Online multisensor-multitarget detection and tracking

In this paper we present an online approach for joint detection and tracking for multiple targets with multiple sensors using sequential Monte Carlo (SMC) methods. There are two main contributions in the paper. The first contribution is the extension of the deterministic detection method proposed in our previous publications to a full SMC context in which the track initiation and termination are executed using Bayesian Monte Carlo methods. In effect the dimensions of the particles are variable, and the number of targets can be obtained by the MAP estimation of the dimensions of these particles. The second contribution is the tracking of maneuvering targets without using multiple-model approaches. This can be achieved by recursively estimating the heading directions of the targets, followed by the sampling of the target state along these directions. In effect the use of multiple models to model target maneuvers may not be necessary. Furthermore there is no limitation on which the number of targets that can be simultaneously handled by proposed algorithm. With the employment of multiple sensors, a central-level tracking strategy is adopted, where the observations from all active sensors are fused together for detection and tracking and a set of global tracks is maintained. To further save in the increased computational load arising as a result of the multisensor scenario, only those observations from different sensors that are close to each other according to a distance metric are used for data association. To cope with the data association between the observations from all active sensors and the targets at a given time, we adopt an efficient 2-D data assignment algorithm. Computer simulations demonstrate that the proposed approach is robust in performing joint detection and tracking for multiple maneuvering targets even though the environment is hostile with high clutter rate and low target detection probability