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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Tracking in clutter with nearest neighbor filters: analysis and performance
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Tracking in clutter with nearest neighbor filters: analysis and performance

机译：使用最近的邻居过滤器进行杂波跟踪：分析和性能

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摘要

The measurement that is "closest" to the predicted target measurement is known as the "nearest neighbor" (NN) measurement in tracking. A common method currently in wide use for tracking in clutter is the so-called NN filter, which uses only the NN measurement as if it were the true one. The purpose of this work is two fold. First, the following theoretical results are derived: the a priori probabilities of all three data association events (updates with correct measurement, with incorrect measurement, and no update), the probability density functions (pdfs) of the NN measurement conditioned on the association events, and the one-step-ahead prediction of the matrix mean square error (MSE) conditioned on the association events. Secondly, a technique for prediction without recourse to expensive Monte Carlo simulations of the performance of tracking in clutter with the NN filter is presented. It can quantify the dynamic process of tracking divergence as well as the steady-state performance. The technique is a new development along the line of the recently developed general approach to the performance prediction of algorithm with both continuous and discrete uncertainties.
机译：与预测目标测量值“最接近”的测量值在跟踪中称为“最近邻居”（NN）测量值。当前广泛用于杂波跟踪的常用方法是所谓的NN滤波器，它仅使用NN测量，就好像它是真实的一样。这项工作的目的有两个方面。首先，得出以下理论结果：所有三个数据关联事件的先验概率（使用正确的度量值进行更新，使用错误的度量值并且没有更新），以关联事件为条件的NN测量的概率密度函数（pdfs） ，并根据关联事件对矩阵均方误差（MSE）进行一步一步预测。其次，提出了一种无需依靠昂贵的蒙特卡洛模拟进行预测的技术，该模拟使用NN滤波器进行了杂波跟踪。它可以量化跟踪发散的动态过程以及稳态性能。该技术是在最近开发的具有连续和离散不确定性的算法性能预测的通用方法的基础上进行的一项新开发。

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