Received Signal Strength Based Indoor Positioning Using a Random Vector Functional Link Network

Cui, Wei; Le, Zhang; Li, Bing; Guo, Jun; Meng, Wei; Wang, Haixia; Xie, Lihua

doi:10.1109/tii.2017.2760915

Cited by 60 publications

(29 citation statements)

References 48 publications

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“…For the comparison with existing methods (UKF, EKF, nonlinear regression (NR), MDS, RVFL) [11], [17], [22], [23], we implemented their algorithms in the same environment of our lab with the best choices of parameters to ensure the fairness of the comparison. The results of moving horizon estimation and particle filter method are from [32], [36], [41].…”

Section: Comparison With Existing Range-based Localization Methodsmentioning

confidence: 99%

“…From the experimental results in the existing range-based localization methods [11], [17], [22], [23], we find that their performance in the altitude direction is generally not as good as other directions. Some possible solutions include: (i) Adding altitude sensors such as Laser beam or Lidar to measure the altitude, but it requires the ground to be even; (ii) Placing anchors on the ceiling, but it may also be difficult for many environments.…”

Section: Introductionmentioning

confidence: 99%

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Graph Optimization Approach to Range-Based Localization

Wang

Nguyen

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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In this paper, we propose a general graph optimization based framework for localization, which can accommodate different types of measurements with varying measurement time intervals. Special emphasis will be on range-based localization. Range and trajectory smoothness constraints are constructed in a position graph, then the robot trajectory over a sliding window is estimated by a graph based optimization algorithm. Moreover, convergence analysis of the algorithm is provided, and the effects of the number of iterations and window size in the optimization on the localization accuracy are analyzed. Extensive experiments on quadcopter under a variety of scenarios verify the effectiveness of the proposed algorithm and demonstrate a much higher localization accuracy than the existing range-based localization methods, especially in the altitude direction.Index Terms-Graph optimization approach, Range-based localization, 2-D and 3-D spaces, Ultra-wide band radio.

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Section: Comparison With Existing Range-based Localization Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Optimization Approach to Range-Based Localization

Wang

Nguyen

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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“…Many indoor localization methods have been proposed in academia as well as in industry. Typical indoor localization methods like Wi-Fi [4][5][6][7][8][9], Bluetooth [10], ultrasound (US) [11], infrared (IR) [12], radio frequency identification (RFID) [13,14], magnetic field (MF) [15], and ultra-wideband (UWB) [16] methods, have been investigated. Indoor positioning systems estimate the location by using different types of measurements, such as angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS).…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, the experimental results showed that the algorithm was more robust to changing environments. Cui et al [8] proposed a random vector functional link network (RVFL) to develop an efficient and robust indoor positioning system. Luo et al [9] proposed a multifloor identification model called MA_LDA to find the floor number and LL_KNN algorithm to obtain the location information of a target on the floor.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Wang

Fan

et al. 2020

Mobile Information Systems

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Along with the advancement of wireless technology, indoor localization technology based on Wi-Fi has received considerable attention from academia and industry. The fingerprint-based method is the mainstream approach for Wi-Fi indoor localization and can be easily implemented without additional hardware. However, signal fluctuations constitute a critical issue pertaining to the extraction of robust features to achieve the required localization performance. This study presents a fingerprint feature extraction method commonly referred to as the Fisher score–stacked sparse autoencoder (Fisher–SSAE) method. Some features with low Fisher scores were eliminated, and the representative features were then extracted by the SSAE. Furthermore, this study establishes a hybrid localization model constructed with the use of the global model and the submodel to avoid significant coordinate localization errors attributed to subregional localization errors. Combined with three accessible fingerprint-based positioning methods, namely, the support vector regression, random forest regression, and the multiplayer perceptron classification, the experimental results demonstrate that the proposed methods improve the localization accuracy and response time compared to other feature extraction methods and the single localization model. Compared with some state-of-the-art methods, the proposed methods have better localization performances when large number of features are used.

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“…Wi-Fi indoor positioning technology can be divided into two categories: range-based and range-free. According to the way of obtaining characteristic parameters, the range-based positioning technology includes positioning based on time of arrival (TOA) [8], positioning based on time difference of arrival (TDOA) [9], positioning based on angle of arrival (AOA) [10], and positioning based on received signal strength indicator (RSSI) [11].…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Indoor Positioning System Based on K-ELM

Hai-xia

Cui

et al. 2019

Journal of Sensors

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Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

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Received Signal Strength Based Indoor Positioning Using a Random Vector Functional Link Network

Cited by 60 publications

References 48 publications

Graph Optimization Approach to Range-Based Localization

Graph Optimization Approach to Range-Based Localization

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Mobile Robot Indoor Positioning System Based on K-ELM

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