Neural network optimization for multi-target multi-sensor passive tracking

Abstract

In this paper, we review a number of neural network approaches to combinatorial optimization. We specifically address the difficult problem of localizing multiple targets using only passive sensors, i.e. the sensors detect only bearing angles. Thus, target positions must be found through triangulation. An efficient solution to this problem has been of particular interest in air defence applications. In this paper, we describe two different neural network based approaches for solving this passive tracking problem. In particular, we demonstrate the use of a Hopfield neural network to preface the subsequent development of the multiple elastic modules (MEM) model. The MEM model is presented as a significant extension to current self-organizing neural networks. We describe the unique features of the MEM model, including nonhomogeneous adaptive temperature field for escaping from poor local optima, and locking and expectation features used for dealing with dynamic real-world problems. Applications of the MEM model to other areas including computer vision, are also briefly described.