Adaptive sampling using mobile sensor networks
This paper presents an adaptive sparse sampling approach and the corresponding real-time scalar field reconstruction method using mobile sensor networks. Traditionally, the sampling methods collect measurements without considering possible distributions of target signals. A feedback driven algorithm is discussed in this paper, where new measurements are determined based on the analysis of existing observations. The information amount of each potential measurement is evaluated under a sparse domain based on compressive sensing framework given all existing information shared among networked mobile sensors, and the most informative one is selected. The efficiency of this information-driven method falls into the information maximization for each individual measurement. The simulation results show the efficacy and efficiency of this approach, where a scalar field is recovered. © 2012 IEEE.
Proceedings - IEEE International Conference on Robotics and Automation
Adaptive sampling using mobile sensor networks.
Proceedings - IEEE International Conference on Robotics and Automation, 657-662.
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