An adaptive Gaussian sum algorithm for radar tracking
W. Tam, Dimitrios Hatzinakos · 2002
In this paper, we propose a new radar tracking algorithm based on the Gaussian sum filter. We have developed a new systematic and efficient way to approximate a non-Gaussian and measurement-dependent function by a weighted sum of Gaussian density functions. We have derived the formula for updating the weights involved in the bank of Kalman-type filters and also suggested a way to alleviate the growing memory problem inherent in the Gaussian sum filter. Our method is compared with the extended Kalman filter (EKF) and the converted measurement Kalman filter (CMKF) and it is shown to be more accurate in term of position and velocity errors.