Improved Approximation of Sensor Network Performance for Seabed Acoustic Sensors

TL;DR

This paper introduces an improved, computationally efficient approximation method for sensor network performance in seabed acoustic sensors, enhancing target detection probability estimates for maritime surveillance.

Contribution

It presents a novel approximation of void probability that better handles target model uncertainty and offers sharper error bounds compared to previous methods.

Findings

Reduced approximation error in void probability estimates

Validated effectiveness using real ship traffic data

Applicable to maritime surveillance scenarios

Abstract

Sensor locations to detect Poisson-distributed targets, such as seabed sensors that detect shipping traffic, can be selected to maximize the so-called void probability, which is the probability of detecting all targets. Because evaluation of void probability is computationally expensive, we propose a new approximation of void probability that can greatly reduce the computational cost of selecting locations for a network of sensors. We build upon prior work that approximates void probability using Jensen's inequality. Our new approach better accommodates uncertainty in the (Poisson) target model and yields a sharper error bound. The proposed method is evaluated using historical ship traffic data from the Hampton Roads Channel, Virginia, demonstrating a reduction in the approximation error compared to the previous approach. The results validate the effectiveness of the improved approximation for maritime surveillance applications.