Posterior distribution preprocessing for passive DTV radar tracking: Simulated and real data

Hanusa, Evan; Vertatschitsch, Laura; Krout, David W.

doi:10.1109/acssc.2013.6810683

Cited by 1 publication

(1 citation statement)

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“…The second stage generates possible two-dimensional estimate and de-ghosting, and the third stage maintains fused tracks in 3-D Cartesian space. A similar three-step procedure was presented by Hanusa et al [10]; however, a posterior distribution illuminator fusion step and a joint probabilistic data association (JPDA)-based tracker were performed in the second and third stage, respectively. A Gaussian mixture probability hypothesis density (GMPHD) approach has recently been presented in the FKIE passive radar data set by Pikora and Ehlers [11].…”

Section: Introductionmentioning

confidence: 99%

DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment

Zhao

et al. 2019

IEEE Access

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Multistatic passive radar exploits illuminators of opportunity such as radio or television stations for tracking airborne targets. This paper focuses on broadcast signals in a single frequency network modulated according to the digital audio/video broadcasting (DAB/DVB) standards. The main challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional measurement-to-target association ambiguity, which increases data association complexity greatly. In this paper, to solve the additional measurement-to-illuminator data association ambiguity, a novel deterministic annealing probabilistic multi-hypothesis tracker based on the extended Kalman filter is proposed. The proposed algorithm works directly in the original three-dimensional Cartesian space, and can efficiently handle a large number of association hypotheses among the targets, measurements, and illuminators in a natural way, also it is computationally attractive. As the angular measurement is not available or often of extremely poor quality, this paper assumes measurements of bistatic range and range rate and does not process bearing information. The simulation results show that the proposed algorithm works reasonably well in dense clutter environment for multistatic passive radar system. INDEX TERMS Multistatic passive radar, extended Kalman filter, data association, deterministic annealing, probabilistic multi-hypothesis tracker.

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Section: Introductionmentioning

confidence: 99%

DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment

Zhao

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

Posterior distribution preprocessing for passive DTV radar tracking: Simulated and real data

Cited by 1 publication

References 5 publications

DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment

DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment

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