UKF算法在被动定位与跟踪中的应用

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Pub Date : 2018-10-01 DOI:10.1109/IAEAC.2018.8577916

Li Bingrong, Qu Chang-wen, Zhou Zheng, Guo Linpeng

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引用次数: 1

摘要

利用到达点和到达点信息，利用固定观测器实现对运动辐射源的无源定位。在定位系统中，状态模型一般是线性的，而测量模型往往是非线性的。借助扩展卡尔曼滤波(EKF)，可以估计目标的状态，并跟踪目标的运动轨迹。无气味卡尔曼滤波(UKF)是解决非线性系统问题的另一种方法。推导和仿真表明，利用基于DOA和TOA信息的EKF和UKF对运动辐射源进行定位和跟踪，具有收敛性和精度高的优点。相对而言，UKF优于EKF。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Application of UKF Alogrithm in Passive Localization and Tracking

Depending on the information of DOA and TOA, passive location to a moving emitter can be realized by a fixed observer. In the location system, the state model is generally linear, but the measurement model is frequently nonlinear. With the help of extended Kalman filter (EKF), the state of the target can be estimated, and the track of it can be also traced. Unscented Kalman filter(UKF) is another method to solve the problem of nonlinear system. Deduction and simulation shows that, using EKF and UKF based on DOA and TOA information to locate and track the moving emitter, can be convergent with a high accuracy. Relatively, UKF is better than EKF.

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来源期刊

2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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