A new filter structure using the multiple model(MM) algorithm based on the extended Kalman filter(EKF) is proposed to estimate the radome slopes and improve the performance of active radar-guided homing missiles.The filter dynamics are built in the three dimensional engagement scenario and the states are composed of relative position,velocity,missile acceleration and target acceleration.The EKF algorithm is adopted to solve the nonlinear measurement function with radome slope interference.The proposed filter algorithm utilizes pseudo-measurements to update the mode probabilities in the MM algorithm based on a series of possible radome slope models.The estimated results of the slopes are introduced into the EKF to get state estimations without radome interference which generates the guidance law command.Simulation results indicate that the MM-EKF algorithm can estimate the radome slopes effectively and improve the accuracy of the guidance system.

针对雷达/红外复合导引头中存在天线罩折射以及外部干扰问题,在三维模型下,提出一种基于扩展卡尔曼滤波(EKF,Extended Kalman Filter)的多模型算法,对天线罩斜率进行估计,并将估计结果代入EKF,降低观测视线角中的天线罩折射干扰,形成最优局部估计.采用基于环境信息的加权因子法对雷达/红外局部估计结果进行融合,通过环境信息度量传感器测量结果的可信度,忽略不可信的局部估计结果.设计4组算例检验融合算法性能,仿真结果表明:所提算法可以准确估计天线罩斜率,合理并有效使用雷达/红外传感器信息,提高系统估计精度.

For homing missile guidance system, a radome compensation method using fuzzy adaptive interacting multiple mode (FAIMM) algorithm for estimating both the radome slope and guidance information is proposed to reduce the influence induced by radome and enhance system performance. The new filtering algorithm includes two levels fuzzy inference systems based on the standard interacting multiple mode (IMM). In the first level the filtering measurement innovations and innovation error covariance are used as the inputs of fuzzy inference to take out the matched degrees for each sub-filter model which are computed intricately in the standard IMM. The radome slope model is adaptively adjusted to reduce the error of estimation using the second level fuzzy inference. Simulation results indicate that FAIMM can estimate the radome slope availably and improve the guidance system performance. Compared with IMM algorithm, the new aglorithm can get more accurate results with the same amount of sub-models.

For homing missile guidance system in three dimensional engagement scenarios, a new filter structure using fuzzy adaptive multiple model (FAMM) algorithm, based on extended Kalman filter (EKF), for estimating both the radome slope and guidance information is proposed to reduce the influence induced by radome and enhance the system performance. The radome slopes are modeled as a series of possible configurations which the multiple model algorithm is used to get the model match degree based on. The new filtering algorithm includes a fuzzy inference system which adjusted the radome slope model on line to reduce the error of estimation. Simulation results indicate that the proposed filter structure can estimate the radome slope availably and improve the guidance system performance.