A Distributed Underwater Target Detection Algorithm Based on Window Statistics

For an underwater sensor network (USN) with randomly deployed sensors, local sensors' time-varying detection performance and shortage of the intrusion target priori model, a distributed target fusion detection algorithm is proposed based on optimal window statistics. This algorithm focuses on the practical passive underwater target detection that only the sensors surrounding the vessel target in a small zone could provide stable local detection results. Thus the detection process is carried out with a moveable virtual window which fuses the binary decisions reported by local sensors inside its coverage with the counting fusion rule. Finally the detection of this particular subarea with the largest number of fusion sensor report “1” is equivalent to that of the point target. Compared with the point target detection problem, the extended area detection is more robust and reliable. The approximate detection probability of the system level is derived analytically．Simulation methods are also employed to compare the application performance between the proposed algorithm and the existing nonparametric voting or counting fusion rules under practical scenarios. Results illustrate that this fusion rule can perform better in system-level detection compared with the existing one, as long as the scanning window size approximatly matches the target radiation signal region.