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首页> 外文期刊>IEEE Transactions on Automatic Control >Tracking in clutter with strongest neighbor measurements. I. Theoretical analysis
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Tracking in clutter with strongest neighbor measurements. I. Theoretical analysis

机译：以最强的邻居测量来进行杂波跟踪。一，理论分析

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摘要

When tracking a target in clutter, a measurement may have originated from either the target, clutter, or some other source. The measurement with the strongest intensity (amplitude) in the neighborhood of the predicted target measurement is known as the "strongest neighbor" (SN) measurement. A simple and commonly used method for tracking in clutter is the so-called strongest neighbor filter (SNF), which uses the SN measurement at each time as if it were the true one. The paper deals with tracking in clutter with the SN measurements. It presents analytic results, along with useful comments, for the SN measurement and the SNF, including the a priori and a posteriori probabilities of data association events, the conditional probability density functions and the covariance matrices of the SN measurement, and various mean-square-error matrices of state prediction and state update. These results provide valuable insight into the problem of tracking in clutter and theoretical foundation for the development of improved tracking algorithms, for performance analysis, prediction, and comparison of tracking with the SN measurements, and for solving some important detection-tracking problems, such as the optimal determination of the detection threshold and gate size.
机译：在杂波中跟踪目标时，测量可能源自目标，杂波或其他来源。在预测目标测量值附近具有最强强度（振幅）的测量值称为“最强邻居”（SN）测量值。一种简单且常用的杂波跟踪方法是所谓的最强邻居滤波器（SNF），它每次都使用SN测量，就好像它是真实的一样。本文讨论了SN测量中的混乱跟踪。它提供了SN测量和SNF的分析结果以及有用的注释，包括数据关联事件的先验概率和后验概率，SN测量的条件概率密度函数和协方差矩阵，以及各种均方状态预测和状态更新的错误矩阵。这些结果为开发杂乱无章的跟踪问题提供了宝贵的见解，并为改进跟踪算法的开发，性能分析，预测以及与SN测量的跟踪比较提供了理论基础，以及解决了一些重要的检测跟踪问题，例如最佳确定检测阈值和门的大小。

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