多重更新粒子滤波：结合GNSS伪距与载波相位观测值的位置估计
Multiple Update Particle Filter: Position Estimation by Combining GNSS Pseudorange and Carrier Phase Observations
AI总结：
针对GNSS载波相位尖峰似然导致粒子权重归零的问题，提出按似然展布迭代加权与重采样的多重更新PF，提升车辆定位精度与收敛速度。
摘要：
本文提出了一种在粒子滤波器（PF）中更新粒子的高效方法，以解决在处理由多个观测值产生的尖峰似然函数时的位置估计问题。尖峰似然函数通常源于全球导航卫星系统（GNSS）中载波相位的毫米级精度距离观测值。然而，当此类似然函数用于粒子权重更新时，峰值内缺少粒子会导致所有粒子权重变为零。为克服这一问题，本研究引入了一种直接且有效的方法，用于在处理由多个观测值获得的尖峰似然函数时更新粒子。所提出的方法称为多重更新PF，它利用关于每个似然函数分布展布的先验知识，并在粒子更新过程中按照似然函数展布的优先级迭代执行权重更新和重采样。实验结果证明了本文所提方法的有效性，尤其是在利用GNSS伪距和载波相位观测值进行位置估计时。与传统PF相比，多重更新PF以更少的粒子表现出更快的收敛速度。此外，在城市环境中开展的车辆位置估计实验表明，所提方法优于传统GNSS定位技术，能够产生更准确的位置估计。
英文摘要：
This paper presents an efficient method for updating particles in a particle filter (PF) to address the position estimation problem when dealing with sharp-peaked likelihood functions derived from multiple observations. Sharp-peaked likelihood functions commonly arise from millimeter-accurate distance observations of carrier phases in the global navigation satellite system (GNSS). However, when such likelihood functions are used for particle weight updates, the absence of particles within the peaks leads to all particle weights becoming zero. To overcome this problem, in this study, a straightforward and effective approach is introduced for updating particles when dealing with sharp-peaked likelihood functions obtained from multiple observations. The proposed method, termed as the multiple update PF, leverages prior knowledge regarding the spread of distribution for each likelihood function and conducts weight updates and resampling iteratively in the particle update process, prioritizing the likelihood function spreads. Experimental results demonstrate the efficacy of our proposed method, particularly when applied to position estimation utilizing GNSS pseudorange and carrier phase observations. The multiple update PF exhibits faster convergence with fewer particles when compared to the conventional PF. Moreover, vehicle position estimation experiments conducted in urban environments reveal that the proposed method outperforms conventional GNSS positioning techniques, yielding more accurate position estimates.
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