TRACKING IN CLUTTER WITH STRONGEST NEIGHBOR MEASUREMENTS - PART I - THEORETICAL-ANALYSIS

When tracking a target in clutter, a measurement may have originated f rom either the target, clutter, or some other source. The measurement with the strongest intensity (amplitude) in the neighborhood of the pr edicted target measurement is known as the ''strongest neighbor'' (SN) measurement. A simple and commonly used method for tracking in clutte r 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 deals with tracking in clutter with the SN measurements. It presents analyti c results, along with useful comments, for the SN measurement and the SNF, including the a priori and a posteriori probabilities of data ass ociation events, the conditional probability density functions and the covariance matrices of the SN measurement, and various mean-square-er ror matrices of state prediction and state update. These results provi de valuable insight into the problem of tracking in clutter and theore tical foundation for the development of improved tracking algorithms, for performance analysis, prediction, and comparison of tracking with the SN measurements, and for solving some important detection-tracking problems, such as the optimal determination of the detection threshol d and gate size.