一种基于多普勒频率变化率单站无源定位的改进跟踪滤波算法

IMM-PF算法巨大的计算量影响跟踪的实时性,IMM-UKF由于线性化误差使得精度不高.针对这些问题,本文在基于多普勒频率变化率的单站无源定位问题的基础上提出了一种改进的交互式多模型滤波算法（IMMEK-UKFPF）,利用不同的模型匹配不同类型的滤波器,充分发挥了卡尔曼滤波和无迹卡尔曼滤波以及粒子滤波各自的优点.仿真结果表明该算法减少了跟踪定位所用时间,提高了计算效率,同时具有良好的跟踪性能和较强的鲁棒性.

An improved tracking filter algorithm of single observer passive localization based on Doppler changing rate

Interacting Multiple Model Particle Filter＇s （IMM-PF） heavy computational load influences the real-time of tracking,Interacting Multiple Model Unscented Kalman Filter exists linearization error and low accuracy. In order to solve the problems, an improved interacting multiple model filter （IMMEK-UKF-PF）, which is based on the doppler changing rate positioning method of single observer passive localization, is proposed. The algorithm makes use of different models to match different filters and gives full play to Particle Filter and Unscented Kalman Filter （UKF） and Kalman Filter （KF）. The simulation results demonstrate that the new filtering method decreases the time of positioning and tracking and improves computational efficiency. And meanwhile, the new algorithm has better tracking performance and strong robustness.