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.

翻译：本研究提出了一种新型高斯混合滤波器（GMF），旨在提升未知环境下自主机器人无线信号源搜索与定位问题的估计性能。首先通过基准数值问题对所提滤波器进行测试，并与粒子高斯混合（PGM）滤波器及粒子滤波器（PF）等当前主流方法进行性能对比验证。随后，在实际机器人野外实验中对该方法与PF及PGM滤波器进行了测试比较，以评估其在真实机器人应用中的效果。所考虑的实际场景存在仅测距观测条件下的部分可观测性以及测量模型的不确定性。结果表明，所提出的滤波器能有效处理此类部分可观测性问题，在计算需求低于PF的同时展现出更优的性能，并且相较于对比技术具有更强的鲁棒性。