Doppler Information Optimization through Fusion Algorithms

This paper analyses the velocity estimation of a target, from the Doppler filter using 1) Kalman filter 2) Adaptive Kalman filter 3) Kalman filter with state vector fusion 4) Adaptive Kalman filter with state vector fusion 5) State vector fused adaptive Kalman filter. Simulation through MATLAB gave good response for 4 th and 5 th algorithms under low signal to noise ratio. 2 nd and 3 rd algorithms gave better results in intensive maneuvers. But 1 st algorithm even though it is low cost and faster, fails due to the delay in response. Keywords Adaptive Doppler Kalman filter, state vector fusion, intensive maneuver. 1. INTRODUCTION For tracking the velocity of a target, required Doppler frequency shift of the received echo signal is obtained using short time Fourier transformation (STFT) [4] on a rectangular window function. Then quadratic interpolation is used to find better frequency shift. This estimated velocity is optimized using fusion technique. The integration or fusion of redundant information can reduce overall uncertainty and thus serve to increase the accuracy with which the features are perceived by the system [10]. Multiple sensors providing redundant information can also serve to increase reliability in the case of sensor error or failure [5]. Papic