Implementation and performance evolution of Kalman Filters for Target Tracking using Bistatic Range and Range Rate Measurements

Abstract

Multisensor target tracking is finding many applications these days, due to its advantages like accurate target tracking and cheaper in cost. Range and range rate measurements from sensor often used for tracking target. In estimating target location in central station, Kalman filter and its extensions (like extended Kalman Filter) are generally preferred, because if we go to the Multilateration process we will get more error even though it may takes less time for calculation. Extended Kalman filter is of two step algorithm prediction and updation. In updating the current state, the Kalman gain or correction factor plays a vital role in convergence of the filter. Kalman gain intern depends upon the initialization of process noise and measurement noise covariance matrices which is called tuning of filter. The process which is going to be estimated is unobservable to the tracker.