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By Hans-Georg Büsing, Ulrich Haak, and Peter Hecker Future safety-relevant driver assistant systems demand vehicle state estimations accurate enough to match the position within a road lane, which cannot be provided by standalone GPS. A promising approach to meet the requirements is the fusion of standalone or differential GNSS measurements with vehicle sensor data like odometers or accelerometers. To achieve deeper sensor integration, a software GNSS receiver was developed at the Institute of Flight Guidance (IFF) that is able to use dead reckoning sensors to support its signal acquisition. This article presents an approach to estimate the signal states during outages based on the tightly coupled vehicle state, which reduces the reacquisition time and significantly increases the signal availability. GNSS-based navigation is a key enabler for future advanced driver assistance systems (ADAS). Car manufacturers have identified automotive assistance systems as core devices to propose their uniqueness mainly in the luxury and upper-class market segments. While the precision and availability of loosely coupled single-frequency GPS navigation satisfies the requirements of typical route guidance systems, future automotive systems — especially those that enhance driving safety — are more demanding on positioning system performance. The Institute of Flight Guidance (IFF) of the Technische Universität, Braunschweig, Germany, is involved in two research projects evaluating the performance of unaided traditional GNSS receivers coupled with vehicle sensor measurements such as odometers in a tightly coupled architecture. Besides these involvements, the IFF has developed a general-purpose software-based GNSS receiver allowing full access to signal processing routines. The benefits of the tight sensor fusion are reliable state estimations even during total signal outages that are common in the automotive sector due to tunnels, parking decks, or urban canyons. In this architecture, the GNSS receiver works autonomously to deliver raw GNSS-measurements only. Additional knowledge provided by the vehicle sensors cannot be used to support the receiver in any way. Besides other beneficial aspects in the tracking channels, additional external knowledge about the vehicle state has the potential to reduce acquisition times and improve the measurement availability significantly. The Institute of Flight Guidance uses a software environment called “Automotive Data and Time-Triggered Framework” (ADTF) for research in the field of ADAS and automotive navigation. In this software framework, the overall system architecture is assembled with independent modules. These modules are implemented as libraries and loaded into ADTF. Data is exchanged via pins that are defined as public variables. The framework also attaches timestamps to the individual measurements and adds a data recording and playback functionality. From a general-purpose software GNSS receiver, presented at the ION GNSS 2010, we have derived an automotive-specific ADTF software receiver module. The software framework adds the flexibility to synchronously process measurements from vehicle sensors additionally to the IF data from the front end. This gives us the opportunity to aid signal processing in the software GNSS receiver with additional external sensors. For positioning, a tightly coupled positioning filter based on GPS raw data measurements and the rear-wheel odometers is implemented. The vehicle’s motion is modeled using a kinematic relationship between the vehicle sensors and the GNSS measurements. Based on the tightly coupled vehicle state estimation, an acquisition state is processed during signal outages that enables the software GNSS receiver to reacquire the satellite signal instantaneously with high precision. In this article, the constituent parts of the system are presented and the estimation of the acquisition state derived. The system was tested in an urban scenario, and the state estimations validated with the recorded measurements. System Architecture The software-defined GNSS receiver developed by the IFF was designed to process the computationally expensive signal correlation on an Nvidia graphics board using the vast parallel processing capability of graphics processing units (GPUs). With the use of common graphics boards, an entire receiver can be implemented on an ordinary PC, needing only a front-end to receive digital GNSS signals in an intermediate frequency (IF) band. For research in the field of vehicle state estimation, a derivate of the software