Title :
Mean square errors for tracking in clutter with strongest neighbor measurements
Author_Institution :
New Orleans Univ., LA, USA
Abstract :
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
Keywords :
approximation theory; clutter; covariance matrices; filtering theory; target tracking; clutter; covariance matrices; mean square errors; performance prediction; state prediction; strongest neighbor filter; tracking; Boolean functions; Covariance matrix; Data structures; Filters; Mean square error methods; Performance analysis; Sea measurements; Target tracking; Time measurement; Tin;
Conference_Titel :
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4187-2
DOI :
10.1109/CDC.1997.652324