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

机译:G0分布杂波中的雷达探测前跟踪策略

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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.
机译:本文考虑通过基于动态编程的雷达先探测后跟踪(DP-TBD)进行目标检测。使用enlr G0分布对杂波进行建模,该分布通常用于对从高分辨率雷达和以小掠角工作的雷达接收到的杂波进行建模。考虑两个目标模型,即Swerling 0和1模型,以捕获雷达横截面随时间的变化。在这种情况下,由于目标类离群值的可能性很高,因此积分幅度的DP-TBD技术会遭受明显的性能损失。本文将对数似然比(LLR)用于DP-TBD的积分过程中,代替幅度,以提高雷达的检测性能。首先导出上述目标模型的LLR表达式。但是,它们都没有封闭形式的解决方案。为了降低评估LLR的复杂性,提出了一种有效而精确的近似方法。然后将近似的LLR用于DP-TBD的集成过程中。仿真用于检查近似方法的效率以及不同DP-TBD策略的性能。

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