Flow Sensing-Based Underwater Target Detection Using Distributed Mobile Sensors

This paper presents a novel bioinspired underwater target detection method with distributed mobile sensors. Inspired by the fish's lateral line system, distributed mobile sensors are proposed to measure their respective local flow pressure and collectively estimate the parameters (e.g., position and dimension) of underwater targets with dynamic sensor trajectory planning. This paper formulates an optimal trajectory planning problem for a group of mobile sensors using information entropy as the cost function, with an aim to maximize the confidence level in the estimated target parameters. This paper in particular investigates the detection of a circular target in a uniform flow. A Bayesian filter is adopted to assimilate the distributed pressure measurements dynamically to estimate the position and radius of given circular targets. To verify the effectiveness of the proposed method, simulation is conducted with both fixed and moving circular targets in uniform flow. Simulation results are presented and analyzed.