用海底声学网络检测海洋目标异常，提升定位精度与实时性。
Outlier Detection of Poisson-Distributed Targets Using a Seabed Sensor Network
- 通过混合正态与异常过程建模目标到达，结合均值方差评估异常概率。
- 相比仅用均值的方法，分类准确率显著提升，实测数据验证有效。
- 动态调整传感器位置，适应异常分布变化，适合海上监控场景。
本文提出一种基于海底声学传感器网络和对数高斯柯克斯过程（LGCPs）的框架，用于在海洋环境中识别空间异常事件。通过将目标到达建模为正常过程与异常过程的混合，估计新观测事件为异常的概率。提出一种二阶近似方法，同时考虑正常强度函数的均值与方差，相比仅使用均值的方法提升了分类准确性。利用詹森不等式分析证明该方法可获得更紧的真概率上界。为进一步增强检测能力，引入实时、近最优的传感器部署策略，根据异常强度演化动态调整传感器位置。该框架在弗吉尼亚诺福克附近的实船交通数据上进行验证，数值结果表明，通过优化传感器部署，本方法在分类性能与异常检测效果上均有显著提升。
原文摘要 · Abstract (English)
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.
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