Gaussian process inference approximation for indoor pedestrian localisation

被引:5
|
作者
Medvesek, J. [1 ]
Symington, A. [1 ]
Trost, A. [2 ]
Hailes, S. [1 ]
机构
[1] UCL, Dept Comp Sci, London, England
[2] Fac Elect Engn, Dept Elect, Ljubljana, Slovenia
关键词
Gaussian processes; approximation theory; interpolation; clutter; indoor radio; pedestrians; radionavigation; graph theory; computational complexity; optimisation; iterative methods; Gaussian process inference approximation; indoor pedestrian localisation; radio propagation; deterministic methods; wireless indoor positioning; spatially correlated measurement error; training samples; GP inference method; pose graph optimisation framework; run-time complexity; optimiser iteration; O(1) bi-cubic interpolation strategy; signal strength; time-of-flight measurements; magnetic strength; inertial strength; single mobile sensor; decimetre precision indoor pedestrian localisation; ENVIRONMENTS;
D O I
10.1049/el.2014.4436
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Clutter has a complex effect on radio propagation, and limits the effectiveness of deterministic methods in wireless indoor positioning. In contrast, a Gaussian process (?) can be used to learn the spatially correlated measurement error directly from training samples, and build a representation from which a position can be inferred. A method of exploiting ?B inference to obtain measurement predictions from within a pose graph optimisation framework is presented. However, ? inference has a run-time complexity of ?(N-3) in the number of training samples N, which precludes it from being called in each optimiser iteration. The novel contributions of this work are a method for building an approximate ? inference map and an ?(1) bi-cubic interpolation strategy for sampling this map during optimisation. Using inertial, magnetic, signal strength and time-of-flight measurements between four anchors and a single mobile sensor, it is shown empirically that the presented approach leads to decimetre precision indoor pedestrian localisation.
引用
收藏
页码:418 / 419
页数:2
相关论文
共 50 条
  • [21] Variational Inference for Sparse Gaussian Process Modulated Hawkes Process
    Zhang, Rui
    Walder, Christian
    Rizoiu, Marian-Andrei
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 6803 - 6810
  • [22] Gaussian process models for sensor-centric robot localisation
    Brooks, Alex
    Makarenko, Alexei
    Upcroft, Ben
    2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), VOLS 1-10, 2006, : 56 - +
  • [23] Hierarchically-partitioned Gaussian Process Approximation
    Lee, Byung-Jun
    Lee, Jongmin
    Kim, Kee-Eung
    ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 54, 2017, 54 : 822 - 831
  • [24] Multiresolution Kernel Approximation for Gaussian Process Regression
    Ding, Yi
    Kondor, Risi
    Eskreis-Winkler, Jonathan
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), 2017, 30
  • [25] GAUSSIAN APPROXIMATION OF SIGNED RANK STATISTICS PROCESS
    PURI, ML
    WU, TJ
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 1985, 11 (03) : 277 - 312
  • [26] Sparse Approximation for Gaussian Process with Derivative Observations
    Yang, Ang
    Li, Cheng
    Rana, Santu
    Gupta, Sunil
    Venkatesh, Svetha
    AI 2018: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, 11320 : 507 - 518
  • [27] DECL: A Circular Inference Method for Indoor Pedestrian Localization Using Phone Inertial Sensors
    Dang, Congwei
    Sezaki, Kaoru
    Iwai, Masayuki
    2014 SEVENTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORKING (ICMU), 2014, : 117 - 122
  • [28] VariFi: Variational Inference for Indoor Pedestrian Localization and Tracking Using IMU and WiFi RSS
    Huang, He
    Yang, Jianfei
    Fang, Xu
    Jiang, Hao
    Xie, Lihua
    IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (10) : 9049 - 9061
  • [29] Efficient Bayesian Inference for a Gaussian Process Density Model
    Donner, Christian
    Opper, Manfred
    UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, 2018, : 53 - 62
  • [30] Statistical inference for α-series process with the inverse Gaussian distribution
    Kara, Mahmut
    Turksen, Ozlem
    Aydogdu, Halil
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2017, 46 (06) : 4938 - 4950