Multi-platform multi-target tracking fusion via covariance intersection: using fuzzy optimised modified Kalman filters with measurement noise covariance estimation - Wren, T.J; Mahmood, A (IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008)
Presented in this paper is a detailed novel approach to tracking multiple moving targets from multiple moving platforms and fusing the individual estimates within platform centric nodes via covariance intersection. The approach presents a method of deconstructing the target model into a nonlinear element and a Kalman filter, modelling the target position and velocity vectors of the targets. The method avoids the increased complexity of using extended Kalman filters. The model state noise covariance is restructured by considering the source of the noise within the simplified imposed model and the measurement noise covariance is estimated from a single coefficient optimized moving average filter. The filter coefficient is optimally determined by the minimization of the variance of the Frobenius norm of the current estimated measurement covariance matrix, via a fuzzy logic feedback structure.