摘要
针对纯方位被动目标跟踪中扩展卡尔曼滤波算法易发散的不足,提出了一种自适应的改进算法。该算法利用极大后验噪声估计器Sage-Husa对虚拟观测噪声进行实时在线估计,动态补偿线性化带来的误差。算法的对比仿真分析结果表明,AEKF较之EKF滤波效果有所改善,增强了稳定性,提高了精度,为水下纯方位被动目标跟踪的实现提供一种新的方法。
Bearings-only tracking（BOT）is a key problem in the academic domain because it is nonlinear essentially. A new adaptive filter（AEKF） is proposed considering the divergence shortage of extend Kalman filter（EKF）. The new algorithm compensates the error of linearization dynamically by using Sage-Husa noise estimator online. The simulation results demonstrate that the AEKF has better performance than the EKF both in stability and precision. It provides a new method for the realization of underwater bearings-only tracking.
出处
《海军工程大学学报》
CAS
北大核心
2007年第1期86-89,98,共5页
Journal of Naval University of Engineering
基金
国防科技预研基金资助项目
