A Fuzzy Logic Approach to Target Tracking

TL;DR

This paper introduces a fuzzy logic-based nonlinear filter for target tracking that does not rely on explicit dynamic models, demonstrating superior performance over Kalman filters through empirical simulations.

Contribution

It presents a novel fuzzy logic approach to target tracking that outperforms traditional Kalman filters without requiring explicit dynamic models.

Findings

Fuzzy filter outperforms Kalman filter in target tracking tasks.

Empirical results show improved accuracy of the fuzzy approach.

Simulations suggest potential for theoretical validation.

Abstract

This paper discusses a target tracking problem in which no dynamic mathematical model is explicitly assumed. A nonlinear filter based on the fuzzy If-then rules is developed. A comparison with a Kalman filter is made, and empirical results show that the performance of the fuzzy filter is better. Intensive simulations suggest that theoretical justification of the empirical results is possible.