Hypothesis testing for target detection model in sensor networks

Recently target detection is widely regarded as a typical hot spot in research of sensor networks. A fast target detection algorithm is proposed by using the hypothesis testing (HT) method in the paper. The objective is to determine whether a target is present in a sensor network for decision-makers. Due to the nature of sensor networks, it is desirable to have a fast algorithm to accomplish the detection and judgment process with low computation cost on a distributed network end node. In the paper mobile target detection is formulated as a statistical inference problem according to the mathematical statistics theory. Moreover, a data fusion process with several sensors is also designed to optimize the final decision result for detection synthesis. It has the advantage of low computational complexity, good performance of real time, and yields high target detection correctness. Numerical experiments are used to demonstrate the efficiency of the HT detection algorithm, where magnetic sensors are applied to collect the output signal from an undetermined target.