A new perspective on the choice of fuzzy membership functions in multitarget tracking systems

Abstract

The functional paradigm for fuzzy multisenosr-multitarget tracking systems with data fusion consists of fuzzification, fuzzy knowledge-base, fuzzy inference mechanism, and defuzzification. In fuzzy system design, users start with some fuzzy rules, which are chosen heuristically based on their experience, and membership functions, which in many cases are chosen subjectively based on understanding the problem, and they use the developed system to tune these rules and membership functions. In most publications, in the area of track-to-track association in multitarget tracking systems, the fuzzy membership functions are chosen subjectively according to the underlying problem. The most commonly used membership functions are trapezoidal, triangular, piecewise linear, and Gaussian membership functions. They are chosen by the users based on their experiences. Therefore the problem of constructing optimal fuzzy membership functions is not considered in most publications. This paper addresses the critical issue of constructing optimal fuzzy membership functions for given input information in case of track-to-track association in multitarget tracking systems.