基于自适应无迹卡尔曼的目标跟踪算法研究

Research on target tracking algorithm based on adaptive untraced Kalman

基于提高装甲车主动防护系统目标跟踪精度和收敛速度、提升目标拦截概率的目的,该文引入了噪声自适应系数对SUKFR(比例对称采样的UKFR)算法进行优化。通过Matlab软件使用Monte Carlo(蒙特卡罗)方法对UKFR(径向速度的无迹卡尔曼)、SUKFR和ASUKFR(自适应系数的SUKFR)算法的滤波性能进行仿真试验,结果表明,采用ASUKFR算法的主动防护系统不仅目标跟踪精度更高、收敛速度更快,而且拦截概率达到了90%以上,为装甲车主动防护目标跟踪领域提供了一种新思路。

Based on the purpose of improving the target tracking accuracy,convergence speed and target intercept probability of the armored vehicle active protection system.The SUKFR(UKFR with proportional symmetric sampling)algorithm is optimized by introducing noise adaptive coefficient.The filtering performance of UKFR(Unscented Kalman with radial velocity),SUKFR and ASUKFR(SUKFR with adaptive coefficient)algorithms was simulated by Monte Carlo method through Matlab software.The results show that the active protection system based on ASUKFR algorithm not only has higher tracking accuracy and faster convergence speed,but also the intercept probability reaches more than 90%.It provides a new idea in the field of target tracking for active protection of armored vehicles.