Near-optimal Sensor Placement for Detecting Stochastic Target Trajectories in Barrier Coverage Systems

TL;DR

This paper proposes a method for near-optimal sensor placement in 2-D barrier coverage systems to detect stochastic target trajectories, using a transformed space approach and numerical experiments with ship data.

Contribution

It introduces a novel approach to sensor placement by transforming the problem space, simplifying trajectory detection for stochastic targets in barrier coverage systems.

Findings

Sensor placements maximize detection probability of passing ships.

The transformed space simplifies handling stochastic line processes.

Numerical experiments validate the effectiveness of the proposed method.

Abstract

This paper addresses the deployment of sensors for a 2-D barrier coverage system. The challenge is to compute near-optimal sensor placements for detecting targets whose trajectories follow a log-Gaussian Cox line process. We explore sensor deployment in a transformed space, where linear target trajectories are represented as points. While this space simplifies handling the line process, the spatial functions representing sensor performance (i.e. probability of detection) become less intuitive. To illustrate our approach, we focus on positioning sensors of the barrier coverage system on the seafloor to detect passing ships. Through numerical experiments using historical ship data, we compute sensor locations that maximize the probability all ship passing over the barrier coverage system are detected.