Dynamic Programming Based Track-before-detect Research Articles

Dually Supervised Track-Before-Detect Processing of Multichannel Video SAR Data

Track-Before-Detect (TBD) algorithm has been used to track weak moving target shadow in video synthetic aperture radar (SAR), where strong maneuverability may deteriorate tracking performance. Fortunately, the Doppler characteristic of target can additionally provide velocity guidance for tracking. This article presents ground moving target indication (GMTI) results of the multi-channel video SAR raw data, and a tracking approach is proposed based on the Doppler supervision in an improved dynamic programming-based TBD (DP-TBD) framework that uses a dual-domain merit function. Both the sequential high-resolution SAR images and low-resolution Range-Doppler (RD) spectra are used, and the clutter in RD spectrum is suppressed by using an adaptive displaced phase center antenna (ADPCA) technique. Fine states can be searched in dual-domain through resampling from random expansion in state initialization and transition. The inverse shadow amplitude and Doppler energy of the same potential target are simultaneously integrated to determine whether the target exists. The proposed Dual-DP-TBD can deal with the tracking of a time-varying number of targets in successive measurements. Compared to other TBD algorithms, this approach has fewer missing alarms and false alarms thanks to precise velocity estimation constrained from diverse features both in SAR image and RD spectrum.

Joint Track-Before-Detect Algorithm for High-Maneuvering Target Indication in Video SAR

Moving target shadows continuously appear in the sequential images produced by video synthetic aperture radar (SAR), which conducts research on shadow-based detection and tracking of moving targets. This article addresses a typical shadow-based detection and tracking problem of dim high-maneuvering targets in the complex background. Building upon the dynamic programming-based track-before-detect (DP-TBD) algorithm, this article proposes a joint-processing-strategy-based DP-TBD (JP-DP-TBD) algorithm to detect and track multiple high-maneuvering targets in video SAR. Benefiting from both of the location and radial velocity information of the targets in the video SAR image and range-Doppler (RD) spectrum, the JP-DP-TBD algorithm can screen the state candidate region and retain more states that conform to the motion law of the target. Meanwhile, we adopt a dual-frame close-range matching mechanism to complete the automatic initialization of the candidate target state, whose validity has been verified on the real video SAR data. Experimental results on simulated data demonstrate that the proposed algorithm has better detection ability and fewer false alarms in comparison with other TBD algorithms.

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

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.

Track-Before-Detect Strategies for Radar Detection in G0-Distributed Clutter

This paper considers target detection via dynamic-programming based track-before-detect (DP-TBD) for radar systems. The clutter is modeled usingenlr G0 distribution, which is usually used to model clutter received from high-resolution radars and radars working at small grazing angles. Two target models, namely, Swerling 0 and 1 models, are considered to capture the radar cross section changes over time. DP-TBD techniques that integrate amplitude suffer from significant performance loss in this case due to the high likelihood of target-like outliers. In this paper, the log-likelihood ratio (LLR) is used in the integration process of DP-TBD, taking the place of amplitude, to enhance radar detection performance. The expressions for the LLR for the above target models are derived first. However, neither of them has a closed-form solution. In order to reduce the complexity of evaluating the LLR, efficient but accurate approximation methods are proposed. Then the approximated LLR is used in the integration process of DP-TBD. Simulations are used to examine the efficiency of the approximation methods as well as the performances of different DP-TBD strategies.

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

In recent years, multi-extended-targets detection in sea clutter has gained a special interest. Dynamic programming based track-before-detect (DP-TBD) algorithm is used to detect extended targets in video data of high resolution radars. Two innovations are presented in this work. First one is a novel partition method to cluster targets into well separate groups for the problem of high-dimensional maximisation. Second one is a novel merit function specifically designed for extended targets for the problem of target extended. Both the principle of this novel DP-TBD method specifically for extended targets and its detail implementation are presented. Then, both the real data and simulated data are performed. The comparison of the results shows that compared with particle filter based track before detect algorithm, the proposed method obtains better performance in detection rate, position error and false alarm rate. Meanwhile, far less calculation is spent with the novel partition method. Therefore, it can safely conclude that the proposed method is very practical under various sea conditions in the real world.

