Simple, High-Performance Fusion Rule for Censored Decisions in Wireless Sensor Networks

Data selection-based summation fusion (DSSF) was developed to overcome the shortcomings of previously developed likelihood ratio tests based on channel statistics (LRT-CS) for the problem of fusing censored binary decisions transmitted over Nakagami fading channels in a wireless sensor network (WSN).The LRT-CS relies on detection probabilities of the local sensors, while the detection probabilities are a pri-ori unknown for uncooperative targets. Also, for Nakagami fading channels, the LRT-CS involves an infinite series, which is cumbersome for real-time application. In contrast, the DSSF only involves data comparisons and additions and does not require the detection probabilities of local sensors. Furthermore, the perform-ance of DSSF is only slightly degraded in comparison with the LRT-CS when the detection probabilities of local sensors are a priori unknown. Therefore, the DSSF should be used in a WSN with limited resources.