被动跟踪应用中的高斯混合初始化

M. Daun, R. Kaune
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引用次数: 17

摘要

本文描述了用高斯混合(GM)逼近非线性后验密度。GM用于初始化一组卡尔曼滤波器。对于每个高斯项,启动一个卡尔曼滤波器。讨论了近似的基本条件和性质。从不同的跟踪应用实例,多静态跟踪和被动辐射源定位的TDOA测量,进行了研究。对结果进行了讨论,并与现有方法进行了比较。用估计的均方根误差作为评价标准。在蒙特卡罗仿真中分析了高斯混合方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gaussian Mixture initialization in passive tracking applications
This paper describes the approximation of a nonlinear posterior density by a Gaussian Mixture (GM). The GM is used to initialize a bank of Kalman filters. For each Gaussian term, a Kalman filter is started. The basic conditions and the quality of the approximation are discussed. Examples from different tracking applications, the multistatic tracking and passive emitter localization using TDOA measurements, are investigated. The results are discussed and compared with existing approaches. The RMS error of the estimate is used as an evaluation criterion. The performance of the Gaussian Mixture approach is analyzed in Monte Carlo simulations.
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