基于改进的粒子滤波的静电目标跟踪算法

An electrostatic target tracking algorithm based on improved particle filtering

考虑到静电探测技术具有被动式、反低空、反隐身的优势,提出了使用静电探测器探测和跟踪低空飞行目标的方法,建立了旋转式三维静电探测器探测低空飞行目标的数学模型,并根据该数学模型的非线性特性和基本粒子滤波(PF)算法存在的不足,提出了一种新的基于中心差分的改进粒子滤波算法。该算法利用中心差分扩展卡尔曼滤波算法产生基本粒子滤波的建议分布函数,实现对目标运动状态的更新。理论分析和仿真结果均表明,与基本粒子滤波算法和无迹粒子滤波(UPF)算法相比,改进的粒子滤波算法能够更有效地利用旋转式三维静电探测器获得的数据实现对目标的跟踪,其定位精度较高,计算量更小。

With the consideration of electrostatic detection＇ s advantages of passivity, anti-low-altitude and anti-stealth, a technique for probing and tracking a low-altitude target by using an electrostatic detector was studied, and a mathe- matical model for detecting low-altitude targets based on a rotating three-dimensional electrostatic detector was es- tablished. Then, an improved particle filtering（PF） algorithm based on the centre differential was proposed according to the characteristics of the mathematical model and the analysis of the shortcomings of the PF algorithm. The im- proved PF algorithm uses the centre differential expanded Kalman filtering algorithm to generate a PF-based propos- al distribution function and update the target state. The theory analysis and simulation results show that the improved PF algorithm can use the data to track moving targets effectively with the higher positioning precision and smaller updated time compared with the PF algorithm and the unscented particle filtering algorithm.