Easy-hardware-implementation MMPF for Maneuvering Target Tracking: Algorithm and Architecture

Hong, Shaohua; Shi, Zhiguo; Chen, Kangsheng

doi:10.1007/s11265-010-0450-4

Cited by 10 publications

(11 citation statements)

References 19 publications

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“…Figure 3 shows the IMH sampler architecture. When computing the acceptance probability, we use the modified method in [26] to avoid division computation. In particular, in our case, we accept particles following the procedure…”

Section: Processing Element Architecturementioning

confidence: 99%

Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing

Liu

Zhang

Chakrabarti

et al. 2011

J Sign Process Syst

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Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-Gaussian dynamic system parameters. As these algorithms are recursive, their real-time implementation can be computationally complex. In this paper, we analyze the bottlenecks in existing parallel PF algorithms, and propose a new approach that integrates parallel PFs with independent Metropolis-Hastings (PPF-IMH) resampling algorithms to improve root mean-squared estiThe parallel particle filter implementation was discussed in our 2010 IEEE Workshop on Signal Processing Systems paper [1]. This work also presents: the new algorithm and hardware implementation described in more detail (Section 3); the effect of the number of processing elements and number of groups in each processing element on the parallel particle filter algorithm performance (Sections 5.2 and 6.1); the waveform-agile sensing algorithm (Section 4.1), the waveform-agile tracking application (Section 4.2), and its hardware (FPGA) implementation (Section 4.3); new simulation and hardware implementation results on waveform-agile tracking (Sections 5.4 and 6.2). mation error (RMSE) performance. We implement the new PPF-IMH algorithm on a Xilinx Virtex-5 field programmable gate array (FPGA) platform. For a one-dimensional problem with 1,000 particles, the PPF-IMH architecture with four processing elements uses less than 5% of a Virtex-5 FPGA's resource and takes 5.85 μs for one iteration. We also incorporate waveform-agile tracking techniques into the PPF-IMH algorithm. We demonstrate a significant performance improvement when the waveform is adaptively designed at each time step with 6.84 μs FPGA processing time per iteration.

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Section: Processing Element Architecturementioning

confidence: 99%

Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing

Liu

Zhang

Chakrabarti

et al. 2011

J Sign Process Syst

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“…Because PF is based on the MC method, the computational complexity is inherently high and the real‐time processing may become an implementation difficulty. Fortunately, some recent researches have shown that with dedicated designed hardware circuits in field programable gate arrays platform, the processing rate can achieve an order of magnitude of 100 kHz . This indicates that the proposed communication system can meet the requirements in moderate data rate communications.…”

Section: Application To Secure Communicationmentioning

confidence: 93%

Improved auxiliary particle filter‐based synchronization of chaotic Colpitts circuit and its application to secure communication

Shi

Zhang

et al. 2013

Wireless Communications

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In this paper, we propose a synchronization scheme based on an improved auxiliary particle filter (IAPF) for chaotic Colpitts circuit and conduct an experimental study on the synchronization performance with application to secure communications. Specifically, with the synchronization scheme, when the chaotic signals generated by an analog Colpitts circuit are transmitted through a nonideal channel, the distorted signals are processed digitally by the novelly designed IAPF at the receiver, in order to obtain the synchronized signals of the transmitter circuit. Experimental results indicate that synchronization can be achieved over both the additive white Gaussian noise channel and the multipath fading channel with low signal‐to‐noise ratio, even if there exist severe circuit parameter mismatches between the transmitter and the receiver. Furthermore, a chaos‐masking secure communication system is constructed and verified over both the additive white Gaussian noise channel and the multipath fading channel, and the bit error rate is evaluated versus different signal‐to‐noise ratios and symbol periods. It is shown that the achievable bit error rate can reach the order of magnitude of 10 − 4 without error correction coding techniques. In addition, security analysis demonstrates that the proposed chaotic secure communication system is resistant to the brute‐force attack. Copyright © 2013 John Wiley & Sons, Ltd.

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“…Target's location is predicted with division of the sum of all models estimations by the sum of particle weights which are obtained by resampling. Thus, A2 does not need model transition probabilities for determining the model and has a better processing time performance when compared to a traditional MMPF [17].…”

Section: Multi Model Particle Filtermentioning

confidence: 99%

“…The results of simulation given for 1000 particles showed that the error performance of the proposed PF is approximately similar to that of MMPF and it uses about 10% less time than MMPF. Hong et al proposed a MMPF structure consisting of a statistical model for maneuvering motion [17]. It is stated that, for the 0.01 noise level and 1000 particles, the presented algorithm has similar error to traditional MMPF and lower processing time at resampling step.…”

Section: Introductionmentioning

confidence: 99%

A New Motion Model Selection Approach for Multi-Model Particle Filters

Ucar¹,

Yılmaz²

2019

RADIOENGINEERING

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One of the important factors in real-time tracking of the moving radar targets is the speed of the algorithm. In the multi-model particle filters (MMPFs) which is frequently preferred tracking of such targets, the numbers of particles and motion models are important parameters determining the speed of the filter. Reducing the number of particles and/or the model transitions processes as much as possible will facilitate real-time tracking of moving targets by accelerating the algorithm. In this study, for reducing the time cost of the MMPF, a new approach called weighted statistical model selection (WSMS) which reduces the number of model estimation calculations is proposed. A new basic MMPF algorithm that allows the use of the WSMS approach is also constituted. In order to evaluate the success of the WSMS; the MMPFs integrated with the WSMS, are simulated for different noise variances, particle numbers, and scenarios. The simulation results are compared based on processing time and prediction error criterions. The results demonstrate that the WSMS approach increases the speed of the algorithm by reducing the processing time at high rates without any change in the prediction error and, thus it can be used in real-time tracking of the moving targets.

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Easy-hardware-implementation MMPF for Maneuvering Target Tracking: Algorithm and Architecture

Cited by 10 publications

References 19 publications

Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing

Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing

Improved auxiliary particle filter‐based synchronization of chaotic Colpitts circuit and its application to secure communication

A New Motion Model Selection Approach for Multi-Model Particle Filters

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