Track‐Before‐Detect Techniques

Davey, Samuel J.; Rutten, Mark; Gordon, Neil

doi:10.1002/9781118450550.ch08

Cited by 11 publications

(7 citation statements)

References 36 publications

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“…

δ_{t} = 1

. In this scenario, we assume that the sensor noise is complex‐Gaussian, which is the standard model employed in radar applications [5]. As both algorithms assume non‐negative real values for each cell, the envelope (absolute value) of the image was supplied to the tracking algorithms.…”

Section: Simulationsmentioning

confidence: 99%

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Histogram‐PMHT for fluctuating target models

Gaetjens

Davey

Arulampalam

et al. 2017

IET Radar, Sonar & Navigation

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“…

δ_{t} = 1

Section: Simulationsmentioning

confidence: 99%

“…Similar to the PMHT, H‐PMHT assumes that the measured contribution from each source follows a multinomial distribution. Its most attractive feature is that it is able to retain linear complexity with the number of targets while still achieving performance that is close to the optimal Bayesian filter [5].…”

Section: Introductionmentioning

confidence: 99%

Histogram‐PMHT for fluctuating target models

Gaetjens

Davey

Arulampalam

et al. 2017

IET Radar, Sonar & Navigation

Self Cite

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“…The particle-filter-based LR-detectors are especially useful for a (radar) Track-Before-Detect application; see, e.g., [9]. Note that in our results/model, it is (implicitly) assumed that an object/signal is either present or absent during the entire M -sized observation window.…”

Section: B Optimalitymentioning

confidence: 99%

Optimal Particle-Filter-Based Detector

Boers

Mandal

2019

IEEE Signal Process. Lett.

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In this letter, we propose and prove the asymptotic optimality of a particle-filter-based detection scheme. The detection method can be used in a general nonlinear/nonGaussian signal detection problem. The proposed detection mechanism is based on likelihood ratio (LR) and thus optimal in the Neyman-Pearson (NP) sense, but we approximate the LR based on a particle filter (PF). We show the asymptotic optimality by proving that the PF-based approximation of the LR converges to the true LR as the number of particles increases to infinity. We also discuss the practical and operational implications of the result, the main one being that it is optimal in the sense that no other processing and detection mechanism can have higher probability of detection while have same or lower false alarm rate.

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“…Different from the traditional detection and tracking method, the TBD method utilizes the motion characteristics of the target and processes consecutive scans jointly to realize the fine detection and tracking of low SCR targets. Therefore, the TBD method has drawn extensive attention [16]. TBD includes batch methods, such as dynamic programming [17] and Hough transform [18], and recursive methods based on the Bayesian approach, such as particle filter (PF) [19].…”

Section: Introductionmentioning

confidence: 99%

A New Maritime Moving Target Detection and Tracking Method for Airborne Forward-looking Scanning Radar

Huo

Pei

Huang

et al. 2019

Sensors

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Maritime moving target detection and tracking through particle filter based track-before-detect (PF-TBD) has significant practical value for airborne forward-looking scanning radar. However, villainous weather and surging of ocean waves make it extremely difficult to accurately obtain a statistical model for sea clutter. As the likelihood ratio calculation in PF-TBD is dependent on the distribution of the clutter, the performance of traditional distribution-based PF-TBD seriously declines. To resolve these difficulties, this paper proposes a new target detection and tracking method, named spectral-residual-binary-entropy-based PF-TBD (SRBE-PF-TBD), which is independent from the prior knowledge of sea clutter. In the proposed method, the likelihood ratio calculation is implemented by first extracting the spectral residual of the input image to obtain the saliency map, and then constructing likelihood ratio through a binarization processing and information entropy calculation. Simulation results show that the proposed method had superior performance of maritime moving target detection and tracking.

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Track‐Before‐Detect Techniques

Cited by 11 publications

References 36 publications

Histogram‐PMHT for fluctuating target models

Histogram‐PMHT for fluctuating target models

Optimal Particle-Filter-Based Detector

A New Maritime Moving Target Detection and Tracking Method for Airborne Forward-looking Scanning Radar

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