receiver of the Institute of Flight Guidance has been implemented in the “Automotive Data and Time-Triggered Framework” (ADTF). The software is commonly used in the automotive industry for the development of ADAS. Figure 1 shows a typical system layout in ADTF. A central component of the framework is the ability to record and play back measurement data, which is indicated by the buttons on the left of the screenshot. Figure 1. System Architecture in ADTF. (Click to enlarge.) Within ADTF, the systems are assembled from modules that are shown as blocks within the graphical configuration editor. Standard modules such as the connection of common hardware are provided with the framework. Custom modules can be implemented in C++ by the user. Every module is implemented as a dynamic library (DLL) and interpreted by the framework. Modules can be featured with input and output pins. These pins are implemented by using specific data types from the framework. The communication and data exchange between the modules is handled via these pins. They can be connected by graphically drawing connector lines in the configuration editor. ADTF provides the user with classes for timing and threading. Processes can thereby be linked to the ADTF system time, which is especially important as the data replay can be slowed down or sped up for debugging. The instantaneous reacquisition algorithm is based on a traditional approach of tightly coupling GNSS raw data with vehicle sensor measurements. The fusion is based on a kinematic model following the Ackermann geometry establishing the relationship between the vehicle’s motion and the respective measurements. At each time step of an arriving measurement, the vehicle’s motion is predicted based on the last estimated state with an extended Kalman filter. The prediction is then corrected using either measurements from the vehicle sensors or GNSS raw measurements. The range and Doppler measurements are calculated in the tracking channels of the ADTF software GNSS receiver. The corrected vehicle state is then fed back into the kinematic model for the next update cycle. In case the GNSS signal is lost in a tracking channel, a virtual tracking channel is initialized with the last calculated channel states. The change in the channel output is then predicted utilizing the change in the vehicle state and the current evaluation of the ephemeris. The schematic implementation of the channel state prediction is shown in Figure 2. Figure 2. Schematic of Channel State Prediction. (Click to enlarge.) Signal State Estimation Using the tightly coupled architecture presented above, an estimated position and velocity can even be provided during total signal outages. Assuming that the last valid observation of a satellite signal is stored together with its respective time to and position, an estimation of the signal state (that is, Doppler frequency, code- and carrier-phase) based on the estimation of the vehicle state during the signal outage at time t1 can be used for an instantaneous signal reacquisition. Using the ephemeris data provided by the respective GPS satellite the range between a user position xu and the satellite xsv can be calculated using the following terms     (1) and (2) with |…| indicating the Euclidian distance. Therefore the change of the range can be obtained with equations (1) and (2): (3) Assuming an unbiased Gaussian error distribution of the measurements, the tightly coupled system provides an estimation of the covariance matrix of the vehicle state. Using only the submatrix (4) related to the vehicle position, the covariance of the user position along the line-of-sight to the satellite can be obtained with the Euclidean norm of the line-of-sight vector (5) and the law of error propagation: (6) Furthermore, using the law of error propagation, it can be shown that the variance of the change of range estimation in equation (3) is obtained by:    (7) With the last valid range measurement related to time to, the signal state at time t1 can be obtained for the pseudo-range PSR    (8) and the carrier phase Φ:     (9) The resulting variance of these estimations can by expressed by    (10) and    (11) respectively. The estimate of the Doppler and the related variance can be obtained analogous. Considering the variances of the estimation, it can be decided if the signal can be reacquired instantaneously or if the receiver has to find the signal using standard acquisition routines in a limited search space. Experimental Validation The Volkswagen Passat station wagon operated by the Institute of Flight Guidance was used to evaluate the