Bearing-Only Multiple Target Tracking with the Sequential PHD Filter for Multi-Sensor Fusion

It is a widely studied problem to locate multiple emitters from passive angle measurements. The traditional multi-sensor passive multi-objective state estimation problem is a data association problem. In mathematics, the representation of data association problems leads to the generalization of the S-dimensional (S-D) assignment problem. Unfortunately, the complexity of solving an S-D assignment problem for S ≥ 3 is a nondeterministic polynomial (NP) hard problem. Multistage Lagrangian relaxation is a practical solution to solve the multidimensional assignment problem. However, its computational complexity rapidly increases with the sensor’s growth. In addition, satisfactory results cannot be achieved in dense clutter. In this chapter, we use the sequential Probability Hypothesis Density (PHD) filter for passive sensors in two different ways to solve the localization problem of multiple emitters. Simulation results verify the effectiveness of the algorithm. Compared with S-D assignment method, the proposed method can achieve better performance and with smaller computational complexity in environment with dense clutter.