Statistical-Geometric Degeneracy in UAV Search: A Physics-Aware Asymmetric Filtering Approach

2026-02-11

AI Summary

In post-disaster search and rescue operations, unmanned aerial vehicles (UAVs) suffer from non-negative ranging biases induced by non-line-of-sight (NLOS) propagation, which invalidate conventional symmetric robust estimators and lead to a statistical-geometric degeneracy (SGD) problem. This work formally characterizes the SGD phenomenon for the first time and proposes AsymmetricHuberEKF, a physics-aware asymmetric filtering approach. By designing an asymmetric loss function that incorporates the non-negative prior of NLOS errors, the method reveals that symmetric filters are merely a degenerate special case. Furthermore, a cooperative active scanning strategy is introduced to fulfill bilateral information requirements. Under conditions of limited observation geometry and scarce data, the proposed method significantly accelerates localization convergence, outperforms existing symmetric robust baselines, and enhances the robustness of post-disaster localization.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Post-disaster survivor localization using Unmanned Aerial Vehicles (UAVs) faces a fundamental physical challenge: the prevalence of Non-Line-of-Sight (NLOS) propagation in collapsed structures. Unlike standard Gaussian noise, signal reflection from debris introduces strictly non-negative ranging biases. Existing robust estimators, typically designed with symmetric loss functions (e.g., Huber or Tukey), implicitly rely on the assumption of error symmetry. Consequently, they experience a theoretical mismatch in this regime, leading to a phenomenon we formally identify as Statistical-Geometric Degeneracy (SGD)-a state where the estimator stagnates due to the coupling of persistent asymmetric bias and limited observation geometry. While emerging data-driven approaches offer alternatives, they often struggle with the scarcity of training data and the sim-to-real gap inherent in unstructured disaster zones. In this work, we propose a physically-grounded solution, the AsymmetricHuberEKF, which explicitly incorporates the non-negative physical prior of NLOS biases via a derived asymmetric loss function. Theoretically, we show that standard symmetric filters correspond to a degenerate case of our framework where the physical constraint is relaxed. Furthermore, we demonstrate that resolving SGD requires not just a robust filter, but specific bilateral information, which we achieve through a co-designed active sensing strategy. Validated in a 2D nadir-view scanning scenario, our approach significantly accelerates convergence compared to symmetric baselines, offering a resilient building block for search operations where data is scarce and geometry is constrained.

Problem

• Non-Line-of-Sight (NLOS)
• Statistical-Geometric Degeneracy
• asymmetric bias
• UAV search
• ranging bias

Innovation

• Asymmetric Filtering
• Non-Line-of-Sight (NLOS)
• Statistical-Geometric Degeneracy
• Physics-Aware Estimation
• Active Sensing