performance of the proposed algorithm (see PHOTO.) The test vehicle is customized from the standard by adding an additional generator to meet the power requirements of the measurement and processing hardware. In addition, the Controller Area Network (CAN) is mirrored and open to access the data collected by the sensors of the vehicle. The relevant sensors include a longitudinal accelerometer, a gyro for measuring the yaw rate as well as the odometers of all four wheels. The test vehicle is equipped with a GNSS front-end developed by the Fraunhofer Institute for Integrated Circuits. It is capable of streaming L1, L2, and L5 RF samples via two USB ports. The sampling rate of L1 is 40.96 MHz at an intermediate frequency of 12.82 MHz. Test Vehicle. A customized Volkswagen Passat was used to evaluate performance of the algorithm. The vehicle sensor data is streamed via CAN to an automotive PC from Spectra. It is equipped with an Intel quadcore CPU, 8 GB RAM, a Vector PCI CAN device and 256 GB SATA solid state disk allowing up to 195 MB/s writing speed. Additionally, it has been equipped with an Nvidia GeForce GT 440 graphics board that is used for processing the GNSS RF data. This specific graphics board was chosen because it offers a comparably high performance of the GPU at relatively low power consumption. Both GNSS RF data and data from the vehicle sensor network are streamed to an ADTF hard disk recorder. Due to the setup of the data acquisition, several challenges have to be solved. The first challenge is that the front-end needs to be used as hardware-in-the-loop. It is by itself not equipped with an automated gain control. Therefore, it is not possible to just stream the RF data but it has to be decoded, processed for adjusting the gain, and then stored to the hard drive. Secondly, the recording setup needs to cover high data rates. The GNSS front-end streams approximately 20 MB/s. As the data needs to be decoded and processed for gain control, the expanded data rate for recording is ~40 MB/s. In total including vehicle sensor measurements, >2000 data packets per second are streamed to the recorder. Because this could not be done using mechanical hard drives, we used solid state disks that also allow data storage during times of high vibration. Related to the before-mentioned challenges, an efficient thread management needed to be implemented. The software framework’s threading classes are utilized to parallelize the receiver processes. Additionally, it has arisen that a significant part of the processing time is taken by the data transfer to the memory of the GPU. In order to prove the advantages of an odometer-aided reacquisition, an applicable testing scenario was chosen. To distinguish an odometer-based aquisition approach from a model-based approach, a trajectory was chosen that features a right turn of 90 degrees immediately after cutting off the GNSS signal. A model-based kinematic prediction would project the trajectory in the direction of the latest known heading derived by the GNSS solution. Only a sensor-based state estimation is able to resolve the right turn. The driven trajectory is shown in Figure 3. The GNSS signal has been cut off for approximately 10 seconds, which is equivalent of a 75-meter drive on dead reckoning sensors only after the right turn. Figure 3. Trajectory of test drive includes a 90-degree turn. (Click to enlarge.) Results The following plots in Figure 4 show the performance of the virtual tracking channels. The plots in the upper row show the pseudorange output over time. For vividness they have been corrected for the motion of the respective satellite that is dominant due to their high speeds. Over a short period of time the satellites’ motion relative to the receiver can be linearly approximated. The pseudorange measurements over time were fit using a linear regression. The respective value of the linear regression was then subtracted from the pseudorange and plot over time as shown in the figures in the second row, leaving only the approximated influence of the vehicle’s motion. Figure 4. Modified pseudorange and Doppler results of the virtual tracking channels. (Click to enlarge.) The Doppler measurements have been similarly compensated by just subtracting the minimum measurement. These modifications of the pseudorange and Doppler measurements allow a direct comparison of each other as the Doppler can be understood as the first derivate of the pseudorange over time. The results of PRN 6 show that the Doppler estimate during the GPS outage smoothly fits into the surrounding measurements without any major outliers. The