Integrated real-time estimation of clutter density for tracking

The spatial density of false measurements is known as clutter density in signal and data processing of targets. It is unknown in reality and its knowledge has a significant impact on the effective processing of targets. This paper presents a number of theoretically sound estimators for clutter density based on conditional mean, maximum likelihood, least squares and method of moments estimation. 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 tracking filters for performance improvement.