Target detection with multi-sensor networks: A survey

The decision fusion-based target detection algorithms are reviewed from the technical level for parallel multi-sensor network topologies. The likelihood ratio test method is categorized as determination of the statistics including the local sensor and data fusion center statistics and the thresholds solving including decision thresholds and data fusion center thresholds, and we carry out discussion on this basis. In the aspect of statistics determination with hard decision fusion system, the detection performance of different systems based on different fusion statistics under the ideal channel and the non-ideal channel is summarized and analyzed. The simulation results are also given to illustrate the performance of different fusion statistics. In the aspect of statistics determination with the soft decision fusion system, the performance metric is concluded. Besides, the decision space partition methods of local sensor nodes are compared and analyzed. When comes to the aspect of threshold solving, it can be summarized as approximation, iteration and Monte Carlo simulation. Further, the applications, advantages and disadvantages of these methods are also considered and compared. Finally, further research trends of decision fusion-based target detection are proposed.