2025/10/06 by Mingyu Kim, Kim, Mingyu, Pronoy Sarker +7
Computer Science · #Target Tracking and Data Fusion in Sensor Networks #Distributed Sensor Networks and Detection Algorithms #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.2510.05343
This paper studies sensor placement when detection performance varies stochastically due to environmental factors over space and time and false alarms are present, but a filter is used to attenuate the effect. We introduce a unified model that couples detection and false alarms through an availability function, which captures how false alarms reduce effective sensing and filtering responses to the disturbance. Building on this model, we give a sufficient condition under which filtering improves detection. In addition, we derive a coverage-based lower bound on the void probability. Furthermore, we prove robustness guarantees showing that performance remains stable when detection probabilities are learned from limited data. We validate the approach with numerical studies using AIS vessel-traffic data and synthetic maritime scenarios. Together, these results provide theory and practical guidance for deploying sensors in dynamic, uncertain environments.