Bias compensation for target tracking from range based Maximum Likelihood position estimates

This paper investigates bias compensation for improving the performance of target tracking using range or range difference measurements. We obtain the Maximum Likelihood estimate of the target position at the current instant and pass it to the Kalman filter as observation to obtain the target track. The nonlinear relationship between the target position and measurements creates bias that can degrade significantly the tracker performance. This paper shows that we can accurately estimate the bias and subtract it from the Maximum Likelihood estimate before the Kalman filter is applied. Consequently the bias accumulation is effectively prevented and the tracking accuracy is greatly improved.