漂移瑞利滤波算法及其在纯方位跟踪中的应用

余华; 朱秋萍; 刘雅娴

漂移瑞利滤波算法及其在纯方位跟踪中的应用

引用本文：余华,朱秋萍,刘雅娴.漂移瑞利滤波算法及其在纯方位跟踪中的应用[J].计算技术与自动化,2014,(4):74-77

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作者	单位
余华,朱秋萍,刘雅娴	(武汉东湖学院电子信息工程学院,湖北武汉430212)

中文摘要:单传感器纯方位跟踪问题仍是目前研究的重点和难点，方位角变化率很大时往往使得扩展卡尔曼滤波等矩匹配算法不稳定或发散。重点研究漂移瑞利滤波算法在方位角变化率很大的复杂单传感器纯方位目标跟踪场景下的性能，比较了漂移瑞利滤波，扩展卡尔曼滤波，不敏卡尔曼滤波，粒子滤波等其他非线性跟踪算法的性能，推导并计算了相关问题的Cramer-Rao下界并将其用作比较估值准确性和衡量算法性能的评价指标。仿真结果表明：漂移瑞利滤波算法的性能优于其他矩匹配算法，能达到与粒子滤波大体相同的计算精度，但它的计算速度比粒子滤波算法快几个数量级。

中文关键词:漂移瑞利滤波算法扩展卡尔曼滤波不敏卡尔曼滤波粒子滤波非线性跟踪算法

Principles of Shifted Rayleigh Filter and Its Application in Single-sensor Bearings-only Tracking

Abstract:The problem of continues to present challenges to tracking algorithms, particularly in certain difficult scenarios such as ones with high bearing rates. In such scenarios, the performance of the Shifted Rayleigh Filter (SRF) is compared with that of other techniques such as Extended Kalman filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) in chapter 3. The results are also compared with the theoretical Cramer-Rao Lower Bound (CRLB). Simulations show that the SRF is superior to other moment matching algorithms such as EKF and UKF and is able to achieve comparable performance to PF while being orders of magnitude faster.

keywords:single-sensor bearings-only tracking Shifted Rayleigh Filter(SRF) Extended Kalman Filter(EKF) Unscented Kalman Filter (UKF) cramer-rao lower bound

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