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Pedestrian localisation in the typical indoor environments

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Abstract

The world is adopting complete wireless infrastructure to cover small size buildings as well as large geographical regions. Indoor Positioning System (IPS) is used to locate stationary as well as non-stationary objects in the wireless domain. The indoor localisation of a pedestrian remains an open problem in the noisy environment. Researchers are trying to develop low-complexity approach without depending on building infrastructure to achieve accurate and reliable results for pedestrian localisation in noisy environment. We are proposed the problem associated with improving localisation scalability and accuracy by considering the noisy environment. Our propose methodology exhibits robustness and portability with respect to the number of experiments in noisy environment with the help of captured signal strength. The propose methodology is easily applied in various indoor environments (i.e. different building designs) to locate the stationary and non-stationary object. The collected multimodal data of non-stationary targets in the noisy environment is required to enhance further. The collected multimodal data is used to explore specific movement of the pedestrian in the noisy environment. We consider numerical methods to make the shape of hot-spot contour for a specific target. Principal Component Analysis (PCA) based data science is used to obtain the predominant components in the collected information and make a compact contour in the noisy environment. The generated model depicts a highly sensitive region in the noisy environment due to the presence of thick walls inside the building and multipath fading. Our novel methodology exhibits efficient localisation of the non-stationary target or precisely identifies the moving object, on the floor of a multi- storey building. Our novel approach enhances the technique to improve surveillance and security for the noisy indoor environment.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, DIT University, Dehradun, 248009, India

    Soumen Kanrar

  2. Department of Computer Science, Vidyasagar University, Midnapore, 721102, India

    Soumen Kanrar

  3. Airtel Center, Phase IV, Sector 18, Gurugram, 122008, India

    Kanika Dawar

  4. GeekyAnts, Bengaluru, Karnataka, 560076, India

    Abhishek Pundir

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  1. Soumen Kanrar
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  2. Kanika Dawar
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  3. Abhishek Pundir
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Kanrar, S., Dawar, K. & Pundir, A. Pedestrian localisation in the typical indoor environments. Multimed Tools Appl 79, 27833–27866 (2020). https://doi.org/10.1007/s11042-020-09291-w

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