用新高斯混合滤波器提升机器人在未知环境中的信号源定位精度与效率。
Autonomous Robotic Radio Source Localization via a Novel Gaussian Mixture Filtering Approach
- 提出新型高斯混合滤波器，融合粒子滤波优势并降低计算负担。
- 在真实机器人实验中，定位误差比传统方法降低18%，计算量减少37%。
- 特别适合传感器观测受限、测量有噪声的复杂现实场景。
本文提出一种新型高斯混合滤波器（GMF），用于提升机器人在未知环境中自主搜索与定位无线电信号源的性能。首先在基准数值问题上验证了该方法相对于粒子滤波（PF）和粒子高斯混合滤波（PGM）的优越性；随后在真实机器人实地实验中，对比了三种方法在仅有距离观测且测量模型存在不确定性条件下的表现。结果表明，所提方法能有效应对部分可观测性问题，在保持定位精度的同时，相比PF显著降低计算开销，并展现出更强的鲁棒性。
原文摘要 · Abstract (English)
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-the-practice approaches such as Particle Filter (PF) and Particle Gaussian Mixture (PGM) filters. 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 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.
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