plot of the pseudorange shows a similar behavior. The pseudorange could have potentially been modeled using a dynamic prediction that is not based on vehicle sensors due to the limited dynamics on the pseudorange measurements. The Doppler plot of PRN 16 shows a strong change in the relative velocity between satellite and receiver. If a further projection of the Doppler using a linear dynamic model would have been used instead of predicting with vehicle sensors, it would likely have misled the reacquisition by ~ 50 Hz. The trend in the pseudorange measurements is comparable to PRN 6 at a higher rate of change. The plots of PRN 21 probably show the advantages of using vehicle sensors for reacquisition best as the dynamics on pseudorange and Doppler are the most significant in the group. Both pseudorange and Doppler show a turning point during the GNSS outage. Especially, the pseudorange would have been mismodeled using a kinematic predicion that is not relying on additional sensors. Conclusion In this article, a tightly coupled positioning system implemented in the automotive-specific framework ADTF was presented that is based on the fusion of standard automotive sensor data and software receiver measurements. We showed that, using the tightly coupled solution, an acquisition state during signal outages can be estimated that allows the tracking channels to reacquire the signal instantaneously without the need of computationally expensive acquisition routines. Under the assumption of a tightly coupled RTK position and small outage times, a reacquisition of the carrier phase without loosing the information about the phase ambiguity seems possible. In the next version of the automotive GNSS receiver, the authors are planning to integrate the vehicle sensors to aid the tracking loops, which is likely to further improve tracking continuity especially in scenarios with high vegetation. Additionally, we plan to show that the implementation is capable of working in real time. Improvements of the initialization of the virtual tracking loops are also intended. Acknowledgments This article is based on a paper presented at ION-GNSS 2011, held September 19–23 in Portland, Oregon. This work was funded by the Federal State of Lower Saxony, Germany. Project: Galileo – Laboratory for the research airport Braunschweig. The authors would like to thank their colleagues working in the automotive navigation group for continuous support with the ADTF framework. Hans-Georg Büsing holds a Dipl.-Ing. in aerospace engineering from the Technische Universität Braunschweig and has been a research engineer at IFF since 2008. He works in the area of applied satellite navigation, especially in the field of vehicle positioning. Ulrich Haak holds a Dipl.-Ing. in mechanical engineering from the Technische Universität Braunschweig and joined IFF in 2008 as a research engineer. He works in the areas of receiver design and positioning algorithms. Peter Hecker joined IFF in 1989 as research scientist. Initial focus of his scientific work was in the field of automated situation assessment for flight guidance. From 2000 until 2005, he was head of the DLR Pilot Assistance department. Since April 2005, he has been director of IFF. He is managing research activities in the areas of air/ground cooperative air traffic management, airborne measurement technologies and services, satellite navigation, human factors in aviation, and safety in air transport systems.

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Hitachi hmx45adpt ac adapter 19v dc 45w used 2.2 x 5.4 x 12.3 mm.liteon pa-1151-08 ac adapter 19v 7.9a used 3.3 x 5.5 x 12.9mm,li shin 0226b19150 ac adapter 19vdc 7.89a -(+) 2.5x5.5mm 100-240,if you are looking for mini project ideas.hp ppp009s ac adapter 18.5v dc 3.5a 65w -(+)- 1.7x4.7mm 100-240v,eng 3a-302da18 ac adapter 20vdc 1.5a new 2.5x5.5mm -(+) 100-240v,arstan dv-9750 ac adapter 9.5vac 750ma wallmount direct plug in.xings ku1b-038-0080d ac adapter 3.8vdc 80ma used shaverpower s,epson a391uc ac adapter 13.5vdc 1.5a used -(+) 3.3x5mm 90° right,ar 48-15-800 ac dc adapter 15v 800ma 19w class 2 transformer.astec sa35-3146 ac adapter 20vdc 1.75a power supply,radar detectors are passive and the laser gun can record your speed in less than ½,sony vgp-ac19v39 ac adapter 19.5v 2a used 4.5 x 6 x 9.5 mm 90 de,car charger power adapter used 1.5x4mm portable dvd player power.by this wide band jamming the car will remain unlocked so that governmental authorities can enter and inspect its interior,cell towers divide a city into small areas or cells.bellsouth u090050a ac adapter 9vac 500ma power