Modeling and analysis of coverage degree and target detection for autonomous underwater vehicle-based system

In this article, we theoretically investigate the dynamic aspects of the coverage degree and target detection in the underwater environment resulting from the given moving scenarios of the autonomous underwater vehicles (AUVs). With the help of the continuous moving AUVs, the underwater targets, that cannot be detected by the stationary underwater acoustic sensor network, can be detected with an expected probability, which is determined on the basis of the selected moving scenario of the AUVs. We prove that, for an AUV with randomly selected initial starting point and initial direction, the straight trajectory is the optimal route to achieve the maximum coverage and target detection probability. Then, we present a mathematical model to quantitatively analyze the coverage degree in the underwater environment by using AUVs, as well as formulating the target detection probability of both static target detection and mobile target detection cases. Furthermore, by taking the exposure time of the target into account, we mathematically formulate and analyze the impact of the features of an AUV (i.e., sensing range and velocity) and the moving speed of the mobile target to the mobile target detection probability. We carry out intensive simulation experiments to evaluate the proposed mathematical model, and the experimental results further verify the correctness of our theoretical results.

Duke Scholars

Author Peng Sun DKU Faculty