A decision fusion rule using the total number of detections reported by the local sensors for hypothesis testing and\nthe total number of detections that report ââ?¬Å?1ââ?¬Â to the fusion center (FC) is studied for a wireless sensor network\n(WSN) with distributed sensors. A logistic regression fusion rule (LRFR) is formulated. We propose the logistic\nregression fusion algorithm (LRFA), in which we train the coefficients of the LRFR, and then use the LRFR to make a\nglobal decision about the presence/absence of the target. Both the fixed and variable numbers of decisions\nreceived by the FC are examined. The fusion rule of K out of N and the counting rule are compared with the LRFR.\nThe LRFA does not depend on the signal model and the priori knowledge of the local sensorsââ?¬â?¢ detection\nprobabilities and false alarm rate. The numerical simulations are conducted, and the results show that the LRFR\nimproves the performance of the system with low computational complexity.
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