Detection of a recurrent static target using binary sensor networks

Abstract

The authors address the problem of decentralised detection of a recurrent event produced by a static uncooperative target in a wireless network of spatially distributed sensors. The presence or absence of the target is determined by applying fusion rules on local binary decisions of individual sensors. Optimal fusion rules require complete statistical knowledge about local detection and false alarm probabilities; hence, in practice they are infeasible. Previous works, such as the counting rule test, scan statistics and the generalised likelihood ratio test, have suggested sub-optimal fusion rules based on the total number of detecting sensors. Neither method takes full advantage of the spatial or temporal correlation between adjacent sensors. In practice, the decisions of adjacent sensors are dependent both in space (as the target is static) and in time (as the event reoccurs). Herein, the authors propose an approach that capitalises on the temporal and spatial arrangement of the local decisions. Simulation examples demonstrate that the detection probability of the proposed spatio-temporal rule is significantly higher than that of the common methods, and also achieves a lower probability of false alarm. Moreover, real data results show that the proposed approach is robust to strong environmental noises.