Dynamic waveform design for target tracking using MIMO radar

Zhang, Jun; Manjunath, B.B.; Maalouli, G.; Papandreou-Suppappola, Antonia; Morrell, Darryl

doi:10.1109/acssc.2008.5074354

Cited by 18 publications

(5 citation statements)

References 23 publications

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“…With this notation, the PCRB for an unbiased estimate of has the form (32) where is the Fisher information matrix (FIM), given as (33) The recursive equation to compute the FIM in an online and recursive manner was proposed in [31], and we state it here for completeness. (17) and (18), respectively.…”

Section: A Computation Of the Posterior Cramér Rao Boundmentioning

confidence: 99%

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Scheduling and Power Allocation in a Cognitive Radar Network for Multiple-Target Tracking

Chavali

Nehorai

2012

IEEE Trans. Signal Process.

191

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We propose a cognitive radar network (CRN) system for the joint estimation of the target state comprising the positions and velocities of multiple targets, and the channel state comprising the propagation conditions of an urban transmission channel. We develop a measurement model for the received signal by considering a finite-dimensional representation of the time-varying system function which characterizes the urban transmission channel. We employ sequential Bayesian filtering at the receiver to estimate the target and the channel state. We propose a hybrid Bayesian filter that operates by partitioning the state space into smaller subspaces and thereby reducing the complexity involved with high-dimensional state space. The feedback loop that embodies the radar environment and the receiver enables the transmitter to employ approximate greedy programming to find a suitable subset of antennas to be employed in each tracking interval, as well as the power transmitted by these antennas. We compute the posterior Cramér-Rao bound (PCRB) on the estimates of the target state and the channel state and use it as an optimization criterion for the antenna selection and power allocation algorithms. We use several numerical examples to demonstrate the performance of the proposed system. Index Terms-Adaptive power allocation, adaptive scheduling, Bayesian inference, cognitive radar network, complex urban environment, multi-target tracking, sequential Monte Carlo estimation.

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Section: A Computation Of the Posterior Cramér Rao Boundmentioning

confidence: 99%

“…Here, the matrices , and are given as and (48) The elements of are given as [33] (52) and the elements of are given as…”

Section: Appendix Derivation Of the Pcrbmentioning

confidence: 99%

Scheduling and Power Allocation in a Cognitive Radar Network for Multiple-Target Tracking

Chavali

Nehorai

2012

IEEE Trans. Signal Process.

191

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“…Under a high signal-to-noise ratio (SNR) assumption, the covariance matrix of error estimation can be approximated by the posterior Cramér-Rao lower bound (PCRLB) [2,[27][28][29] …”

Section: Waveform-agile Sensing Algorithmmentioning

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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“…The covariance matrix of error estimation can be approximated by the posterior Cramér-Rao lower bound (PCRLB) [66,67,68,69]…”

Section: Waveform-agile Sensing Algorithmmentioning

confidence: 99%

Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

Liu

Zhang

Chakrabarti

et al. 2013

IEEE Trans. Signal Process.

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Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems.First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance.We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking.For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. i Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate com...

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Dynamic waveform design for target tracking using MIMO radar

Cited by 18 publications

References 23 publications

Scheduling and Power Allocation in a Cognitive Radar Network for Multiple-Target Tracking

Scheduling and Power Allocation in a Cognitive Radar Network for Multiple-Target Tracking

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

Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

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