2024 03 v.29 12-19
基于IMM-KF的混合场景非视距定位算法
基金项目(Foundation):
国家自然科学基金项目（62101442）
邮箱(Email):
DOI:
10.13682/j.issn.2095-6533.2024.03.002
中文作者单位:
西安邮电大学通信与信息工程学院;陕西省信息通信网络及安全重点实验室;
摘要(Abstract):
为了降低视距(Line of Sight, LOS)和非视距(Non-Line of Sight, NLOS)混合场景下无线定位的误差，提出一种基于交互式多模型-卡尔曼滤波(Interactive Multiple Model-Kalman Filter, IMM-KF)的残差选择NLOS定位算法。构建适用于LOS的多边定位模型和适用于NLOS的残差选择3边定位模型，通过似然概率加权估计两个模型的融合位置。结合卡尔曼滤波进行误差估计确定残差，得到最优的位置估计值，从而降低计算复杂度和由计算不准确导致的模型失配问题。仿真结果表明，所提算法在混合场景中NLOS噪声服从高斯分布、指数分布及均匀分布下，定位精度优于其他对比算法，能有效降低NLOS误差。
关键词(KeyWords):
无线传感器网络;非视距;视距;交互式多模型;卡尔曼滤波;残差选择
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[4] CUI Z,GAO Y,HU J,et al.LOS/NLOS identification for indoor UWB positioning based on Morlet wavelet transform and convolutional neural networks[J].IEEE Communications Letters,2020,25(3):879-882.
[5] 张宝军，田奇，王珩，等.基于CNN和在线学习的UWB室内定位算法[J].传感技术学报，2020,33(4):511-516.ZHANG B J,TIAN Q,WANG H,et al.UWB indoor positioning algorithm based on CNN and online learning[J].Chinese Journal of Sensors and Actuators,2020,33(4):511-516.(in Chinese)
[6] TOMIC S,BEKO M.A bisection-based approach for exact target localization in NLOS environments[J].Signal Processing,2018,143:328-335.
[7] NIU Q,SHI W,XU Y,et al.High-accuracy NLOS identification based on random forest and high-precision positioning on 60 GHz millimeter wave[J].China Communications,2023,20(12):96-110.
[8] 孙伟，孙沛伦.一种基于机器学习分类器的UWB异常信号识别方法[J].测绘科学，2023,48(5):1-8.SUN W,SUN P L.UWB abnormal signal recognition based on machine learning classifier[J].Science of Surreying and Mapping,2023,48(5):1-8.(in Chinese)
[9] JIN D,YIN F,ZOUBIR A M,et al.Exploiting sparsity of ranging biases for NLOS mitigation[J].IEEE Transactions on Signal Processing,2021,69:3782-3795.
[10] 尹烨佳，华惊宇，李枫，等.非视距环境中的无线网络残差加权定位算法[J].传感技术学报，2019,32(5):749-755.YIN Y J,HUA J Y,LI F,et al.Residual weighted based wireless localization algorithmin NLOS environments[J].Chinese Journal of Sensors and Actuators,2019,32(5):749-755.(in Chinese)
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[13] 张云洲，付文艳，项姝，等.室内环境下基于IMM-EKF算法的移动目标定位[J].计算机研究与发展，2014,51(11):2408-2415.ZHANG Y Z,FU W Y,XIANG S,et al.IMM-EKF algorithm based indoor moving target localization[J].Journal of Computer Research and Development,2014,51(11):2408-2415.(in Chinese)
[14] 于晓升，王莹，孟亚男，等.基于IMM-IKF的无线传感器网络非视距节点定位方法[J].控制与决策，2018,33(6):1069-1074.YU X S,WANG Y,MENG Y N,et al.Non-line of sight node localization method based on IMM-IKF for wireless sensor networks[J].Control and Decision,2018,33(6):1069-1074.(in Chinese)
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基本信息:
DOI：10.13682/j.issn.2095-6533.2024.03.002
中图分类号:TN713;TP18
引用信息:
[1]黄庆东,张典,李佳欣等.基于IMM-KF的混合场景非视距定位算法[J].西安邮电大学学报,2024,29(03):12-19.DOI:10.13682/j.issn.2095-6533.2024.03.002.
基金信息:
国家自然科学基金项目（62101442）