An algorithm based on logistic regression with data fusion in wireless sensor networks

Longgeng Liu, Guangchun Luo, Ke Qin, Xiping Zhang

Abstract: A decision fusion rule using the total number of detections reported by the local sensors for hypothesis testing and the total number of detections that report “1” to the fusion center (FC) is studied for a wireless sensor network (WSN) with distributed sensors. A logistic regression fusion rule (LRFR) is formulated. We propose the logistic regression fusion algorithm (LRFA), in which we train the coefficients of the LRFR, and then use the LRFR to make a global decision about the presence/absence of the target. Both the fixed and variable numbers of decisions received by the FC are examined. The fusion rule of K out of N and the counting rule are compared with the LRFR. The LRFA does not depend on the signal model and the priori knowledge of the local sensors’ detection probabilities and false alarm rate. The numerical simulations are conducted, and the results show that the LRFR improves the performance of the system with low computational complexity.