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

2025-06-30

AI Summary

This paper addresses anomaly detection in sequential Random Finite Set (RFS) observations, where the goal is to distinguish in-control (normal) from out-of-control (anomalous) states in real time and detect statistical shifts in the underlying data-generating process. Method: We propose an adaptive online monitoring framework that introduces a novel RFS-based Power Discounting posterior distribution family and designs a predictive posterior density function enabling simultaneous, adaptive detection of both systematic drifts and sporadic outliers. The approach integrates RFS modeling, Bayesian sequential learning, and power discounting to enable dynamic adaptation to evolving normal behavior. Contribution/Results: Extensive simulations demonstrate that the method achieves high detection accuracy, strong robustness, and low false alarm rates under challenging conditions—including heavy clutter, time-varying target cardinality, and nonstationary dynamics—thereby advancing the state of the art in RFS-based sequential anomaly detection.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Detects anomalies in sequential random finite set observations
• Adapts dynamically to behavioral shifts in data patterns
• Identifies abnormal point patterns using predictive posterior density

Innovation

• Adaptive anomaly detection for RFS observations
• Online learning of normal data behavior
• Power Discounting Posteriors for dynamic adaptation