Abstract
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.
Highlights
Current most distributed detection and data fusion methods adopt probabilistic description for the observations of sensors and apply Bayesian rule to synthesize information
In contrast to the multi-sensor Bayesian centralized detection which needs to collect the detail data from all sensors to make decision, multi-sensor Bayesian distributed detection refers to that each sensor could make local decision to be referred to the fusion center to make final decision
Multi-sensor data fusion refers to synergistic combination of information inferred by sensors for a better decision usually corresponding with a minimized Bayesian risk or less probabilities of error
Summary
Current most distributed detection and data fusion methods adopt probabilistic description for the observations of sensors and apply Bayesian rule to synthesize information. Multi-sensor Bayesian distributed detection and data fusion is an emerging technology, which is applied on a wide range of areas such as automated target recognition, field surveillance and so-on. In contrast to the multi-sensor Bayesian centralized detection which needs to collect the detail data from all sensors to make decision, multi-sensor Bayesian distributed detection refers to that each sensor could make local decision to be referred to the fusion center to make final decision. Multi-sensor data fusion refers to synergistic combination of information inferred by sensors for a better decision usually corresponding with a minimized Bayesian risk or less probabilities of error. There is a challenge for designing likelihood threshold of each sensor because of non-convex optimization when the number of sensors increases over five for parallel architecture of distributed detection and data fusion
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