Multitarget detection in heterogeneous radar sensor network with energy constraint

• We propose four algorithms for targets detection.
• We apply optimized energy allocation scheme to targets detection.
• The optimized energy allocation scheme improves the detection performance.
• The two algorithms for multi-targets detection each have their own advantages.

Heterogeneous radar sensor networks (HRSNs) are gaining popularity due to the superior detection performance compared to conventional homogeneous radar sensor networks. In this paper, under the assumption that radar sensors perform differently in target detection and energy management, we propose optimized energy allocation scheme based on different fusion approaches for both single moving target and multiple moving targets. For one target detection situation, two decision fusion algorithms, the optimized energy allocation – likelihood ratio (OEA-LR) and the optimized energy allocation – approximate likelihood ratio (OEA-ALR) are proposed to improve the system detection performance given system energy constraint. In multi-target detecting environment, two decision fusion algorithms, namely likelihood ratio with ML function (LR-ML) and approximate likelihood ratio with ML function (ALR-ML) are also investigated and the optimized energy allocation scheme, the algorithm of likelihood function with the minimum Bayes risk (LF-BR) are also proposed. Performances are compared and analyzed in terms of probability of detection, probability of false alarm, detection probability of multiple hypothesis, number of local RSs, etc. via simulations. The proposed approaches not only optimize the energy allocation in HRSNs, but also offer an appropriate tradeoff between resource consumption and target detection performance.