An improved low-altitude measurement error correction algorithm for interactive multimodel particle filter

In response to the significant errors caused by altitude measurement jumps in low altitude radar and the limited filtering accuracy caused by the lack of latest measurement information in traditional interactive multi model (IMM) particle filtering algorithms, the paper improves the interactive multi model particle filtering algorithm according to the mixed Kalman particle filtering and proposes an interactive multi model mixed Kalman particle filtering algorithm. Firstly, this algorithm uses Unscented Kalman Filter (UKF) to generate system state estimation, and then uses EKF to obtain the importance recommendation distribution of particles, fully utilizing the latest measurement information to update the particle state. The experimental results prove that the improved algorithm significantly reduces the height error of low altitude radar compared to traditional data filtering methods. This method can improve the height measurement accuracy of low altitude radar and has high engineering application value in the field of low altitude measurement.