Adaptive sampling using mobile robotic sensors

This paper presents an adaptive sparse sampling approach based on mobile robotic sensors. Traditionally, the sampling methods collect measurements without considering possible distributions of target signals. In this paper a feedback driven algorithm is discussed, where new measurements are determined based on the analysis of existing observations under a sparse domain. More specifically, Wavelet structure is considered to optimize measurement projections to substantially reduce the number of measurements based on compressive sensing framework. Sensor motion is designed based on the distribution of optimal measurements, striking a balance between moving cost and measurement value. Simulation results are presented to compare the performance with normal compressive sensing method that uses random measurements and other adaptive sampling methods.