Multi-platform multi-target tracking fusion via covariance intersection: using fuzzy optimised modified Kalman filters with measurement noise covariance estimation

T.J. Wren, A. Mahmood
2008 IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications   unpublished
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
more » ... 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.
doi:10.1049/ic:20080071 fatcat:ojfpk3772rdizm7uaqt5cw2b7m