Nonlinear estimation fusion in distributed passive sensor networks

The focus of the paper is to present the nonlinear estimation fusion in distributed passive sensor networks which include multiple maneuverable aircrafts with onboard direction finder in each one to execute surveillance over the certain area. The main issue addressed in this research is to construct the hierarchical architecture which consists of passive sensors, local processors, and global processor. The tracking is performed in both Cartesian and modified spherical coordinates (MSC). The state estimate is available from each local processor which processes angle-only measurements using the extend Kalman filter (EKF). In global processor, a weighted least squares (WLS) estimator utilizes the filter covariance matrices which transformed from MSC to reference Cartesian coordinates to compute each filter weight for combining the corresponding local processor outputs. The EKF encounters slow convergence problem under realistic over flight scenarios, where the lateral sightline motion inputs are mild. By using the data fusion technique, the convergence of the WLS estimator is greatly accelerated. Both typical cases target motion analysis and emitter location are investigated through simulations, the results show that the proposed approach compared with the EKF has dramatically improved roughly about averaged 98% and 92% in position and velocity estimations, respectively.