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.

翻译：本文提出了一种高效的粒子滤波更新方法，以解决多观测源产生的尖峰状似然函数下的位置估计问题。在全局导航卫星系统（GNSS）中，载波相位的毫米级精度距离观测通常会产生尖峰状似然函数。然而，将此类似然函数用于粒子权重更新时，由于峰内缺乏粒子分布，会导致所有粒子权重归零。针对该问题，本研究引入了一种直接有效的粒子更新方法，适用于多观测源获取的尖峰状似然函数。所提出的方法被称为多重更新粒子滤波，它利用每个似然函数分布范围的先验知识，在粒子更新过程中优先考虑似然函数分布范围，并迭代执行权重更新与重采样。实验结果表明，该方法在利用GNSS伪距与载波相位观测进行位置估计时尤为有效。与常规粒子滤波相比，多重更新粒子滤波能以更少的粒子实现更快的收敛。此外，在城市环境中的车辆位置估计实验表明，该方法优于传统GNSS定位技术，能够提供更精确的位置估计。