Integrated real-time estimation of clutter density for tracking

The spatial density of false measurements is known as clutter density in si gnal rind data processing of targets. It is unknown in practice and its kno wledge has a significant impact on the effective processing of tar get info rmation. This paper presents in the first time a number of theoretically so lid estimators for clutter density based on conditional mean, maximum likel ihood, and method of moments, respectively. They are computationally highly efficient and require no knowledge of the probability distribution of the clutter density. They can be readily incorporated into a variety of tracker s for performance improvement. Simulation verification of the superiority o f the proposed estimators to the previously used heuristic ones is also pro vided.