Fuzzy evidential filter for detection and tracking of dim objects

This paper discusses new analytical and computational aspects of detecting and tracking dim objects (targets). It describes a filter that processes an image from a detector array and updates a function that measures the degree of belief that there is a target present in the individual detectors of the array. The nonlinear filter is derived using an evidential approach based on dynamic programming and fuzzy sets. In numerical experiments with computer simulation, targets whose signals are only one-half the noise level (¸-3db) are detected and tracked. The filter works with multiple targets in two dimensions and can be extended in complexity of target motion, noise, clutter, and geometry. This fuzzy filter can also be viewed as a neural network. The computing load of this filter is very low, depending only on the number of detectors, not the number of tracks. This is important when there are many targets. The estimated degree of belief functions can be utilized by fuzzy controllers.