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  • Open Access

    ARTICLE

    Maximum Correntropy Criterion-Based UKF for Loosely Coupling INS and UWB in Indoor Localization

    Yan Wang*, You Lu, Yuqing Zhou, Zhijian Zhao

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2673-2703, 2024, DOI:10.32604/cmes.2023.046743

    Abstract Indoor positioning is a key technology in today’s intelligent environments, and it plays a crucial role in many application areas. This paper proposed an unscented Kalman filter (UKF) based on the maximum correntropy criterion (MCC) instead of the minimum mean square error criterion (MMSE). This innovative approach is applied to the loose coupling of the Inertial Navigation System (INS) and Ultra-Wideband (UWB). By introducing the maximum correntropy criterion, the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise, thus enhancing its adaptability to diverse environmental localization requirements. Particularly in the presence of non-Gaussian noise,… More >

  • Open Access

    ARTICLE

    An Efficient Indoor Localization Based on Deep Attention Learning Model

    Amr Abozeid1,*, Ahmed I. Taloba1,2, Rasha M. Abd El-Aziz1,3, Alhanoof Faiz Alwaghid1, Mostafa Salem3, Ahmed Elhadad1,4

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2637-2650, 2023, DOI:10.32604/csse.2023.037761

    Abstract Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, image-based localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization’s accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This… More >

  • Open Access

    ARTICLE

    Integrating WSN and Laser SLAM for Mobile Robot Indoor Localization

    Gengyu Ge1,2,*, Zhong Qin1, Xin Chen1

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6351-6369, 2023, DOI:10.32604/cmc.2023.035832

    Abstract Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for the following path planning task. In an indoor environment where the global positioning system signal fails or becomes weak, the wireless sensor network (WSN) or simultaneous localization and mapping (SLAM) scheme gradually becomes a research hot spot. WSN method uses received signal strength indicator (RSSI) values to determine the position of the target signal node, however, the orientation of the target node is not clear. Besides, the distance error is large when the indoor signal receives interference. The laser SLAM-based method usually uses… More >

  • Open Access

    ARTICLE

    Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

    Yun Fen Yong1,*, Chee Keong Tan1, Ian Kim Teck Tan2, Su Wei Tan1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1801-1818, 2023, DOI:10.32604/cmc.2023.032710

    Abstract A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations.… More >

  • Open Access

    ARTICLE

    An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

    Edgar Scavino1,*, Mohd Amiruddin Abd Rahman1, Zahid Farid2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 379-397, 2023, DOI:10.32604/cmc.2023.023824

    Abstract Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received… More >

  • Open Access

    ARTICLE

    RSS-Based Indoor Localization System with Single Base Station

    Samir Salem Al-Bawri1,*, Mohammad Tariqul Islam2, Mandeep Jit Singh1,2, Mohd Faizal Jamlos3, Adam Narbudowicz4, Max J. Ammann4, Dominique M. M. P. Schreurs5

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5437-5452, 2022, DOI:10.32604/cmc.2022.020781

    Abstract The paper proposes an Indoor Localization System (ILS) which uses only one fixed Base Station (BS) with simple non-reconfigurable antennas. The proposed algorithm measures Received Signal Strength (RSS) and maps it to the location in the room by estimating signal strength of a direct line of sight (LOS) signal and signal of the first order reflection from the wall. The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office, sampling 21 different locations in the room. It is demonstrated the system can identify user’s real-time location with a maximum estimation error below 0.7 m… More >

  • Open Access

    ARTICLE

    Ensembling Neural Networks for User’s Indoor Localization Using Magnetic Field Data from Smartphones

    Imran Ashraf, Soojung Hur, Yousaf Bin Zikria, Yongwan Park*

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2597-2620, 2021, DOI:10.32604/cmc.2021.016214

    Abstract Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors: Smartphone heterogeneity and smaller data lengths. The use of multifarious smartphones cripples the performance of such approaches owing to the variability of the magnetic field data. In the same vein, smaller lengths of magnetic field data decrease the localization accuracy substantially. The current study proposes the use of multiple neural networks like deep neural network (DNN), long short term memory network (LSTM), and gated recurrent unit network (GRN) to perform indoor localization based on the embedded magnetic sensor of the smartphone. A voting scheme… More >

  • Open Access

    ARTICLE

    MFPL: Multi-Frequency Phase Difference Combination Based Device-Free Localization

    Zengshan Tian1, Weiqin Yang1, Yue Jin1, Liangbo Xie1, *, Zhengwen Huang2

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 861-876, 2020, DOI:10.32604/cmc.2020.07297

    Abstract With the popularity of indoor wireless network, device-free indoor localization has attracted more and more attention. Unlike device-based localization where the target is required to carry an active transmitter, their frequent signal scanning consumes a large amount of energy, which is inconvenient for devices with limited energy. In this work, we propose the MFPL, device-free localization (DFL) system based on WiFi distance measurement. First, we combine multi-subcarrier characteristic of Channel State Information (CSI) with classical Fresnel reflection model to get the linear relationship between the change of the length of reflection path and the subcarrier phase difference. Then we calculate… More >

  • Open Access

    ARTICLE

    Improved GNSS Cooperation Positioning Algorithm for Indoor Localization

    Taoyun Zhou1,2, Baowang Lian1, Siqing Yang2,*, Yi Zhang1, Yangyang Liu1,3

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 225-245, 2018, DOI: 10.3970/cmc.2018.02671

    Abstract For situations such as indoor and underground parking lots in which satellite signals are obstructed, GNSS cooperative positioning can be used to achieve high-precision positioning with the assistance of cooperative nodes. Here we study the cooperative positioning of two static nodes, node 1 is placed on the roof of the building and the satellite observation is ideal, node 2 is placed on the indoor windowsill where the occlusion situation is more serious, we mainly study how to locate node 2 with the assistance of node 1. Firstly, the two cooperative nodes are located with pseudo-range single point positioning, and the… More >

  • Open Access

    ARTICLE

    Localization Algorithm of Indoor Wi-Fi Access Points Based on Signal Strength Relative Relationship and Region Division

    Wenyan Liu1, Xiangyang Luo1,*, Yimin Liu1, Jianqiang Liu2, Minghao Liu1, Yun Q. Shi3

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 71-93, 2018, DOI:10.3970/cmc.2018.055.071

    Abstract Precise localization techniques for indoor Wi-Fi access points (APs) have important application in the security inspection. However, due to the interference of environment factors such as multipath propagation and NLOS (Non-Line-of-Sight), the existing methods for localization indoor Wi-Fi access points based on RSS ranging tend to have lower accuracy as the RSS (Received Signal Strength) is difficult to accurately measure. Therefore, the localization algorithm of indoor Wi-Fi access points based on the signal strength relative relationship and region division is proposed in this paper. The algorithm hierarchically divide the room where the target Wi-Fi AP is located, on the region… More >

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