飞行器目标跟踪中的改进自适应滤波算法  

Improved Adaptive Filtering Algorithm in Aircraft Target Tracking

作　　者：兰天 王小虎[1] 张志健[1] Lan Tian;Wang Xiaohu;Zhang Zhijian(Beijing Institute of Control&Electronics Technology,Beijing 100038,China)

机构地区：[1]北京控制与电子技术研究所,北京100038

出　　处：《航天控制》2022年第4期26-32,共7页Aerospace Control

摘　　要：针对经典“当前”统计模型预设目标参数无法随运动状态进行实时调整的问题,提出一种基于卡尔曼滤波的改进自适应滤波算法(MAF),通过对机动频率及加速度方差的计算,实现目标运动参数的自适应调整,提高跟踪精度。仿真结果表明,该种新型自适应滤波算法相比固定参数的“当前”统计模型滤波算法具有更高的跟踪精度、更强的稳定性,在高信噪比环境下的位置跟踪精度提高20%,低信噪比环境下的位置跟踪精度提高2倍,具有一定工程实用价值。Aiming at solving the problem that the preset target parameters of classical “current” statistical model can not be adjusted along with the motion state in real time, a modified adaptive filtering algorithm based on kalman filtering(MAF)is presented. By calculating the maneuver frequency and acceleration variance, the target motion parameters can be adjusted adaptively to improve the tracking precision. The simulation results show that the new adaptive filtering algorithm has more accurate tracking precision and stability than the fixed parameter “current” statistical model filtering algorithm. The location tracking precison under high signal-to-noise ratio environment is improved by 20%, and the location precison under low signal-to-noise ratio environment is improved by 2 times, which is of value in practical engineering.

关 键 词：飞行器 机动目标跟踪 “当前”统计模型 机动频率 加速度方差 

分 类 号：TJ765.43[兵器科学与技术—武器系统与运用工程]