Survey of fuzzy logic and neural network technology for multitarget tracking

Adaptive techniques for multi-target tracking have primarily been based on prior assumptions for the target and its background distribution. The statistical distribution theory, on the other hand, demands more complex mathematical modeling, which turns out to be computationally intensive as well. It is hard to deny the role of distribution theory and probabilistic approaches to the Multi-Target Tracking (MTT) particularly within the last two decades. However, despite the strength of statistical techniques and Bayesian approaches, the number of sensor samples for accurate modeling of current highly dynamical targets and their complex maneuvering capabilities require rather unrealistic assumptions about target dynamics. Practical target maneuvers with today's technology can be so short in duration that constant and uniform acceleration models for several samples may easily result in loss of tracks. This means the target can be undetected for many samples while making sharp turns. In recent years, there has been a paradigm shift toward fuzzy logic and neural network techniques. The membership functions of a fuzzy controller and nonlinear mapping capability of a trained neural network have made these two different technologies a viable combined system. The objective of this paper is to conduct a survey in the fuzzy logic technology as applied to target tracking and discuss its relation to neural networks when combined together.