A Fuzzy Logic-Based Energy-Adaptive Localization Scheme by Fusing WiFi and PDR

Yan-kan, Yang; Huang, Baoqi; Xu, Zhendong; Yang, Runze

doi:10.1155/2023/9052477

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…PDR, when utilizing smartphone inertial and magnetic sensors, provides a straightforward and cost-effective solution for indoor positioning. The algorithm primarily encompasses three essential steps: step detection, step-length estimation, and heading determination [34]. The PDR positioning principle is illustrated in Figure 1.…”

Section: Pdr Algorithm Principlementioning

confidence: 99%

Improving Indoor Pedestrian Dead Reckoning for Smartphones under Magnetic Interference Using Deep Learning

Zhu,

Yu,

Han

et al. 2023

Sensors

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As micro-electro-mechanical systems (MEMS) technology continues its rapid ascent, a growing array of smart devices are integrating lightweight, compact, and cost-efficient magnetometers and inertial sensors, paving the way for advanced human motion analysis. However, sensors housed within smartphones frequently grapple with the detrimental effects of magnetic interference on heading estimation, resulting in diminished accuracy. To counteract this challenge, this study introduces a method that synergistically employs convolutional neural networks (CNNs) and support vector machines (SVMs) for adept interference detection. Utilizing a CNN, we automatically extract profound features from single-step pedestrian motion data that are then channeled into an SVM for interference detection. Based on these insights, we formulate heading estimation strategies aptly suited for scenarios both devoid of and subjected to magnetic interference. Empirical assessments underscore our method’s prowess, boasting an impressive interference detection accuracy of 99.38%. In indoor environments influenced by such magnetic disturbances, evaluations conducted along square and equilateral triangle trajectories revealed single-step heading absolute error averages of 2.1891° and 1.5805°, with positioning errors averaging 0.7565 m and 0.3856 m, respectively. These results lucidly attest to the robustness of our proposed approach in enhancing indoor pedestrian positioning accuracy in the face of magnetic interferences.

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Section: Pdr Algorithm Principlementioning

confidence: 99%

Improving Indoor Pedestrian Dead Reckoning for Smartphones under Magnetic Interference Using Deep Learning

Zhu,

Yu,

Han

et al. 2023

Sensors

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“…Pedestrian dead reckoning(PDR), as an inertial navigation technology, does not require the additional deployment of equipment, and can be located even when the smartphone signal is blocked. Due to the cumulative errors of PDR, it is usually fused with absolute positioning technologies such as Wi-Fi and geomagnetic field to improve positioning accuracy [6][7] . In order to improve the efficiency of the indoor fusion positioning system, an accurate and efficient step detection function is required.…”

Section: Introductionmentioning

confidence: 99%

A real-time step detection algorithm based on dynamic peak-valley change threshold screening and dual-time threshold constraints

Kong

Wang

Han

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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As indoor inertial navigation, Pedestrian Dead Reckoning (PDR) has attracted much attention because its positioning results are not affected by the signal of smartphones. Due to the accumulated error of PDR, it is mostly used to improve indoor positioning accuracy by combining with absolute positioning technologies such as Wi-Fi and geomagnetism. The accuracy and efficiency of step detection in PDR have an important impact on the fusion positioning system. The existing step detection algorithms and the built-in pedometer of Android smartphones have the phenomenon of the delayed output of step detection results, which affects the efficiency of real-time indoor positioning based on PDR. In this paper, a real-time step detection algorithm based on dynamic peak-valley change threshold screening and dual-time threshold constraints is proposed. First, the true peak and true valley accelerations are screened according to the amplitude of peak-valley change based on the dynamic peak drop threshold and valley rise threshold, to eliminate the influence of noise near peak-valley values. Then, the peak-valley accelerations are further determined according to the dual-time threshold constraints. The accuracy and efficiency of step detection are effectively improved. This paper has completed experiments on the self-developed Android APP, and the experimental results show that the accuracy and efficiency of step detection are better than the pedometer provided by smartphones.

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2D-Localization Based on Tracking Antenna Using Artificial Intelligence Mapping (AIM) Approach

Madany

2023

IEEE Access

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Many recent low-range applications require only two-dimensional localization. It is more dependable and simpler to use in real-time low-power and range applications because it requires significantly fewer calculations than three dimensions. As antennas can function as sensors in this system, they are regarded as crucial components in the development of localization systems. To avoid a relatively difficult object detection and achieve 360° azimuth plane coverage, the antenna must be as small as possible. The purpose of this study is to design an effective algorithm for the 2D-localization of low-range object positioning using the proposed Artificial Intelligence Mapping (AIM) approach. The proposed AIM approach's modelling and simulation of object localization based on a tracking antenna and the proposed Modified Triangulation Method (MTM) are introduced and analyzed. The design and implementation of the object-localization system were completed. To fulfil the specific aim and suggested AIM approach, the practical 2D-Localization Object System (2D-LOS) uses a commercial identification system. Several trace scenarios for the tracking antenna were illustrated, tested, and compared with samples from other methods in the literature to demonstrate the effectiveness of the proposed localization method for different low-range applications. The proposed AIM approach, which is based on real-world data, has a relatively low resolution and a small error difference.

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A Fuzzy Logic-Based Energy-Adaptive Localization Scheme by Fusing WiFi and PDR

Cited by 5 publications

References 38 publications

Improving Indoor Pedestrian Dead Reckoning for Smartphones under Magnetic Interference Using Deep Learning

Improving Indoor Pedestrian Dead Reckoning for Smartphones under Magnetic Interference Using Deep Learning

A real-time step detection algorithm based on dynamic peak-valley change threshold screening and dual-time threshold constraints

2D-Localization Based on Tracking Antenna Using Artificial Intelligence Mapping (AIM) Approach

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