Track‐before‐detect algorithm based on dynamic programming for multi‐extended‐targets detection

Yan, Baixing; Xu, Luping; Li, Mu-Qing; Yan, Jiang

doi:10.1049/iet-spr.2016.0582

Cited by 40 publications

(32 citation statements)

References 27 publications

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“…The measurement at each radar scan is an image of an arbitrary dimensionality with N x × N y pixels indexed by i = 1...N x in the X axis and the j = 1...N y in Y axis, respectively. Recent TBD research [13,14] on radar considers extended targets with Gaussian or non-Gaussian noise, such as Rayleigh noise [15] and KA-distributed noise [16,17]. Due to the complexity of the multi-target tracking problem, we assume the point target of Constant Velocity (CV) model with Gaussian noise for simplicity in this paper.…”

Section: Target Dynamic Model and Measurement Modelmentioning

confidence: 99%

Parallel Computing Based Dynamic Programming Algorithm of Track-before-Detect

Guo

Wenming³

et al. 2018

Symmetry

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The conventional dynamic programming-based track-before-detect (DP-TBD) methods are usually intractable in multi-target scenarios. The adjacent targets may interfere with each other, and the computational complexity is increased with the number of targets. In this paper, a DP-TBD method using parallel computing (PC-DP-TBD) is proposed to solve the above problems. The search region of the proposed PC-DP-TBD is divided into several parts according to the possible target movement direction. The energy integration is carried out independently and parallel in each part. This contributes to reducing the computational complexity in each part, since the divided search region is smaller than the whole one. In addition, the target energy can only be integrated adequately in the part in which the search direction matches the target movement. This is beneficial to improve the ability to detect the targets with various movement directions in different parts with different search directions. The solution to the problem of the adjacent targets interfering with each other is discussed. The procedure of the parallel computing in the proposed PC-DP-TBD is presented in detail. Simulations are conducted to verify the superiority of the proposed PC-DP-TBD in terms of detection probability and computational complexity.

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Section: Target Dynamic Model and Measurement Modelmentioning

confidence: 99%

Parallel Computing Based Dynamic Programming Algorithm of Track-before-Detect

Guo

Wenming³

et al. 2018

Symmetry

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“…Various algorithms have been developed for extended target detection and tracking. The algorithms are mainly fall into two categories: extended target probability hypothesis density (ET-PHD) filters [1,2,3,4,5] and track-before-detect algorithms [6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…For the presence of clutter measurements, extended target which only generates a few measurements is hard to be detected. Track-before-detect algorithms [6,7,8,9,10] are superior in detecting and tracking weak targets for taking full merits of multiscan. Track-before-detect algorithms mainly have three implementations: particle filter based track-before-detect (PF-TBD) [6], dynamic programming based track-before-detect (DP-TBD) algorithms [7,8] and Hough transformation based track-before-detect (HT-TBD) [9,10].…”

Section: Introductionmentioning

confidence: 99%

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A Grey Wolf Optimization-Based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

Yan

Zhao

et al. 2019

Sensors

Self Cite

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A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.

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“…This kind of method can effectively avoid the information loss caused by threshold decision and improve the detection and tracking performance under low SNR. The common TBD method includes Dynamic Programming (DP) [40,41], Hough Transform (HT) [42], and particle filter (PF) [43]. Particle filtering techniques that have the advantage of providing computational tractability are applicable under most general circumstances since there is no assumption made on the form of the density.…”

Section: Introductionmentioning

confidence: 99%

A Joint Detection and Tracking Algorithm for Unresolved Target and Radar Decoy

Song¹,

Cai²,

Fu³

2019

PIER B

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Miniature Air Launched Decoy (MALD) is an electronic warfare technique for inducing an angular deception in a monopulse radar by recreating glint angular error. MALD flies cooperatively with the true target, forms unresolved group targets within the radar beam, and destroys the detection, tracking and parameter estimation of monopulse radar for the true target. In this paper, a typical scenario for one target and one decoy was discussed, and the measurement model of target and decoy based on the actual non-ideal sampling conditions was established. The joint multi-targets probability density was adopted to dynamically describe the number and state of the targets within the radar beam. Based on the original observation without threshold decision, a joint detection and tracking algorithm for unresolved target and decoy was proposed under the Bayesian framework, and the judgment of existence of jamming and the target state estimation were deduced. Simulation results showed that the proposed method enabled quick detection of the appearance of MALD and estimated the state of target with minimal delay and high precision. Stable tracking of the true target was achieved under severe jamming conditions.

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Track‐before‐detect algorithm based on dynamic programming for multi‐extended‐targets detection

Cited by 40 publications

References 27 publications

Parallel Computing Based Dynamic Programming Algorithm of Track-before-Detect

Parallel Computing Based Dynamic Programming Algorithm of Track-before-Detect

A Grey Wolf Optimization-Based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

A Joint Detection and Tracking Algorithm for Unresolved Target and Radar Decoy

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