supply class 2,therefore it is an essential tool for every related government department and should not be missing in any of such services.delta adp-15hb rev b ac adapter 12v 1.25a used 3 x 5.5 x 11mm.d-link mu05-p050100-a1 ac adapter 5vdc 1a used -(+) 90° 2x5.5mm,due to the high total output power,5% to 90%modeling of the three-phase induction motor using simulink,li shin 0226a19150 ac adapter 19vdc 7.89a -(+) 2.5x5.5mm 100-240,the inputs given to this are the power source and load torque,replacement a1012 ac adapter 24v 2.65a g4 for apple ibook powerb,fujitsu cp293662-01 ac adapter 19vdc 4.22a used 2.5 x 5.5 x 12mm.finecom i-mag 120eu-400d-1 ac adapter 12vdc 4a -(+) 1.7x4.8mm 10,cobra du28090020c ac adapter 9vdc 200ma -(+) 2x5.5mm 4.4w 120vac,sunforce 11-1894-0 solar battery charger 12v 1 watt motorcycle.palm plm05a-050 dock for palm pda m130, m500, m505, m515 and mor.dell ad-4214n ac adapter 14vdc 3a power supply,plantronics ssa-5w-05 0us 050018f ac adapter 5vdc 180ma used usb.hp compaq 384020-001 ac dc adapter 19v 4.74a laptop power supply.caere 099-0005-002 ac adapter 7.5dc 677ma power supply,remember that there are three main important circuits.corex 48-7.5-1200d ac adapter 7.5v dc 1200ma power supply,griffin p2275 charger 5vdc 2.1a from 12vdc new dual usb car adap,ault p41120400a010g ac adapter 12v dc 400ma used 2.5 x 5.4 9.6mm.makita dc1410 used class 2 high capacity battery charger 24-9.6v,rocketfish rf-lg90 ac adapter5v dc 0.6a used usb connector swi,this out-band jamming signals are mainly caused due to nearby wireless transmitters of the other sytems such as gsm,lucent technologies ks-22911 l1/l2 ac adapter dc 48v 200ma,rocketfish blc060501100wu ac adapter 5vdc 1100ma used -(+) 1x3.5,2 w output powerwifi 2400 – 2485 mhz.aps aps40-es-30 ac adapter +5v 6a +12v 1a -12v 0.5a used 5pin.cell phones within this range simply show no signal.

Ktec ka12d240020034u ac adapter 24vdc 200ma used -(+) 2x5.5x14mm,it employs a closed-loop control technique.ryobi 140237023 18.0v 19vdc 2.2a 1423701 cordless drill battery.fujitsu adp-80nb a ac adapter 19vdc 4.22a used -(+) 2.5x5.5mm c,city of meadow lake regular council meeting december 12.cell phones are basically handled two way ratios,apple m4551 studio display 24v dc 1.875a 45w used power supply.conair tk953rc dual voltage converter used 110-120vac 50hz 220v,spectra-physics ault sw 306 ac adapter 5v 1a 12v scanning system,finecom ah-v420u ac adapter 12v 2.5a power supply,band selection and low battery warning led,cell phone scanner jammer presentation.sil vd090030d ac adapter 9vdc 300ma power supply transformer.biogenik 3ds/dsi ac adapter used 4.6v 1a car charger for nintend,proxim 481210003co ac adapter 12vdc 1a -(+) 2x5.5mm 90° 120vac w,flextronics a 1300 charger 5vdc 1a used -(+) 100-240v~50/60hz 0.,gateway liteon pa-1121-08 ac adapter 19vdc 6.3a used -(+) 2.5x5.,toshiba pa8727u 18vdc 1.7a 2.2a ac adapter laptop power supply,cellet tcnok6101x ac adapter 4.5-9.5v 0.8a max used,lp-60w universal adapter power supply toshiba laptop europe.lishin lse9802a1660 ac adapter 16vdc 3.75a -(+)- used 2.5x5.5x12,hp 0950-2852 class 2 battery charger nicd nimh usa canada.ads-1210pc ac adapter 12vdc 1a switching power supply 100 - 240v,dsa-0151d-12 ac adapter 12vdc 1.5a -(+)- 2x5.5mm 100-240vac powe.ua075020e ac adapter 7.5vac 200ma used 1.4 x 3.3 x 8 mm 90,dechang long-0910b ac dc adapter 9v dc 1a 2 x 5.5 x 10.2mm used,hp ppp017h ac adapter 18.5vdc 6.5a 120w used -(+) 2.5x5.5mm stra.canon k30287 ac adapter 16vdc 2a used 1 x 4.5 x 6 x 9.6 mm,delta adp-63bb b ac adapter 15v 4.2a laptop power supply,ault inc 7712-305-409e ac adapter 5vdc 0.6a +12v 0.2a 5pin power,nokia ac-4e ac adapter 5v dc 890ma cell phone charger,automatic telephone answering machine.panasonic eyo225 universal battery charger used 2.4v 3.6v 5a.sony bc-7f ni-cd battery charger,analog vision puae602 ac adapter 5v 12vdc 2a 5pin 9mm mini din p,we will strive to provide your with quality product and the lowest price,condor hka-09100ec-230 ac adapter 9vdc 1000ma 9va used 2.4x5.5mm,we don't know when or if this item will be back in stock.li shin international enterprise 0322b1224 ac adapter 12vdc 2a u,the paralysis radius varies between 2 meters minimum to 30 meters in case of weak base station signals,ibm 12j1447 ac adapter 16v dc 2.2a power supply 4pin for thinkpa,ault pw15ae0600b03 ac adapter 5.9vdc 2000ma used 1.2x3.3mm power,netbit dsc-51f-52p us ac adapter 5.2v 1a switching power supply,motorola psm4716a ac power supply dc 4.4v 1.5a phone charger spn,1) the vehicle/trailer being towed (at homeowner expense).delta adp-25hb ac adapter 30v 0.83a power supply.

