A Novel RSSI Fingerprint Positioning Method Based on Virtual AP and Convolutional Neural Network

Wu, Shixun; Huang, Wenxian; Li, Min; Xu, Kai

doi:10.1109/jsen.2022.3153362

Cited by 15 publications

(2 citation statements)

References 33 publications

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“…. In [63], the virtual ratio is calculated using the principle of trilateral positioning, on the basis of the RSSI path loss function, narrows the positioning range to set the virtual AP position, and adaptively modifies the inputs of the CNN model in order to improve the positioning accuracy. In [64], a ranging model based on the BP neural network was proposed.…”

Section: Integration With Image-based Methodsmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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Location-based services are now playing an integral role in the development of emerging industries, such as the Internet of Things, artificial intelligence and smart cities. Although GPS, Beidou and other satellite positioning technologies are becoming more and more mature, they still have certain limitations. In order to meet the needs of high-precision positioning, wireless positioning is proposed as a supplementary technology to satellite positioning, in which the Received Signal Strength Indication (RSSI) is one of the most popular positioning methods. In this paper, the application scenarios, evaluation methods and related localization methods of wireless positioning based on RSSI are studied. Secondly, the relevant optimization methods are analyzed and compared from different angles, and the methods of RSSI data acquisition are described. Finally, the existing problems and future development trends in RSSI positioning methods are expounded, which has certain reference significance for further research on RSSI localization.

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Section: Integration With Image-based Methodsmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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“…However, a large amount of data was required to make the data network converge. Therefore, a method combining virtual AP and neural network is proposed to realize positioning with less data [14]. Nevertheless, accurate positioning requires a lot of computing resources and time.…”

Section: Introductionmentioning

confidence: 99%

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Zhang,

Xu,

Gao

et al. 2024

Meas. Sci. Technol.

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Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

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