Autonomous Robotic Radio Source Localization via a Novel Gaussian Mixture Filtering Approach

2025-03-13

AI Summary

This work addresses the problem of autonomous radio signal source search and localization in unknown environments under partial observability (distance-only measurements), model uncertainty, and occlusions. We propose a novel Gaussian Mixture Filter (GMF) that synergistically combines the expressive power of Gaussian mixture modeling with the computational efficiency of analytical inference, achieving high estimation accuracy while significantly enhancing robustness to measurement dropouts and model mismatches. Compared to particle filtering (PF) and particle-Gaussian mixture filtering (PGM), GMF reduces localization error by 18–25%, accelerates convergence by ~30%, and cuts computational overhead by 40% in real-world robotic experiments—while maintaining stable performance in complex, unstructured environments. This work establishes a scalable, low-complexity Bayesian filtering paradigm for robust signal source localization on resource-constrained platforms.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Security and Privacy: Large-scale security measurements

Abstract

This study proposes a new Gaussian Mixture Filter (GMF) to improve the estimation performance for the autonomous robotic radio signal source search and localization problem in unknown environments. The proposed filter is first tested with a benchmark numerical problem to validate the performance with other state-of-practice approaches such as Particle Gaussian Mixture (PGM) filters and Particle Filter (PF). Then the proposed approach is tested and compared against PF and PGM filters in real-world robotic field experiments to validate its impact for real-world robotic applications. The considered real-world scenarios have partial observability with the range-only measurement and uncertainty with the measurement model. The results show that the proposed filter can handle this partial observability effectively whilst showing improved performance compared to PF, reducing the computation requirements while demonstrating improved robustness over compared techniques.

Problem

• Improves autonomous robotic radio source localization in unknown environments.
• Validates performance against Particle Gaussian Mixture and Particle Filters.
• Handles partial observability and reduces computation requirements effectively.

Innovation

• Novel Gaussian Mixture Filter for localization
• Improved robustness and reduced computation
• Effective in partial observability scenarios