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基于固定平台传感器误差极大似然配准(MLR)算法,针对机动平台存在姿态角系统误差的问题,提出了对机动平台传感器系统误差和目标状态进行批处理离线估计的机动极大似然配准(MLRM)算法。该算法利用所有传感器对目标的量测值,通过把传感器量测向目标状态进行投影、对传感器系统误差和目标状态进行期望最大化迭代以及对目标的状态进行融合估计,最终实现量测、姿态角系统误差和目标状态的有效估计。仿真结果表明,该算法迭代收敛速度快,对系统误差估计精度高,对系统误差可观测性较低的配准环境的适应性强并且对传感器姿态角的相关性不敏感,具有很强的工程实用性。
Based on MLR algorithm of fixed platform sensor, in order to solve the problem of attitude error of maneuvering platform, this paper proposes a maneuvering maximum likelihood (LML) estimation system based on offline estimation of system error and target state of the sensor. Quasi (MLRM) algorithm. The algorithm uses the measured values of all the sensors to the target by projecting the sensor measurements to the target state, iteratively maximizing the sensor system error and the target state, and performing a fusion estimation on the state of the target to finally realize the measurement and attitude Angular System Error and Effective Estimation of Target State. The simulation results show that the proposed algorithm has the advantages of fast iterative convergence, high precision of system error estimation, high adaptability to the registration environment with low observability of systematic errors and insensitivity to the correlation of sensor attitude angle. It has strong engineering Practicality.