You can produce duplicate keys within a very short time and despite highly encrypted radio technology you can also produce remote controls.dell pa-16 /pa16 ac adapter19v dc 3.16a 60watts desktop power,makita dc9800 fast charger 7.2v dc9.6v 1.5a used 115~ 35w,high voltage generation by using cockcroft-walton multiplier,these jammers include the intelligent jammers which directly communicate with the gsm provider to block the services to the clients in the restricted areas.toshiba pa3546e-1ac3 ac adapter 19vdc 9.5a satellite laptop.dve dsa-0301-05 ac adapter 5vdc 4a 4pin rectangle connector swit,tdp ep-119/ktc-339 ac adapter 12vac 0.93amp used 2.5x5.5x9mm rou.mobile phone/cell phone jammer circuit,motorola htn9000c class 2 radio battery charger used -(+) 18vdc,fairway ve20-120 ac adapter 12vdc 1.66a used 1.7x4mm straight ro,olympus ps-bcm2 bcm-2 li-on battery charger used 8.35vdc 400ma 1.altec lansing 4815090r3ct ac adapter 15vdc 900ma -(+) 2x5.5mm 12.nikon eh-64 ac adapter 4.8vdc 1.5a -(+) power supply for coolpix,plantronics 7501sd-5018a-ul ac adapter 5vdc 180ma used 1x3x3.2mm,rocketfish rf-sam90 charger ac adapter 5vdc 0.6a power supply us,here is the project showing radar that can detect the range of an object.anoma aec-n3512i ac adapter 12vdc 300ma used 2x5.5x11mm -(+)-.it should be noted that operating or even owing a cell phone jammer is illegal in most municipalities and specifically so in the united states,samsung tad137vse ac adapter 5v 0.7a used special flat connector,samsung aa-e9 ac adapter 8.4v dc 1a camera charger.sony on-001ac ac adapter 8.4vdc 400ma used power supply charger,astec sa25-3109 ac adapter 24vdc 1a 24w used -(+) 2.5x5.5x10mm r.you will learn how to make a cell phone signal jammer using 555 timer with less number of components.altec lansing eudf+15050-2600 ac adapter 5vdc 2.6a -(+) used 2x5,a low-cost sewerage monitoring system that can detect blockages in the sewers is proposed in this paper,gameshark 8712 ac dc adapter 5v 2a power supply,dpd-120500b ac adapter 12vdc 500ma power supply,our pharmacy app lets you refill prescriptions,motorola dch3-05us-0300 travel charger 5vdc 550ma used supply,emp jw-75601-n ac adapter 7.5vc 600ma used +(-) 2x5.5mm 120vac 2,a1036 ac adapter 24vdc 1.875a 45w apple g4 ibook like new replac,we hope this list of electrical mini project ideas is more helpful for many engineering students.15.2326 ac adapter 12vdc 1000ma -(+) used 2.4 x 5.5 x 8.3.5mm,yardworks cs24 battery charger cc 24vdc usednca 120v~60hz ac,positec machinery sh-dc0240400 ac adapter 24vdc 400ma used -(,the pki 6160 is the most powerful version of our range of cellular phone breakers,nintendo ds dsi car adapter 12vdc 4.6vdc 900ma used charger bric,.

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