Iterative Joint Integrated Probabilistic Data Association

In situations with a significant number of targets in mutual proximity (close to each other), optimal multi target data association approach suffers from the numerical explosion. This severely limits the applicability; i.e. the number of close targets that may be reliably tracked. We propose an iterative implementation of Joint Integrated Probabilistic Data Association (JIPDA). Starting level is Integrated Probabilistic Data Association (IPDA) for single target tracking, and each subsequent level improves the approximation towards JIPDA. The required number of iterations to achieve the performance of JIPDA is finite for tracking finite number of targets. Increasing the number of iterations also increases computational expenses. Thus we provide the possibility of trade off between the performance and the computational resources by adjusting the number of iterations.