Dynamic Configuration of Time-Varying Waveforms for Agile Sensing and Tracking in Clutter

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

doi:10.1109/tsp.2007.894418

Cited by 105 publications

(69 citation statements)

References 22 publications

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“…where M is a positive semi-definite weighting matrix that ensures the units of the objective function are consistent [23] or is used to facilitate individual component penalization, η is SNR and f ROC denotes the ROC curve that links P f a with P d .…”

Section: Prior Detection Threshold Optimizationmentioning

confidence: 99%

“…When measurement equation involves nonlinearities, e.g., range and range rate are provided to the tracker [23], the unscented Kalman filter (UFK), which exhibits many performance improvements over the extended Kalman filter (EKF), can be used as elemental filter in the IMM algorithm. However PDA can still be used to deal with the measurement-origin uncertainty.…”

Section: Extension To the Case With The Nonlinear Measurement Equationmentioning

confidence: 99%

“…Then the measurements that are not target-originated still increase the covariance of the state estimate. It has been shown that the expected posterior covariance matrix of the state in this scenario can be given by [23] E…”

Section: Extension To the Case With The Nonlinear Measurement Equationmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Adaptive Detection Threshold Optimization for Tracking Maneuvering Targets in Clutter

Wang¹,

Wang²,

Qin³

et al. 2012

PIER B

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Abstract-In this paper, we focus on the adaptive prior detection threshold setting problem to optimize the overall performance of the joint detection-tracking system for maneuvering target tracking in clutter. It is shown that our problem can be reduced to the information reduction factor (IRF) maximization by Gaussian fitting of maneuvering target Markovian switching dynamics via moment matching, even for the case with the nonlinear measurement equation. Our proposed adaptive threshold setting method outperforms the conventional threshold setting approaches greatly and also exhibits a mildly improvement in comparison with the earlier method for this problem in terms of tracking performance, especially in track loss percentage (TLP). However the computational burden of our method is reduced significantly because in our method generally only one IRF corresponding to the common validation region, not the every IRF corresponding to the individual model-conditioned validation region, is needed for threshold optimization at each time step and an approximate closed-form solution can also be obtained for the special case of the Neyman-Pearson (NP) detector.

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Section: Prior Detection Threshold Optimizationmentioning

confidence: 99%

Section: Extension To the Case With The Nonlinear Measurement Equationmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Adaptive Detection Threshold Optimization for Tracking Maneuvering Targets in Clutter

Wang¹,

Wang²,

Qin³

et al. 2012

PIER B

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show abstract

“…In [11,12], iterative adaptive waveform techniques were developed for nonlinear system models with a single target and using frequency-modulated waveforms. These adaptive waveform techniques assume that the measurements are processed using matched filters on a fixed grid.…”

Section: Introductionmentioning

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%

Adaptive highly localized waveform design for multiple target tracking

Kyriakides

Morrell

Papandreou-Suppappola

2012

EURASIP J. Adv. Signal Process.

Self Cite

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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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Conditional Posterior Cramér–Rao Lower Bound and its Applications in Adaptive Sensor Management

Niu

Zuo

Maşazade

et al. 2011

Distributed Video Sensor Networks

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Dynamic Configuration of Time-Varying Waveforms for Agile Sensing and Tracking in Clutter

Cited by 105 publications

References 22 publications

Efficient Adaptive Detection Threshold Optimization for Tracking Maneuvering Targets in Clutter

Efficient Adaptive Detection Threshold Optimization for Tracking Maneuvering Targets in Clutter

Adaptive highly localized waveform design for multiple target tracking

Conditional Posterior Cramér–Rao Lower Bound and its Applications in Adaptive Sensor Management

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