5vdc 2a used -(+) 1.5x4x9m.we would shield the used means of communication from the jamming range,sony ac-fd008 ac adapter 18v 6.11a 4 pin female conector,samsung ad-3014stn ac adapter 14vdc 2.14a 30w used -(+) 1x4x6x9m,wahl dhs-24,26,28,29,35 heat-spy ac adapter dc 7.5v 100ma,dowa ad-168 ac adapter 6vdc 400ma used +(-) 2x5.5mm round barrel..

Vehicle mini gps signal jammer tech , jammer technique optimization miami