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首页> 外文期刊>Chinese Journal of Electronics >A Dynamic Programming Track-Before-Detect Algorithm Based on Local Linearization for Non-Gaussian Clutter Background
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A Dynamic Programming Track-Before-Detect Algorithm Based on Local Linearization for Non-Gaussian Clutter Background

机译：高斯杂波背景下基于局部线性化的动态规划事前检测算法

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摘要

The Dynamic programming track before detect (DP-TBD) algorithm has been widely used for detection and tracking of weak targets. The selection of the merit function has an immediate influence on the performance of the DP-TBD. The amplitude merit function is easy to calculate, but the performance of which will decrease in the presence of non-Gaussian clutter. The likelihood ratio merit function in closed analytical form is difficult to derive under non-Gaussian background without target signal parameters. To solve this problem, a novel DPTBD algorithm based on local linearization is proposed. Taking maximum of the state conditional probability ratio of the target as the optimal criteria, a recursive integration equation is derived. The equation is locally linearized by Taylor series expansion and a suboptimal multi-frame test statistic is developed. The calculation of new merit function in the statistic needs only clutter distribution model, and heavy clutter peak can be restrained by making use of clutter distribution characters. So the proposed algorithm can efficiently extract weak target in strong non-Gaussian clutter. Numerical simulations are provided to assess and compare the performance of the proposed algorithm. It turns out that the proposed algorithm has better detection and tracking performance than the widely used DPTBD algorithm at present and is resilient to various clutter distribution models.
机译：检测前动态编程跟踪（DP-TBD）算法已广泛用于检测和跟踪弱目标。优值功能的选择直接影响DP-TBD的性能。幅度优值函数易于计算，但是在存在非高斯杂波的情况下其性能会降低。在没有目标信号参数的非高斯背景下，很难得出封闭分析形式的似然比优值函数。为了解决这个问题，提出了一种基于局部线性化的DPTBD算法。以目标的状态条件概率比的最大值为最佳准则，推导了一个递归积分方程。该方程通过泰勒级数展开进行局部线性化，并开发了次优的多帧检验统计量。统计中新的价值函数的计算只需要杂波分布模型，就可以通过利用杂波分布特征来抑制较重的杂波峰。因此，该算法可以有效地提取出强非高斯杂波中的弱目标。提供数值模拟以评估和比较所提出算法的性能。事实证明，与目前广泛使用的DPTBD算法相比，该算法具有更好的检测和跟踪性能，并且对各种杂波分布模型具有弹性。

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