A hybrid indoor localization algorithm based on RSSI and inertial navigation

Yaping ZHU; Weiwei XIA; Yueyue ZHANG; Feng YAN; Xuzhou ZUO; Lianfeng SHEN

doi:10.11959/j.issn.1000-0801.2017284

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A hybrid indoor localization algorithm based on RSSI and inertial navigation

research and development | 更新时间：2024-06-05

- A hybrid indoor localization algorithm based on RSSI and inertial navigation
- Telecommunications Science Vol. 33, Issue 10, Pages: 99-106(2017)
- 作者机构：
  
  1. 东南大学移动通信国家重点实验室，江苏南京 210096
  2. 电子科技大学信息与软件工程学院，四川成都 610054
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2017284
  CLC： TP393
- Published Online：2017-10，
  
  Published：15 October 2017
- 稿件说明：
移动端阅览
Yaping ZHU, Weiwei XIA, Yueyue ZHANG, et al. A hybrid indoor localization algorithm based on RSSI and inertial navigation[J]. Telecommunications science, 2017, 33(10): 99-106.
DOI：

Yaping ZHU, Weiwei XIA, Yueyue ZHANG, et al. A hybrid indoor localization algorithm based on RSSI and inertial navigation[J]. Telecommunications science, 2017, 33(10): 99-106. DOI： 10.11959/j.issn.1000-0801.2017284.

摘要

针对目前对高精度室内定位算法的需求，提出一种基于接收信号强度识别（RSSI）和惯性导航的融合室内定位算法。基于无线传感网中ZigBee节点的RSSI值，采用位置指纹识别算法，对网络中的未知节点进行定位。结合惯性传感单元（IMU）提供的惯性数据，对RSSI定位结果进行融合修正。利用Kalman滤波器，采用状态方程描述待定位节点位置坐标的动态变化规律，从而实现一种以无线传感网络定位为主、IMU为辅的融合定位方法。仿真结果表明，提出的融合定位算法既能改善单独使用RSSI定位受环境干扰较大的问题，又能避免单独使用惯性导航带来的累积误差，极大地提高了定位精度。

Abstract

To cater for the requirements of high-precision indoor localization algorithms

a hybrid indoor localization algorithm based on received signal strength identification (RSSI) and inertial navigation was proposed.This algorithm used fingerprint identification algorithm to localize the agents

based on the RSSI values of ZigBee nodes in wireless sensor network.The algorithm combined the inertial information provided by inertial measurement units (IMU)

to correct the RSSI localization results.This algorithm used Kalman filter and adopted state equations to describe the dynamic change rules of agents’ positions

thus it achieved a hybrid localization algorithm which relied WSN localization first and IMU last.Simulations evaluate that the proposed algorithm can improve the localization performances of algorithms which adopt RSSI localization and inertial navigation individually

and can greatly improve localization accuracy.

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references

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