# Adaptive Out-of-Control Point Pattern Detection in Sequential Random Finite Set Observations

arXiv: https://arxiv.org/abs/2506.23802 · HTML: https://mlsanity.com/paper/2506.23802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework](https://mlsanity.com/paper/2108.12159/) (2021)
- [Model-Based Multiple Instance Learning](https://mlsanity.com/paper/1703.02155/) (2017)
- [Heterogeneous Multi-sensor Fusion with Random Finite Set Multi-object Densities](https://mlsanity.com/paper/2106.08088/) (2021)
- [A Cooperative Multi-Agent Probabilistic Framework for Search and Track Missions](https://mlsanity.com/paper/2302.10723/) (2023)
- [Probabilistic Search and Track with Multiple Mobile Agents](https://mlsanity.com/paper/2302.00518/) (2023)
- [Control of Large Swarms via Random Finite Set Theory](https://mlsanity.com/paper/1801.07314/) (2018)
- [Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework](https://mlsanity.com/paper/2102.01882/) (2021)
- [Decentralized Search and Track with Multiple Autonomous Agents](https://mlsanity.com/paper/2302.00515/) (2023)
- [Jointly-Optimized Searching and Tracking with Random Finite Sets](https://mlsanity.com/paper/2302.01845/) (2023)
- [Cooperative Simultaneous Tracking and Jamming for Disabling a Rogue Drone](https://mlsanity.com/paper/2302.07575/) (2023)
- [Downing a Rogue Drone with a Team of Aerial Radio Signal Jammers](https://mlsanity.com/paper/2303.09884/) (2023)

## Abstract (arXiv)

In this work we introduce a novel adaptive anomaly detection framework specifically designed for monitoring sequential random finite set (RFS) observations. Our approach effectively distinguishes between In-Control data (normal) and Out-Of-Control data (anomalies) by detecting deviations from the expected statistical behavior of the process. The primary contributions of this study include the development of an innovative RFS-based framework that not only learns the normal behavior of the data-generating process online but also dynamically adapts to behavioral shifts to accurately identify abnormal point patterns. To achieve this, we introduce a new class of RFS-based posterior distributions, named Power Discounting Posteriors (PD), which facilitate adaptation to systematic changes in data while enabling anomaly detection of point pattern data through a novel predictive posterior density function. The effectiveness of the proposed approach is demonstrated by extensive qualitative and quantitative simulation experiments.