Multi-Sensor Distributed Fusion based on cross-location for passive tracking

Abstract To solve the problem of passive multi-sensor tracking in clutter, a distributed fusion algorithm based on trajectory integral distance and pseudo-measurement is proposed. First, a twofold Gaussian mixture model is used to quantize the target measurement signal, and achieve a more accurate estimate of the initial target state. The labelled multi-Bernoulli filter is employed in adjusting the number of tracking targets and getting single-sensor tracks. Subsequently, in the fusion center, a track-to-track association method based on global nearest-neighbour is employed. Trajectory integration distance is utilized to improve association performances. A maximum-likelihood Cross-location track-to-track fusion strategy using pseudo-measurement transformation is proposed to enhance fusion accuracy in passive scenarios. Simulation results verify the performance of the proposed distributed fusion method.