Distributed Data Fusion via Hybrid Approach

Abstract

The work of this paper is to present the hybrid architecture for computing the fused estimate from distributed multiple filters tracking a maneuvering target with the weighted least square (WLS) estimator. The architecture consists of information Kalman filters, local processors and global fuser. Each sensor tracker is utilized in the reference Cartesian coordinate system when the radar measures range, bearing and elevation angle in the spherical coordinate system. The hierarchical estimation algorithm is used in each local processor to merge two tracks representing the same target. Due to the block-diagonal covariance matrix, the resulting global fuser, WLS estimator, can be implemented in a parallel structure to facilitate estimation fusion calculation. Simulation results show that the proposed architecture has computational advantages over the conventional information matrix fusion method with similar performance.