Time-Varying Waveform Selection and Configuration for Agile Sensors in Tracking Applications

Sira, S.P.; Papandreou-Suppappola, Antonia; Morrell, Darryl

doi:10.1109/icassp.2005.1416445

Cited by 28 publications

(16 citation statements)

References 8 publications

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“…The target is tracked using a phased-array radar system, transmitting waveforms from a class of generalized frequencymodulated (GFM) waveforms with Gaussian envelope [70]. A Gaussian-windowed GFM waveform, at time step k, is given by…”

Section: Waveform-agile Tracking Applicationmentioning

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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Section: Waveform-agile Tracking Applicationmentioning

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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“…Therefore, at each time step of the scenario, the parameters of the waveform to be transmitted at the next time step are selected such as to achieve low AF sidelobes in the areas where the weak target is expected to be found, resulting to a minimization of the expected RMSE. This is computationally efficient compared to iterative methods of waveform parameter selection [11,12]. However, the entire multitarget particle filtering method proposed is still associated with a large computational load which is not expected to reach real time operation with state-of-the art hardware which also limits the number of targets that can be tracked.…”

Section: Adaptive Waveform Selectionmentioning

confidence: 99%

“…We also develop an adaptive configuration strategy to select the MCPC waveform parameters based on the relative positioning of the targets in the delay-Doppler plane. In contrast to previous methods [11,12], the proposed adaptive waveform selection method is not iterative; in contrast, waveform parameters are directly selected based on the state information on the weak target relative to the strong targets. The new IP-LPF tracker uses a proposal distribution that is based on the independent partitions algorithm [13,14] and the likelihood particle filter [15].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive highly localized waveform design for multiple target tracking

Kyriakides

Morrell

Papandreou-Suppappola

2012

EURASIP J. Adv. Signal Process.

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When tracking multiple targets, radar measurements from weak targets are often masked by the ambiguity function (AF) sidelobes of the measurements from stronger targets. This results in deteriorated tracking performance and lost tracks. In this study, we consider the design of configurable waveforms whose AF sidelobes can be positioned to unmask weak targets. Specifically, we construct multicarrier phase-coded (MCPC) waveforms based on Björck constant amplitude zero-autocorrelation (CAZAC) sequences. The MCPC CAZAC waveforms exhibit wide regions in their AF surface without sidelobes and allow for selective positioning of sidelobes. We apply these waveforms in the context of a target tracker by selecting waveform parameters that minimize the expected tracking error. We show that this is accomplished by selecting the position of AF sidelobes to unmask weak targets. The target tracker is based on an independent partitions likelihood particle filter that is capable of processing the high-resolution measurements resulting from the Björck CAZAC sequences and tracks a fixed and known number of targets. Using simulations, we demonstrate the improvement in tracking performance when we adaptively select the MCPC CAZAC waveforms over tracking using non-adaptive waveform configurations or single-carrier phase-coded CAZAC waveforms.

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“…Nevertheless, systems that provide limited libraries of selectable waveforms (e.g., constantfrequency pulses, linear and quadratic chirps with various parameters) are on the horizon and work on optimal scheduling with such libraries is being published (e.g., [4]). …”

Section: Acoustic Sensingmentioning

confidence: 99%

Waveform-Agile Sensing: Opportunities and Challenges

Cochran

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

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Time-Varying Waveform Selection and Configuration for Agile Sensors in Tracking Applications

Cited by 28 publications

References 8 publications

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

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

Adaptive highly localized waveform design for multiple target tracking

Waveform-Agile Sensing: Opportunities and Challenges

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