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

arXiv: https://arxiv.org/abs/2508.13099 · HTML: https://mlsanity.com/paper/2508.13099/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Event Outlier Detection in Continuous Time](https://mlsanity.com/paper/1912.09522/) (2019)
- [Toward optimal placement of spatial sensors](https://mlsanity.com/paper/2307.04634/) (2023)
- [Improved Approximation of Sensor Network Performance for Seabed Acoustic Sensors](https://mlsanity.com/paper/2505.00804/) (2025)
- [Near-optimal Sensor Placement for Detecting Stochastic Target Trajectories in Barrier Coverage Systems](https://mlsanity.com/paper/2505.00825/) (2025)

## Abstract (arXiv)

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.