Underwater multi-sensor Bayesian distributed detection and data fusion

The relationship of decision rule of sensor for each other is relevant to data fusion, so different topological network of sensors usually results in different performance. This paper considers the parallel and sequential topological data fusion in some detail and applies it to detect underwater signal with three sensors which respectively detects the energy, impulse width and frequency. In this paper, the signal detection model is specified for binary hypotheses testing problem. This paper compares the probabilities of error and Bayesian risk under both topologies corresponding to different value of priori probabilities of two hypotheses. Usually, the parallel architecture of detection and fusion with three sensors as specified in this paper needs to solve eleven nonlinear equations to determine the thresholds of three sensors and fusion rules, as to sequential architecture, five nonlinear equations need to be solved. So, this paper attempts to search numerical solutions for the parallel and sequential architecture of distributed detection and data fusion. Finally, this signal detection problem is simulated