Tracking Filters for Radar Systems with Correlated Measurement Noise

Publication Type:

Journal Article


Information & Security: An International Journal, Volume 2, p.90-101 (1999)


data processing; target tracking; adaptive estimation; linear prediction


An algorithm and computer simulation results for radar data processing are presented in this work. Tracking filter for systems with colored measurement noise is developed. A measurement difference approach and state space partition is used as a decorrelation scheme. The measurement noise is modeled as a first order Autoregressive (AR) process. A new technique for adaptive evaluation of the AR parameters is proposed since in practice they are usually unknown. The realized algorithm, which is appropriate for on-line processing, is incorporated into the Interacting Multiple Model (IMM) estimation algorithm for tracking maneuvering objects. The results from Monte Carlo simulation show that the suggested algorithm provides almost the same tracking accuracy as in the case of exactly known AR parameters and better estimation capabilities compared to the undecorrelated measurement error. The substantial improvement in velocity and acceleration estimation is particularly useful in missile guidance and situation of abrupt changes in acceleration, induced by the pilot.