EFFICIENT ADAPTIVE DETECTION THRESHOLD OP- TIMIZATION FOR TRACKING MANEUVERING TAR- GETS IN CLUTTER
EFFICIENT ADAPTIVE DETECTION THRESHOLD OP- TIMIZATION FOR TRACKING MANEUVERING TAR- GETS IN CLUTTER
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DOI：
10.2528/pierb12041701
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
J. T. Wang;H. Q. Wang;Y. Qin;Z. Zhuang
中科院分区：
文献类型：
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作者：
J. T. Wang;H. Q. Wang;Y. Qin;Z. Zhuang
在本文中，我们重点研究自适应先验检测阈值设置问题，以优化杂波中机动目标跟踪的联合检测跟踪系统的整体性能。结果表明，即使对于非线性测量方程的情况，我们的问题也可以通过力矩匹配对机动目标马尔可夫切换动力学进行高斯滑动来简化为信息缩减因子（IRF）最大化。我们提出的自适应阈值设置方法大大优于传统的阈值设置方法，并且与该问题的早期方法相比，在跟踪性能，特别是跟踪丢失百分比（TLP）方面也表现出轻微的改进。然而，我们的方法的计算负担显着减少，因为在我们的方法中，通常只需要一个对应于公共验证区域的IRF，而不是每个对应于单独模型条件验证区域的IRF，在每个时间步进行阈值优化，并且对于Neyman-Pearson（NP）检测器的特殊情况也可以获得近似封闭形式的解。
In this paper, we focus on the adaptive prior detection threshold setting problem to optimize the overall performance of the joint detection-tracking system for maneuvering target tracking in clutter. It is shown that our problem can be reduced to the information reduction factor (IRF) maximization by Gaussian fltting of maneuvering target Markovian switching dynamics via moment matching, even for the case with the nonlinear measurement equation. Our proposed adaptive threshold setting method outperforms the conventional threshold setting approaches greatly and also exhibits a mildly improvement in comparison with the earlier method for this problem in terms of tracking performance, especially in track loss percentage (TLP). However the computational burden of our method is reduced signiflcantly because in our method generally only one IRF corresponding to the common validation region, not the every IRF corresponding to the individual model-conditioned validation region, is needed for threshold optimization at each time step and an approximate closed-form solution can also be obtained for the special case of the Neyman-Pearson (NP) detector.