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

arXiv: https://arxiv.org/abs/2602.15893 · HTML: https://mlsanity.com/paper/2602.15893/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [SARDO: An Automated Search-and-Rescue Drone-based Solution for Victims Localization](https://mlsanity.com/paper/2003.05819/) (2020)
- [Learning Continuous Control Policies for Information-Theoretic Active Perception](https://mlsanity.com/paper/2209.12427/) (2022)
- [Optimal Sensor Placement for Source Localization: A Unified ADMM Approach](https://mlsanity.com/paper/2109.03639/) (2021)
- [RF Signal Source Search and Localization Using an Autonomous UAV with Predefined Waypoints](https://mlsanity.com/paper/2301.07027/) (2023)
- [Joint Communication and Sensing in OTFS-based UAV Networks](https://mlsanity.com/paper/2311.17742/) (2023)

## Abstract (arXiv)

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.