Velocity tracking based on interpolated adaptive Doppler filter

Relative velocity obtained through estimated Doppler frequency is often prone to error because of high noise and the target maneuverings. Better performance is achieved by applying adaptive length window functions [1] to the target echo signal. The optimum length is primarily dependent on target dynamics, expressed in terms of its acceleration and signal to noise ratio of the obtained echo. Papic et al [1] gave lookup table to select the window length for different signal-to-noise ratios (SNR) at three fixed accelerations. Other than these target accelerations, approximating the window length leads to error in the velocity estimation to low SNR. In this paper we have used Lagranges interpolation technique to get better window length adaptation for any range of target acceleration. The estimated velocity is then smoothened using Kalman filter and adaptive Kalman filter. Simulated results show that in high noise level scenario, our interpolated adaptive filter gives better estimation in comparison to the existing methods[1].