Improved target tracking with particle filtering

In the literature, there are claims stating that particle filters cannot be used for high dimensional systems because their random measures degenerate to single particles. While this may be true for standard implementations of particle filtering, it may not be true for alternative implementations. In this paper we build on our previous work for tracking multiple targets with multiple particle filters, where each particle filter tracks its own target. We avoid the collapse of traditional particle filtering by considering an interconnected network of such particle filters where each of them works on a relatively low dimensional space. We assume that our interest is in finding the marginal posterior distributions of the state vectors describing the different targets and not in the joint posterior of all the targets. We test the method on the problem of multiple target tracking based on sensor data which represent a superposition of contributions of all the targets in the field. The computer simulations demonstrate the performance of the newly proposed method and compare it with other implementations of particle filtering.