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[2602.15893] Statistical-Geometric Degeneracy in UAV Search: A Physics-Aware Asymmetric Filtering Approach
Summary
This article presents a novel approach to UAV search operations in post-disaster scenarios, addressing the challenges posed by Non-Line-of-Sight (NLOS) propagation through the introduction of a physics-aware asymmetric filtering method.
Why It Matters
The research addresses critical issues in UAV search operations, particularly in disaster recovery where traditional methods fail due to asymmetric biases. By proposing a new filtering approach, it enhances the reliability and efficiency of locating survivors, which is vital for timely rescue efforts.
Key Takeaways
- Introduces Statistical-Geometric Degeneracy (SGD) affecting UAV search accuracy.
- Proposes the AsymmetricHuberEKF to handle NLOS biases effectively.
- Demonstrates improved convergence rates in search operations compared to traditional methods.
- Highlights the importance of active sensing strategies in data-scarce environments.
- Validates the approach through practical 2D scanning scenarios.
Computer Science > Robotics arXiv:2602.15893 (cs) [Submitted on 11 Feb 2026] Title:Statistical-Geometric Degeneracy in UAV Search: A Physics-Aware Asymmetric Filtering Approach Authors:Zhiyuan Ren, Yudong Fang, Tao Zhang, Wenchi Cheng, Ben Lan View a PDF of the paper titled Statistical-Geometric Degeneracy in UAV Search: A Physics-Aware Asymmetric Filtering Approach, by Zhiyuan Ren and 4 other authors View PDF HTML (experimental) 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 pr...