Multiframe Radar Detection of Fluctuating Targets Using Phase Information

This paper considers the detection of fluctuating targets via dynamic-programming-based track-before-detect (DP-TBD) in radar systems. Swerling targets of types 0, 1, and 3 are considered. DP-TBD usually integrates either squared amplitude or the logarithm of the envelope likelihood ratio (LELR) scoring functions. Thus, only amplitude information is taken into account regardless of the fact that the measurements are often complex valued. In this paper, the phase information is used in the integration process of DP-TBD to enhance radar detection performance. More precisely, the logarithm of the complex-measurement-based likelihood ratio (LCLR) is used, taking the place of squared amplitude or the LELR. First, we derive the expressions for the LELR and the LCLR for the three Swerling types. Then, to reduce the complexity of computing LELR and LCLR, we also propose efficient but accurate approximations for the LELR and the LCLR. Simulations are used to assess the performance of different DP-TBD strategies.

Knowledge-Based Track-Before-Detect Strategies for Fluctuating Targets in <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math> </inline-formula>-Distributed Clutter

In this paper, we address the problem of detecting fluctuating targets in heavy-tailed clutter through the use of dynamic programming based track-before-detect (DP-TBD) in radar systems. The clutter is modeled in terms of $K$ distribution, while the well-known Swerling targets of types 1 and 3 are considered to capture the target amplitude fluctuation scan-to-scan. Conventional DP-TBD suffers significant performance loss in this case due to the high frequency of target-like outliers. In this paper, we resort to the knowledge-based techniques, i.e., use a priori information to enhance radar detection performance. More precisely, knowledge-based DP-TBD (KB-DP-TBD) strategies are developed by incorporating the amplitude information into the integration process of DP-TBD. Simulations are used to assess performances and computational complexities of different DP-TBD strategies. The relevant result is that KB-DP-TBD can improve the detection performance over the conventional DP-TBD, especially for very heavy-tailed $K$ -distributed clutter.

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

This paper considers range distributed target detection in compound-Gaussian clutter, which is often arisen in high resolution radars. Two dynamic programming based track-before-detect (DP-TBD) strategies, i.e., noncoherent integration based DP-TBD (NCI-DP-TBD) and the generalized likelihood ratio test based DP-TBD (GLRT-DP-TBD) strategies, are proposed to address the detection problem. The detection performances of the proposed strategies are examined via numerical simulations.

New analytical approach to detection threshold of a dynamic programming track‐before‐detect algorithm

Maintaining the constant false alarm rate (CFAR) is an important issue for the dynamic programming-based track-before-detect (DP-TBD) in low signal-to-noise ratio environment. However, the existing method for analysing the false alarm probability, based on extreme value theory (EVT), leads to the inaccuracy of the obtained detection threshold. In this study, a new analytical approach to compute the false alarm probability of DP-TBD is presented. In the proposed method, the generalised Pareto distribution is utilised to approximate the false alarm probability based on the peaks over threshold model, which can maintain CFAR for DP-TBD method in the low signal-to-noise environment effectively. Simulation results show that this approach provides a more accurate false alarm probability estimation than previous EVT methods.

An Efficient Multi-Frame Track-Before-Detect Algorithm for Multi-Target Tracking

This paper considers the multi-target tracking (MTT) problem through the use of dynamic programming based track-before-detect (DP-TBD) methods. The usual solution of this problem is to adopt a multi-target state, which is the concatenation of individual target states, then search the estimate in the expanded multi-target state space. However, this solution involves a high-dimensional joint maximization which is computationally intractable for most realistic problems. Additionally, the dimension of the multi-target state has to be determined before implementing the DP search. This is problematic when the number of targets is unknown. We make two contributions towards addressing these problems. Firstly, by factorizing the joint posterior density using the structure of MTT, an efficient DP-TBD algorithm is developed to approximately solve the joint maximization in a fast but accurate manner. Secondly, we propose a novel detection procedure such that the dimension of the multi-target state no longer needs be to pre-determined before the DP search. Our analysis indicates that the proposed algorithm could achieve a computational complexity which is almost linear to the number of processed frames and independent of the number of targets. Simulation results show that this algorithm can accurately estimate the number of targets and reliably track multiple targets even when targets are in proximity.