Multi-array Data Fusion Based Direct Position Determination Algorithm

Traditional multi-station localization system needs to estimate the direction of arrival (DOA) to determine the location of the target. In low SNR situation the localization fails because of the large estimation errors. To improve the performance of the localization system in low SNR situation, we investigate the direct position determination problem from passive measurements made with several antenna arrays of base station in the case of an emitting source. In this paper, a multi-array data fusion based direct position determination algorithm (MDF-DPD) is proposed. The output data of each array are first processed with self-correlation and eigenvalue decomposition to get the noise subspace data. The position of the target is then determined by searching the best array response in the monitoring area which has the biggest sum of projections to respective noise subspace using quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results demonstrated that it performed better in low SNR situation than classical approach.