【作者】 邱晓波； 周启煌； 窦丽华；

【Author】 QIU Xiao-bo~(1,2),ZHOU Qi-huang~2,DOU Li-hua~1 (1.School of Information Science and Technology,Beijing Institute of Technology,Beijing 100081, China;2.Department of Control Engineering,Academy of Armored Force Engineering,Beijing 100072, China)

【机构】 北京理工大学信息科学技术学院； 装甲兵工程学院控制工程系；

【摘要】 为了精确地跟踪机动目标,建立了参数辨识模型结构,并对其自适应滤波算法进行研究。首先,分析了参数辨识模型对目标各种机动状态的适应能力,然后,采用扩张状态观测器,从含有较强噪声的观测值序列中,直接估计出目标机动的一阶、二阶、三阶导数,进而实现机动目标运动模态和运动模型参数的实时动态辨识。最后,给出了采用参数辨识模型的自适应Kalman滤波算法。仿真结果表明,参数辨识准确,对目标的各种机动状态具有广泛的适应能力,跟踪误差小。基本满足实际应用中对机动目标跟踪的适应能力强、精度高、便于实时实现等要求。更多还原

【Abstract】 In order to track maneuvering targets accurately,an parameter recognition model is established and its adaptive filter algorithm is investigated.First,the adaptive capability of parameter recognition model for tracking maneuvering targets is analysed. Extended states observer is used to direct estimate first-order,second-order and third-order differential coefficient of maneuvering targets from the measured target position information with strong noise.Then the mode and the model parameters of maneuvering target are recognized.Finally,the adaptive Kalman filter which use parameter recognition model is discussed as well.Simulation results indicate that the model parameters are recognized accurately,and the parameter recognition model estimator can be a self-adjusting filter,which makes it natural for tracking maneuvering targets with little error.It can satisfy the requirements of adaptive and real time,as well as higher precision in engineering application.更多还原