A Novel Fusion Estimation Method for RSS-AOA-Based Indoor Target Tracking

This article is concerned with the indoor target tracking system based on received signal strength (RSS) and angle of arrival (AOA) combined measurements. Aiming at the nonlinear state estimation of the system and the multisensor fusion problem under the correlation of local estimation error, a high-order modified extended Kalman particle filter (HM-EKPF) with improved proposal distribution is proposed. The effect of truncation errors on the estimation accuracy is reduced by updating and compensating the covariance of state prediction and measurement prediction error. A fusion method based on cross-covariance optimization of local sensors and matrices weighting is proposed. The optimal weighting matrices are determined by constructing the cross-covariance optimal objective function to realize the optimal fusion estimation, which reduces the effect of local estimation correlation on fusion accuracy. An indoor tracking experimental system is established to verify the validity of the proposed method. The experimental results show that the tracking accuracy of the proposed HM-EKPF method is improved by 10.8% and the average tracking error after fusion is reduced by 12.2% compared with the existing methods.