An Adaptive converted measurement kalman filtering algorithm for Space-based Early Warning Radar

n order to solve the target tracking problem in the space-based early warning radar system condition, first, this paper establishes a geometrical motion model and a tracking system model of space-based early warning radar; secondly, this paper introduces the idea of cubature and an adjustment coefficient for the covariance matrix, and finally, an adaptive converted measurement Kalman filtering (CMKF) algorithm is put forward. The proposed algorithm transforms the measured polar coordinate data of ENZ coordinate system into ECEF coordinate system by means of Unbiased transformation, and transforms the measurement covariance matrix into ECEF coordinate system by means of cubature, and then use the new information matrix to adjust the covariance matrices Q and R. The proposed algorithm solved the problems of coordinate conversion error and the covariance matrix mismatching in space-based early warning radar. Finally, compared with the existing algorithms, the simulation results of Monte-Carlo experiments proved that the proposed algorithm improves the problem of filtering divergence and reduces the filtering error, and the new algorithm improves the space-based early warning radar tracking performance.