Open Access
 Issue JNWPU Volume 38, Number 2, April 2020 427 - 433 https://doi.org/10.1051/jnwpu/20203820427 17 July 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## 1 行人导航算法的总体结构

 图1导航算法结构框架

## 2 零速检测方法

1) 加速度模值检测

2) 角速度模值检测

3) 加速度方差检测

C1, C2, C3条件同时满足时, 视为零速时刻, 即

## 3 自适应粒子滤波器设计

Step1   初始粒子计算。在初始时刻t=0, 初始粒子x0~p(x0), 根据采样分布p(x0)对其进行采样得到初始粒子集{x0(i)}i=1N, 取粒子数为N=100, 每个粒子的初始权重为1/N。

Step2   重要性采样更新粒子和权重。构造重要性概率密度函数q(xk(i)|yk(i)), 根据公式(5)计算每个粒子xk(i)对应的权重xk(i)

Step3  计算滤波值。根据归一化的粒子集, 可由k-1时刻的状态值得到k时刻的值, 即最优估计, 状态估计为

Step4  粒子重采样。由于粒子在滤波过程中会出现退化现象, 粒子多样性逐渐减少, 解决此问题的方法是对粒子重采样, 从粒子集中舍弃权值较小的粒子, 重新抽取权值较大的粒子并复制, 建立新的粒子集。使粒子集的均值以最大概率(值为1)趋向数学期望。

1) 选取自适应阈值MM满足等式(8)

M达到上限Mb时, M=Ma

2) 保留粒子判断。如果xk(i)>M, 则保留xk(i), 否则xk(i)=xk-1(i)

3) 对粒子退化现象做二次判断

## 4 实验验证

 图2导航系统模块

 图3EKF、PF和APF的速度误差曲线
 图4EKF、PF和APF的位置误差曲线

 图5二维实验轨迹

10组实验定位滤波效果对比 m

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10组实验定位滤波效果对比 m

## All Figures

 图1导航算法结构框架 In the text
 图2导航系统模块 In the text
 图3EKF、PF和APF的速度误差曲线 In the text
 图4EKF、PF和APF的位置误差曲线 In the text
 图5二维实验轨迹 In the text

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