基于CHAN的改进卡尔曼滤波室内定位算法

doi:10.11959/j.issn.1000-436x.2023006

通信学报 ›› 2023, Vol. 44 ›› Issue (2): 136-147.doi: 10.11959/j.issn.1000-436x.2023006

基于CHAN的改进卡尔曼滤波室内定位算法

蒋锐¹^,², 虞跃¹^,², 徐友云², 王小明¹^,², 李大鹏¹^,²

¹ 南京邮电大学通信与信息工程学院，江苏南京 210003
² 南京邮电大学通信与网络技术国家工程研究中心，江苏南京 210003

修回日期:2022-11-11 出版日期:2023-02-25 发布日期:2023-02-01
作者简介:蒋锐（1985– ），男，江苏南京人，博士，南京邮电大学副教授，主要研究方向为传感器定位、人工智能等
虞跃（1966– ），男，江苏泰州人，南京邮电大学硕士生，主要研究方向为传感器定位
徐友云（1966– ），男，浙江兰溪人，博士，南京邮电大学教授，主要研究方向为传感器定位、移动通信等
王小明（1986– ），男，山东聊城人，博士，南京邮电大学副教授，主要研究方向为传感器定位、无线移动通信、人工智能等
李大鹏（1982– ），男，河南平顶山人，博士，南京邮电大学教授，主要研究方向为传感器定位、移动通信、群体智能通信等
基金资助:
国家自然科学基金资助项目(61971241);国家自然科学基金资助项目(62071245)

Improved Kalman filter indoor positioning algorithm based on CHAN

Rui JIANG¹^,², Yue YU¹^,², Youyun XU², Xiaoming WANG¹^,², Dapeng LI¹^,²

¹ College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
² National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Revised:2022-11-11 Online:2023-02-25 Published:2023-02-01
Supported by:
The National Natural Science Foundation of China(61971241);The National Natural Science Foundation of China(62071245)

摘要/Abstract

摘要：

由于现在室内环境的复杂度越来越高，室内非视距情况下定位误差的影响逐渐增大，如何降低其影响显得尤其重要。定位技术选用的是UWB室内定位技术，其中，卡尔曼滤波在降低室内非视距定位误差方面有着广泛的应用，针对其在视线线路与非视距场景转换过程中会产生新的误差的问题，提出了一种基于CHAN的改进卡尔曼滤波定位算法。采用5个或以上基站的测距结果构建定位解算方程组，选取在非视距情况下定位误差敏感的CHAN算法进行解算，将解算结果与各基站的位置进行残差处理，并划分出不同的置信区域。对于不同置信区域，预先设定匹配的卡尔曼滤波增益系数K，提高视线线路和非视距场景转换的定位结果稳定性，降低转换过程中的定位误差。实验结果表明，在仿真环境中，所提算法可将误差降低至80 cm左右；在实际应用中，可将复杂场景下的定位误差降低至60 cm，比正常复杂室内定位精度提高60%。

关键词: 非视距, CHAN, 残差判别, 卡尔曼滤波

Abstract:

Due to the raising complexity of the indoor environment, the influence of the non-line-of-sight error is gradually increasing in indoor positioning.How to reduce the non-line-of-sight error in the indoor positioning environment is particularly significant.The UWB indoor positioning technology was selected, Kalman filter has been used widely in reducing the positioning error in indoor non-line-of-sight situations.However, in the process of switching between line of sight and non-line-of-sight scenes, the Kalman filter will produce a new error.To solve this problem, an improved Kalman filter indoor positioning algorithm based on CHAN was proposed.The ranging results of five or more base stations were used to construct a positioning solution equation set.Then the CHAN algorithm that was sensitive to positioning errors under non-line-of-sight conditions was selected to calculate the result.Finally, residual processing was performed with the results of each base station and different confidence regions were judged in line with the residual processing.For different confidence regions, the Kalman filter preset gain factor K was used to improve the stability of the positioning result and reduce the positioning error in the conversion process.In the simulation environment, the error can reach about 80 cm.In real scene, the algorithm can reduce the positioning error in non-line-of-sight scenes to 60 cm, which can improve accuracy 60% higher than normal complex indoor positioning.

Key words: non-line-of-sight, CHAN, residual error judgement, Kalman filter

中图分类号:

TN925

蒋锐, 虞跃, 徐友云, 王小明, 李大鹏. 基于CHAN的改进卡尔曼滤波室内定位算法[J]. 通信学报, 2023, 44(2): 136-147.

Rui JIANG, Yue YU, Youyun XU, Xiaoming WANG, Dapeng LI. Improved Kalman filter indoor positioning algorithm based on CHAN[J]. Journal on Communications, 2023, 44(2): 136-147.

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信道模型	μ_NLOS	σ_NLOS
室内住宅	2.693 6	0.448 9
室内办公室	2.075 4	0.178 3

算法	RMSE/cm
传统的CHAN算法	221.553
基于分组的协同定位算法	200.865
CHAN+卡尔曼滤波算法	132.151
CHAN+改进卡尔曼滤波算法	87.799

算法	实际运行时间/s
传统的CHAN算法	0.006 11
基于分组的协同定位算法	0.009 15
CHAN+卡尔曼滤波算法	0.007 08
CHAN+改进卡尔曼滤波算法	0.030 93

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基于CHAN的改进卡尔曼滤波室内定位算法

Improved Kalman filter indoor positioning algorithm based on CHAN

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基于CHAN的改进卡尔曼滤波室内定位算法

Improved Kalman filter indoor positioning algorithm based on CHAN

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