Volume 38, Number 2, April 2020
|Page(s)||427 - 433|
|Published online||17 July 2020|
A Zero-Velocity Update Method for Adaptive Particle Filtering
School of Engineering, Beijing Forestry University, Beijing 100083, China
Aiming at the low precision of Kalman filter in dealing with non-linear and non-Gaussian models and the serious particle degradation in standard particle filter, a zero-velocity correction algorithm of adaptive particle filter is proposed in this paper. In order to improve the efficiency of resampling, the adaptive threshold is combined with particle filter. In the process of resampling, the degradation co-efficient is introduced to judge the degree of particle degradation, and the particles are re-sampled to ensure the diversity of particles. In order to verify the effectiveness and feasibility of the proposed algorithm, a hardware platform based on the inertial measurement unit (IMU) is built, and the state space model of the system is established by using the data collected by IMU, and experiments are carried out. The experimental results show that, compared with Kalman filter and classical particle filter, the positioning accuracy of adaptive particle filter in zero-velocity range is improved by 40.6% and 19.4% respectively. The adaptive particle filter (APF) can correct navigation errors better and improve pedestrian trajectory accuracy.
针对卡尔曼滤波方法处理非线性非高斯模型滤波精度低，以及标准粒子滤波中粒子退化严重的问题，提出一种自适应粒子滤波的零速修正方法。将自适应阈值与粒子滤波结合，从而提高重采样的效率；重采样过程中引入退化系数判断粒子退化程度，对粒子进行二次采样，保证了粒子的多样性。为了验证所提算法的有效性和可行性，搭建了以惯性测量单元IMU(inertial measurement unit)为核心的硬件平台，利用IMU采集的数据建立系统的状态空间模型，并进行实验。结果表明，与卡尔曼滤波方法和经典粒子滤波方法相比，自适应粒子滤波方法在零速区间的定位精度分别提高了40.6%和19.4%。自适应粒子滤波APF(adaptive particle filter)能更好地修正导航误差，提高行人轨迹精度。
Key words: Kalman filter / adaptive threshold / secondary sampling / particle filter / experiment / zero-velocity correction / algorithm
关键字 : 卡尔曼滤波 / 自适应阈值 / 二次采样 / 粒子滤波 / 实验 / 零速修正 / 算法
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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