Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Mixed Discrete/Continuous SearchConstraint Satisfaction and Optimization: Distributed CSP/Optimization
Near-optimal Sensor Placement for Detecting Stochastic Target Trajectories in Barrier Coverage Systems
🤖 AI Summary
This work addresses the optimal deployment of underwater sensors for two-dimensional barrier coverage, aiming to maximize the detection probability of vessels traversing a surveillance region whose trajectories follow a Log-Gaussian Cox Line Process (LGCLP). To tackle the inherent complexity of optimizing over random line processes, we propose a novel line-to-point transformation framework that maps trajectory distributions into a transformed domain, thereby converting the geometric barrier coverage problem into a tractable and scalable optimization of a probabilistic detection function. Leveraging AIS historical trajectory data, we design a data-driven numerical algorithm to compute near-optimal sensor placements. Experimental evaluation on real maritime datasets demonstrates that our approach achieves a barrier-wide detection probability of 98.3%, significantly outperforming conventional grid-based and greedy deployment strategies.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Optimizing sensor placement for 2-D barrier coverage systems
Detecting stochastic target trajectories using log-Gaussian Cox process
Maximizing detection probability for ship passages via seafloor sensors
Innovation
Methods, ideas, or system contributions that make the work stand out.
Transforms target trajectories into point representation
Maximizes detection probability via optimized sensor placement
Uses log-Gaussian Cox process for stochastic modeling
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