Data association probability and measurement density function of tracking in clutter with strongest neighbor measurements

When tracking a target in clutter, a measurement may have originated from either the target, clutter, or some other source. The measurement with the strongest intensity (amplitude) in the neighborhood of the predicted target measurement is known as the "strongest neighbor" (SN) measurement. A simple and commonly used method for tracking in clutter is the so-called Strongest Neighbor Filter (SNF), which uses the SN measurement at each time as if it were the true one. This paper presents analytic results, along with discussions, for the SN measurement, including the a priori and a posteriori probabilities of data association events and the conditional probability density functions. These results provide theoretical foundation for performance prediction and development of improved tracking filters.