WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments

Cao, Hongji; Wang, Yunjia; Bi, Jingxue; Xu, Shenglei; Qi, Hongxia; Si, Minghao; Yao, Guangchun

doi:10.1109/access.2020.3041773

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…In order to deal with the harsh indoor environment, a good return based on Gaussian process was proposed to improve the effect of data processing. e model can determine the ground point position coordinates according to the known access point position [20]. Yang et al proposed a visionbased indoor positioning technology.…”

Section: Related Workmentioning

confidence: 99%

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

Zhang

Wang

et al. 2022

Mathematical Problems in Engineering

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With the development of modern society, the demand for indoor positioning technology is higher and higher. The existing indoor positioning technology is difficult to really solve the problem of accuracy and achieve high-performance indoor positioning system design. Based on iBeacon equipment, this paper proposes a method to optimize received signal strength indication indoor positioning algorithm by using the Gaussian filtering method so as to reduce the adverse impact of multipath fading in indoor environment. In order to further improve the accuracy of indoor positioning algorithm, the stack automatic encoder in the deep neural network algorithm is introduced. Through the deep learning method, the high-dimensional information of the fingerprint database collected by the system can be extracted and the adverse impact of data noise on the database is also reduced. Through the simulation test of the system, it can be seen that the error of received signal strength indication indoor positioning algorithm based on extended Gaussian filter is small. Compared with the traditional iBeacon algorithm, the improved algorithm can achieve better data classification. The maximum error of the whole system is 1.02 M. Comprehensive analysis shows that the proposed indoor positioning system has a certain practical value and can be applied to the indoor positioning needs in a certain range of environment.

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Section: Related Workmentioning

confidence: 99%

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

Zhang

Wang

et al. 2022

Mathematical Problems in Engineering

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“…The purpose of the online stage is to estimate the position by real-time or post-process. Algorithms for location estimation mainly include the EWKNN algorithm [8], GPR [42,43], artificial neural network (ANN) [41,44], probabilistic algorithm [45,46], SVM [47], rank [48,49], etc.…”

Section: Related Workmentioning

confidence: 99%

DBSCAN and TD Integrated Wi-Fi Positioning Algorithm

Cao

Wang

et al. 2022

Remote Sensing

Self Cite

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A density-based spatial clustering of applications with noise (DBSCAN) and three distances (TD) integrated Wi-Fi positioning algorithm was proposed, aiming to enhance the positioning accuracy and stability of fingerprinting by the dynamic selection of signal-domain distance to obtain reliable nearest reference points (RPs). Two stages were included in this algorithm. One was the offline stage, where the offline fingerprint database was constructed and the other was the online positioning stage. Three distances (Euclidean distance, Manhattan distance, and cosine distance), DBSCAN, and high-resolution distance selection principle were combined to obtain more reliable nearest RPs and optimal signal-domain distance in the online stage. Fused distance, the fusion of position-domain and signal-domain distances, was applied for DBSCAN to generate the clustering results, considering both the spatial structure and signal strength of RPs. Based on the principle that the higher resolution the distance, the more clusters will be obtained, the high-resolution distance was used to compute positioning results. The weighted K-nearest neighbor (WKNN) considering signal-domain distance selection was used to estimate positions. Two scenarios were selected as test areas; a complex-layout room (Scenario A) for post-graduates and a typical large indoor environment (Scenario B) covering 3200 m2. In both Scenarios A and B, compared with support vector machine (SVM), Gaussian process regression (GPR) and rank algorithms, the improvement rates of positioning accuracy and stability of the proposed algorithm were up to 60.44 and 60.93%, respectively. Experimental results show that the proposed algorithm has a better positioning performance in complex and large indoor environments.

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“…The weighted K-nearest neighbor (WKNN) algorithm [12] is often used for positioning estimation, and WKNN [12], modified weighted K-nearest neighbor (M-WKNN) [13], and enhanced weighted K-nearest neighbor (EWKNN) [14] are improved on the Knearest neighbor (KNN) algorithm. Other algorithms applied to positioning estimation include support vector machines [15], Gaussian process regression [16], and neural networks [17].…”

Section: Introductionmentioning

confidence: 99%

RF-KELM indoor positioning algorithm based on WiFi RSS fingerprint

Hou,

Wang

2024

Meas. Sci. Technol.

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WiFi-based fingerprint indoor positioning technology has been widely concerned, but it has been facing the challenge of lack of robustness to signal changes, and the positioning service requires fast and accurate positioning estimation. Therefore, an RF-KELM positioning algorithm with good comprehensive performance is proposed in this paper. Both offline and online phases are included by this algorithm. In the offline phase, the original data of WiFi fingerprint is first transformed into a form more suitable for positioning. Then, AP selection is performed on the fingerprint database containing many useless access points (APs), in which random forest algorithm (RF) which can evaluate the importance of features is used. Finally, the KELM algorithm is trained with the sub-database that have undergone data transformation and AP selection. In the online phase, firstly, the obtained signal is processed, and then the trained KELM is used to predict the position of the data processed signal. In this paper, the performance of the proposed RF-KELM positioning algorithm is thoroughly tested on a publicly available dataset, and the experimental results demonstrate that the proposed algorithm not only has high positioning accuracy and robustness, but also takes only 0.08 s to position online.

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WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments

Cited by 15 publications

References 30 publications

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

Regional Double-Layer, High-Precision Indoor Positioning System Based on iBeacon Network

DBSCAN and TD Integrated Wi-Fi Positioning Algorithm

RF-KELM indoor positioning algorithm based on WiFi RSS fingerprint

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