A NOVEL DECISION FUSION METHOD BASED ON MULTI-SENSOR BEHAVIOR AND ITS APPLICATION FOR NETWORKED TARGET IDENTIFICATION

Abstract

In this paper, we would focus on submitting a new decision fusion method based on multiple sensors' behaviors applying to target detection and identification in a network of distributed sensors. Each sensor has its own reliability, error rate and output data. Hence, in a processing and decision-making center in which target data are received from different sensors and sources, correctness and speed of final decision-making depend on data fusion method. The extraction, modeling and weighing of long-time and temporary behavior functions of each data source and using precise and fast decision making/fusion method are the main purpose of this article. After the introduction, we try to consider the data fusion method in decision level, such as voting schemes, rank based method and Bayesian inference. Hence, in a distributed target detection and identification system, we explain the specific and the functional features model of each source using long-time and temporary behavior functions. So we introduce the behavior based method as a new decision fusion method based on long-time and temporary behaviors of local decision makers. Therefore, we will observe that the behavior based method results, which pointed both to the temporal and the long time behaviors of the input decision makings, are very much nearer to reality and its correctness in target identification is much higher than the other methods. Examples are given corresponding to the target detection and identification systems to compare the new method with the other methods are shown that the behavior based method has its own exclusive capability in target detection, identification and producing final decision without ambiguity.