TRACKING ALGORITHM FOR MMW/IR MULTI-SENSORS FUSION

Abstract

Millimeter wave(MMW)/infrared (IR) sensor is a key technology for composite guidance system of missiles. Aimming to solve the problems that there were linear errors in the algorithm of square-root unscented Kalman filter (SR-UKF) and it was difficult to obtain the importance density function for the algorithm of particle filter(PF), a square-root unscented Kalman particle filter (SR-UK-PF) algorithm with the sequential fusion was presented by combining SR-UKF with PF. The main idea of this algorithm was to calculate the state transition matrix and the error covariance matrix by SR-UKF, and to construct the importance density function by the sequential fusion of particle filter. Thus, the importance density function could integrate the latest observation into system state transition density, and the proposal distribution could be more in line with the distribution of real states. To demonstrate the effectiveness of this model, simulations were carried out based on tracking algorithm for the surface-to-air missile with MMW/IR sensor.The results show that this technique can overcome the flaw that it is hard to get the optimization importance density function in the particle filter, and it can significantly improve the accuracy of state estimation for the system with multi-sensors.