Outlier Detection of Poisson-Distributed Targets Using a Seabed Sensor Network

TL;DR

This paper introduces a novel framework combining statistical modeling and adaptive sensor placement to detect and classify outliers in maritime target data using seabed sensor networks and LGCPs.

Contribution

It develops a new probabilistic outlier detection method with a second-order approximation and a dynamic sensor placement strategy for maritime environments.

Findings

Improved outlier classification accuracy over mean-only methods

Effective real-time sensor deployment based on outlier intensity

Validated approach with real ship traffic data near Norfolk

Abstract

This paper presents a framework for classifying and detecting spatial commission outliers in maritime environments using seabed acoustic sensor networks and log Gaussian Cox processes (LGCPs). By modeling target arrivals as a mixture of normal and outlier processes, we estimate the probability that a newly observed event is an outlier. We propose a second-order approximation of this probability that incorporates both the mean and variance of the normal intensity function, providing improved classification accuracy compared to mean-only approaches. We analytically show that our method yields a tighter bound to the true probability using Jensen's inequality. To enhance detection, we integrate a real-time, near-optimal sensor placement strategy that dynamically adjusts sensor locations based on the evolving outlier intensity. The proposed framework is validated using real ship traffic data near Norfolk, Virginia, where numerical results demonstrate the effectiveness of our approach in improving both classification performance and outlier detection through sensor deployment.