稳健的单站无源目标跟踪算法研究

无源定位与跟踪系统中面临着可观测性弱、初始误差大的问题,因此寻找一种稳健快速的跟踪算法显得尤为关键。本文在对现有跟踪算法进行分析和比较的基础上,提出一种IUKF(Improved Unscented Kalman Filter)算法,它通过对传统的UKF算法进行修正,改善了对状态滤波值和协方差的估计。与现有算法(如EKF,UKF)相比,新算法不仅适应能力强、稳定性高,而且收敛速度快、跟踪误差小,是一种稳健的无源目标跟踪算法,数值仿真和试验结果表明了算法的正确性和有效性。

Research on Robust Algorithm for Single Observer Passive Target Tracking

It is of great importance to develop a robust and fast tracking algorithm in passive localization and tracking system be- cause its inherent disadvantage of weak observability and large initial error.In this paper,a new algorithm named Improved Unscented Kalman Filter(IUKF) is proposed based on the analysis and comparision of conventional nonlinear tracking problem.The algorithm is developed from UKF but has more accurate state and covariance estimate.Compared with usual algorithms used in passive localization such as EKF and UKF,the proposed algorithm is more robust and has shorter convergence time,higher tracking accuracy.The correct- ness as well as validity of the algorithm are also showed through numerical simulation and experiment results.