RRIFLoc: Radio Robust Image Fingerprint Indoor Localization Algorithm Based on Deep Residual Networks

Deng, Shanghui; Zhang, Wenjie; Xu, Li; Yang, Jingmin

doi:10.1109/jsen.2022.3226303

Cited by 9 publications

(5 citation statements)

References 50 publications

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“…It can be seen from Figure 1 that wireless positioning technology has been widely used in the fields of precision advertising push, emergency rescue, emergency evacuation, auxiliary architectural design, intelligent transportation, etc. [13][14][15], to make up for the shortcomings of satellite positioning. As the output data is used by certain devices for further applications, the performance of the devices will be degraded if the positioning accuracy is not high enough.…”

Section: Applicationmentioning

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: Applicationmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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“…In this section, to evaluate the influence of differences in datasets from different months on the robustness of the model in a dynamic environment, we use each month's datasets as a validation of the VITL algorithmand compare it with the conventional machine learning approach and the deep learning approach. We used five conventional machine learning algorithms, SVM [22], KNN [23], RF [24], DT [25], and GNB [26], for comparison, after which they showed the best performance with those in [44] and [45] a baseline neural network comprising two fully connected hidden layers, with 128 and 68 nodes, and five deep learning algorithms CNN [27], C-FNN1, HADNN1 [46] and rrifloc [47] were compared. In figure 5, the DT algorithm shows poor robustness with other conventional machine learning algorithms, such as the GNB algorithm, after the ninth month, although the other conventional machine learning algorithms are slightly better but still start to float more in the ninth month.…”

Section: Algorithmic Comparison Of Vtil and Conventional Machine Lear...mentioning

confidence: 99%

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

Zhou,

Yang,

Deng

et al. 2024

Eng. Res. Express

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With the popularization and application of WiFi technology, precise location in multi-layer indoor environments play an increasingly important role in current public places. However, there are still problems such as noise and multi-path effects caused by the propagation degradation of the Received Signal Strength Indicator (RSSI) in indoor scenarios and the shading of obstacles, which lead to a reduction in indoor location accuracy. In this paper, we propose a Vision Transformer indoor localization (VTIL) algorithm based on RSSI and Vision-Transformer (ViT). First, the RSSI fingerprint is normalized to the maximum and minimum values, and the Principal Component Analysis (PCA) method is used for feature extraction to reduce the influence of noise or unimportant features. Secondly, the RSSI fingerprint library is converted into an RSSI gray image library. Then the RSSI gray image is divided into several small blocks, and the position-coding input is performed in the form of a sequence. The ViT model divides the weight ratio of each block in the RSSI image to alleviate the impact of multi-path effects. According to the experimental results using public datasets, our method reduces the average distance estimation error by 37.26% compared with the currently known indoor fingerprint localization methods.

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“…In the above equation, F is the state transition matrix. (24). We express equation (22) as follows (25).…”

Section: Robust Localization Based On Enhanced Iterative M-estimationmentioning

confidence: 99%

“…In [23], the author uses fingerprint enhancement to apply deep learning to indoor localization. Sung et al Deng et al [24] uses deep residual networks and other networks for indoor positioning. However, mathematical methods can also be used to locate well.…”

Section: Introductionmentioning

confidence: 99%

An Indoor Localization Algorithm of UWB and INS Fusion based on Hypothesis Testing

2024

KSII TIIS

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With the rapid development of information technology, people's demands on precise indoor positioning are increasing. Wireless sensor network, as the most commonly used indoor positioning sensor, performs a vital part for precise indoor positioning. However, in indoor positioning, obstacles and other uncontrollable factors make the localization precision not very accurate. Ultra-wide band (UWB) can achieve high precision centimeter-level positioning capability. Inertial navigation system (INS), which is a totally independent system of guidance, has high positioning accuracy. The combination of UWB and INS can not only decrease the impact of non-line-of-sight (NLOS) on localization, but also solve the accumulated error problem of inertial navigation system. In the paper, a fused UWB and INS positioning method is presented. The UWB data is firstly clustered using the Fuzzy C-means (FCM). And the Z hypothesis testing is proposed to determine whether there is a NLOS distance on a link where a beacon node is located. If there is, then the beacon node is removed, and conversely used to localize the mobile node using Least Squares localization. When the number of remaining beacon nodes is less than three, a robust extended Kalman filter with M-estimation would be utilized for localizing mobile nodes. The UWB is merged with the INS data by using the extended Kalman filter to acquire the final location estimate. Simulation and experimental results indicate that the proposed method has superior localization precision in comparison with the current algorithms.

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RRIFLoc: Radio Robust Image Fingerprint Indoor Localization Algorithm Based on Deep Residual Networks

Cited by 9 publications

References 50 publications

Research Progress of Wireless Positioning Methods Based on RSSI

Research Progress of Wireless Positioning Methods Based on RSSI

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

An Indoor Localization Algorithm of UWB and INS Fusion based on Hypothesis Testing

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