摘要
在非线性系统中,最常用的是扩展卡尔曼滤波算法(EKF),当目标距离较远时,滤波器由于量测方程非线性的影响,误差较大。以机动目标“当前”统计模型为基础,建立新的机动目标模型,加入多普勒速度测量对补偿线性化误差的跟踪算法(PTLKF)进行改进。最后融入修正的加速度方差自适应算法对机动目标进行跟踪。仿真结果表明:在非线性观测条件下,改进的PTLKF算法和修正的加速度方差自适应算法的融合可以有效地改善跟踪的效果,并且其计算量明显小于强跟踪滤波算法。
Based on the ＂current＂ statistical model of maneuvering target, a new spiral maneuvering target model was built. Doppler velocity measurement was applied to modify the PTLKF algorithm that was used to compensate the linearization errors, and then the modified acceleration variance adaptive algorithm was integrated for maneuvering target tracking. Simulation results indicate that under the nonlinear observation conditions, the fusion of modified PTLKF and modified acceleration variance adaptive algorithm can effectively improve the tracking of the maneuvering target, and its computation cost is much less than that of the strong tracking filtering algorithm.
出处
《电光与控制》
北大核心
2006年第2期3-7,共5页
Electronics Optics & Control
