Random Sets-Based Multi-target Tracking with Ambiguous Measurements
Xiaoshu Sun · Journal of Astronautics · 2008
When the number of targets is unknown or varied with time,we represented multi-target state and ambiguous measurements as random sets,used ambiguous likelihood to fuse ambiguous data for tracking multiple targets,and implemented multi-target tracking with probability hypothesis density(PHD) particle filter.Firstly,we used particle filter to predict and update the PHD of Random Sets.Secondly,estimated the number of targets N.Finally,extracted N peaks of PHD.Simulations show that in the same circumstance,our method can robustly tracking multiple targets,performance is better than PHD particle filter with non-fuzzy measurements.