Mean square errors for tracking in clutter with strongest neighbor measurements

A simple method for tracking in clutter is the so-called strongest neighbor filter (SNF), which uses the "strongest neighbor" (SN) measurement (i.e., the one with the strongest amplitude in the neighborhood of the predicted target measurement) at each time as if it were the true one. This paper presents analytic results, along with insightful discussions, for the SN measurement and the SNF, including the covariance matrices of the SN measurement, and various matrix mean square errors of state prediction and state update. These results provide theoretical foundation for performance prediction and development of improved tracking filters.