Sensor selection based on maximum entropy fuzzy clustering for target tracking in large-scale sensor networks

This study proposes a sensor selection approach based on maximum entropy fuzzy clustering to address the target tracking problem in large-scale sensor networks. The authors try to deal with this problem at two levels: (i) sensor-level tracking: data association problem and sensor-level tracking are carried out at the local level, and only the track outputs are transmitted to the fusion centre for data fusion; (ii) global-level fusion: two sensor selection strategies are adopted at the fusion centre, which seek to only choose a subset of reliable sensors for track-to-track fusion and bias registration. In addition, an improved sensor selection approach is proposed for data fusion in both sparse and dense target environments, and a new fuzzy membership reconstruction strategy is introduced for data association in dense target environments. Furthermore, the proposed sensor selection strategy is also effective in the presence of the possible changing sensor biases. Simulation results are given to evaluate the performance of the proposed approaches.