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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.

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.

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.

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. HighlightsRange distributed targets in compound-Gaussian clutter is considered.Noncoherent integration and GLRT based DP-TBD strategies are proposed.A covariance matrix estimation strategy is proposed.

In this paper, radar detection of Swerling target of type 3 in GO-distributed clutter via dynamic programming based track-before-detect (DP-TBD) is considered. Conventional DP-TBD suffers from significant performance loss due to the high frequency of target-like outliers. To enhance the detection performance of DP-TBD, the log-likelihood ratio (LLR) of the measurements is derived and used in the integration process of DP-TBD taking the place of amplitude that used in the conventional DP-TBD strategy. Various simulations are used to examine the detection performances of different DP-TBD strategies. Simulation results show that significant performance improvement can be achieved, especially for vary heavy-tailed clutter.