Track-before-detect strategies for range distributed target detection in compound-Gaussian clutter

Jiang, Haichao; Yi, Wei; Cui, Guolong; Kong, Lingjiang; Yang, Xiaobo

doi:10.1016/j.sigpro.2015.09.034

Cited by 34 publications

(8 citation statements)

References 19 publications

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“…However, the TBD method described above can obtain only one scatterer range trajectory, which cannot meet the requirement of getting range trajectories of each scatterers. Although some TBD methods for multitarget detecting and tracking have been proposed [20,21], they require that the state of different targets can not be the same. However, in this paper, the different scatterers are usually at the same azimuthal angle and may be of the same range at some scans (i.e., the range trajectories may be intersected).…”

Section: Range Trajectory Backtracking and Micromotion Featurementioning

confidence: 99%

Micromotion Feature Extraction of Space Target Based on Track-Before-Detect

Chen¹,

Zhang²,

Luo³

et al. 2017

Journal of Sensors

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The micromotion feature of space target provides an effective approach for target recognition. The existing micromotion feature extraction is implemented after target detection and tracking; thus the radar resources need to be allocated for target detection, tracking, and feature extraction, successively. If the feature extraction can be implemented by utilizing the target detecting and tracking pulses, the radar efficiency can be improved. In this paper, by establishing a feedback loop between micromotion feature extraction and track-before-detect (TBD) of target, a novel feature extraction method for space target is proposed. The TBD technology is utilized to obtain the range-slow-time curves of target scatterers. Then, micromotion feature parameters are estimated from the acquired curve information. In return, the state transition set of TBD is updated adaptively according to these extracted feature parameters. As a result, the micromotion feature parameters of space target can be extracted concurrently with implementing the target detecting and tracking. Simulation results show the effectiveness of the proposed method.

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Section: Range Trajectory Backtracking and Micromotion Featurementioning

confidence: 99%

Micromotion Feature Extraction of Space Target Based on Track-Before-Detect

Chen¹,

Zhang²,

Luo³

et al. 2017

Journal of Sensors

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“…Existing approaches to TBD include dynamic programming (DP) [8]- [10], maximum likelihood (ML) [5], [11], particle filter (PF) [7], [12], [13], and random finite set (RFS) [14], [15] based methods. Being subject to computational complexity, the DP-TBD is most widely used and has superior performance with different types of dim targets, such as point target [16], extended target [9], [17], range distributed target [18] and the targets of different Swerling types [17]. Various clutter environments arising in engineering applications, such as compound-Gaussian [18], G0-distributed [19], K-distributed [20], have also been considered, and the DP-TBD has also been tested with more complex real data, e.g., target detection in severe sea clutter [21].…”

Section: Introductionmentioning

confidence: 99%

“…Being subject to computational complexity, the DP-TBD is most widely used and has superior performance with different types of dim targets, such as point target [16], extended target [9], [17], range distributed target [18] and the targets of different Swerling types [17]. Various clutter environments arising in engineering applications, such as compound-Gaussian [18], G0-distributed [19], K-distributed [20], have also been considered, and the DP-TBD has also been tested with more complex real data, e.g., target detection in severe sea clutter [21]. The standard procedure of such techniques is the basic generalized likelihood ratio test (GLRT) approach on the range-azimuth map, range-Doppler map, or maps of higher dimensions [16], [18].…”

Section: Introductionmentioning

confidence: 99%

Target Detection and Tracking With Errors in Intensity and Mapping Dimensions

Luo¹,

Wang²,

Deng³

et al. 2019

IEEE Access

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This paper addresses an engineering problem that measurement errors in intensity and mapping dimensions exist in the track-before-detect (TBD) architecture simultaneously. Drawing lessons from the probability data association (PDA) and the basic generalized likelihood ratio test TBD (GLRT-TBD), the joint log-likelihood ratio test TBD (JLLRT-TBD) method is proposed. Unlike conventional TBD algorithms, which consider intensities captured in the cells occupied by physically admissible transitions only, the novel method regards the cells centered at the transitions as effective ones. After that, detection probabilities are deduced from their positions, and then weighting likelihood ratios of their intensities results in metrics of different transitions. Finally, the maximum metric is compared with the designed threshold, and the estimation of the target state is obtained by retracing the maximization process. With this, the incoherent integration gain along consecutive scans is acquired, and the increment in computational complexity is avoided. Monte Carlo numerical results demonstrate that the detection and estimation performances of the JLLRT-TBD are superior significantly to those of the basic GLRT-TBD while the lower averaged execution time is spent.

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“…The dynamic-programming-based trackbefore-detect (DP-TBD) technique [4] is a practical method for detecting weak targets. Rangespread target detection based on DP-TBD has been investigated in compound-Gaussian clutter with an unknown texture distribution [5].…”

mentioning

confidence: 99%

“…The results of evaluating the detection performance of the improved DP-TBD method are depicted in Figure 1. Simulated generalized Pareto clutter with different shape parameters is used to verify the performance of the improved DP-TBD and GLRT-DP-TBD [5] methods, as illustrated in Figure 1 Improved DP-TBD with λ=5 Improved DP-TBD with λ=10 Improved DP-TBD with λ=20 Improved DP-TBD with λ=50 GLRT-DP-TBD with λ=5 GLRT-DP-TBD with λ=10 GLRT-DP-TBD with λ=20 GLRT-DP-TBD with λ=50 For simplicity, prior information about the velocity is assumed to be known. The data from all 30 scans is processed using the improved DP-TBD method in a way of slide windows [7].…”

mentioning

confidence: 99%