Cubature Kalman Fusion For Bearings-Only Tracking Networks

In this paper, we study the nonlinear data fusion problem for a kind of Bearings-only tracking network system with correlations among measurement noises. As a result, a decentralized cubature Kalman fusion algorithm is proposed by using an information filtering form (CIF) of cubature Kalman filter (CKF) and a noise de-correlation way. Based on the CIF and an augmented measurement, a centralized cubature Kalman fusion algorithm is firstly established and a problem on computational performance is pointed out. In order to improve computational performance of the centralized fusion method, the noise de-correlation technology is used to obtain a functionally equivalent Bearings-only tracking fusion system without the noise correlations, namely the variance of an augmented measurement noise is diagonalized. Accordingly, the proposed decentralized fusion method based on the CIF can be used to achieve a more effective tracking fusion estimate.