基于初值更新的水下纯方位短时目标跟踪

Underwater Bearings-Only Short Time Target Tracking Based on Initial Value Updating

水下被动单站纯方位目标跟踪具有布局简单、隐蔽性强的优点,但所使用的滤波方法均存在对初值选取敏感的问题,且实际工程中会遇到目标速度快、观测时间短、量测数据少的情况,使这一问题更加凸显。针对这一情况,文中研究了初值对滤波产生影响的原因和方式,提出了一种基于初值更新的扩展卡尔曼滤波改进方法,通过后向迭代对初值重新估计并不断更新,逐步降低初值误差对滤波结果的影响。仿真结果表明,该方法可减少滤波器对初值选取的依赖,降低最终的估计误差。

Underwater passive bearings-only target tracking by single-observer has the advantages of simple layout and strong concealment.However,the filtering methods are sensitive to the initial value,and the actual engineering will encounter the situation of fast target speed,short observation time and less measurement data,which makes this problem significantly prominent.In view of this situation,this paper studies the causes and the effect of initial values on filtering,and presents an improved method of extended Kalman filter based on initial value update.By re-estimating and updating the initial values through backward iteration,the effect of the initial error on the filtering result is gradually reduced.The simulation results show that this method can reduce the dependence of the filter on initial value selection and the final estimation error.