Multiple-target tracking based on compressed sensing in the Internet of Things

An algorithm based on compressed sensing for tracking multiple targets in the Internet of Things (IoT) is proposed in this study. First, we build a sparse representation of the changes in the sampling signal caused by nodes to multiple targets in a monitored area. Second, we observe the sampling of the sensing signals by nodes to mobile targets and reconstruct the sampling subtraction data. Third, we use sampling subtraction, which is extended by a background subtraction technique in video target tracking, to obtain useful tracking data with sampling subtraction data and to locate the mobile targets. Simulation results show that the proposed algorithm recovers the sensing signal with sparse sampling subtraction data, accurately locates multiple targets, significantly reduces network communication traffic, and improves the energy efficiency of the system with the sparse sampling strategy.