Multiscale tracking for maneuver target using single sensor

The single-scale filtering method only takes advantage of the fact that it fails to make full use of the characteristics of the observed data at different scales, especially when the system fluctuates drastically, the filtering effect decreases seriously. We propose a novel algorithm of multi-scale fusion and estimation for single sensor target tracking in this paper. Utilizing discrete wavelet transform, we reformulate the state equation and observation equation of Kalman filter in a multi-scale form to found a novel multi-scale Kalman filtering model. By making full use of the signal feature on the diffident scales, the algorithm is more effective for tracking maneuver target, especially in a low signal to noise ratio scenario. A set of Monte Carlo simulations are performed, and the results show the efficiency of the algorithm in this paper.