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Tang Z, Li S, Kim KS, Smith JS. Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization. SENSORS (BASEL, SWITZERLAND) 2024; 24:1026. [PMID: 38339745 PMCID: PMC10857661 DOI: 10.3390/s24031026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)-i.e., one of the state-of-the-art multi-building and multi-floor localization models-and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of "by a single building", where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.
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Affiliation(s)
- Zhe Tang
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China; (Z.T.); (S.L.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;
| | - Sihao Li
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China; (Z.T.); (S.L.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;
| | - Kyeong Soo Kim
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China; (Z.T.); (S.L.)
| | - Jeremy S. Smith
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;
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Lee J, Park K, Kim Y. Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22124447. [PMID: 35746229 PMCID: PMC9231099 DOI: 10.3390/s22124447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/28/2022] [Accepted: 06/10/2022] [Indexed: 06/01/2023]
Abstract
Indoor device-free localization (DFL) systems are used in various Internet-of-Things applications based on human behavior recognition. However, the usage of camera-based intuitive DFL approaches is limited in dark environments and disaster situations. Moreover, camera-based DFL schemes exhibit certain privacy issues. Therefore, DFL schemes with radars are increasingly being investigated owing to their efficient functioning in dark environments and their ability to prevent privacy issues. This study proposes a deep learning-based DFL scheme for simultaneous estimation of indoor location and posture using 24-GHz frequency-modulated continuous-wave (FMCW) radars. The proposed scheme uses a parallel 1D convolutional neural network structure with a regression and a classification model for localization and posture estimation, respectively. The two-dimensional location information of the target is estimated for localization, and four different postures, namely standing, sitting, lying, and absence, are estimated simultaneously. We experimentally evaluated the proposed scheme and compared its performance with that of conventional schemes under identical conditions. The results indicate that the average localization error of the proposed scheme is 0.23 m, whereas that of the conventional scheme is approximately 0.65 m. The average posture estimation error of the proposed scheme is approximately 1.7%, whereas that of the conventional correlation, CSP, and SVM schemes are 54.8%, 42%, and 10%, respectively.
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3
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Cui L, Yang Y, Wang Q, Xu F, Guo Q. Fingerprint building and positioning based on wireless sensor networks for underground. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2021; 92:095004. [PMID: 34598498 DOI: 10.1063/5.0056249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 08/15/2021] [Indexed: 06/13/2023]
Abstract
This paper proposes an algorithm of fingerprint constructing and positioning based on wireless sensor networks. The positioning area of underground is divided into several sub-areas according to the neighborhood principle. The reliability mechanism based on the calibration node is established in each sub-area, and the availability of the reference point fingerprint is later determined by the reliability mechanism of the sub-area. When the fingerprint data of the sub-region reference point changes too much, the neighborhood mapping fingerprint model trained by the back propagaption neural network is used to construct the fingerprint. The principle of the neighborhood mapping model is to train the neighborhood relationship between each reference point and its adjacent calibration node in the offline phase to form a network model structure. Then, we use this model to build the fingerprint of reference point in the online stage. In the real-time positioning stage, we use the positioning model based on the adaptive network-based fuzzy inference system. The average positioning error of our proposed algorithm is 3.03 m, when there is a seven day interval between the training dataset and testing dataset, which confirms that the proposed algorithm can be better adapted to the changing environment and to achieve better positioning results.
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Affiliation(s)
- Lizhen Cui
- School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Yong Yang
- School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - QiaoLi Wang
- School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - FanFei Xu
- School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
| | - Qianqian Guo
- School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
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4
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Fingerprinting-Based Indoor Localization Using Interpolated Preprocessed CSI Phases and Bayesian Tracking. SENSORS 2020; 20:s20102854. [PMID: 32443394 PMCID: PMC7287928 DOI: 10.3390/s20102854] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/05/2020] [Accepted: 05/07/2020] [Indexed: 11/16/2022]
Abstract
Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.
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Han X, Xue L, Xu Y, Liu Z. A Radio Environment Maps Estimation Algorithm based on the Pixel Regression Framework for Underlay Cognitive Radio Networks Using Incomplete Training Data. SENSORS 2020; 20:s20082245. [PMID: 32326665 PMCID: PMC7218896 DOI: 10.3390/s20082245] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 03/31/2020] [Accepted: 04/09/2020] [Indexed: 11/16/2022]
Abstract
In the underlay cognitive radio networks, the radio environment maps (REMs) estimation is the main challenge in sensing the idle wireless spectrum resources. Traditional deep learning-based algorithms estimate the REMs on the basis of the high-quality, large-scale complete training images. However, collecting the complete radio environment images is time-consuming and requires a numerous number of power spectrum sensing nodes. For this reason, we propose a generative adversarial networks-based pixel regression framework (PRF) for underlay cognitive radio networks. The PRF algorithm relaxes the requirement of the complete training images, and estimates the radio environment maps only on the basis of the incomplete REMs images, which are easier to be collected. First, we transform the radio environment maps estimation task into a pixel regression task through the color mapping progress. Then, to extract helpful information from the incomplete training data, we design a feature enhancing module for the PRF algorithm, which intelligently learns and emphasizes the important features from the training images. Finally, we use the trained pixel regression framework to reconstruct the radio environment maps in the target area. The proposed algorithm learns accurate radio environment characteristics from the incomplete training data rather than making direct biased or imprecise radio propagation assumptions as in the traditional methods. Thus, the PRF algorithm has a better REMs reconstruction performance than the traditional methods, as verified by simulations.
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Affiliation(s)
| | | | - Ying Xu
- Correspondence: ; Tel.: +86-551-6592-7438
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6
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Ni Y, Chai J, Wang Y, Fang W. A Fast Radio Map Construction Method Merging Self-Adaptive Local Linear Embedding (LLE) and Graph-Based Label Propagation in WLAN Fingerprint Localization Systems. SENSORS 2020; 20:s20030767. [PMID: 32019229 PMCID: PMC7038483 DOI: 10.3390/s20030767] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 01/27/2020] [Accepted: 01/28/2020] [Indexed: 11/16/2022]
Abstract
Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.
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Affiliation(s)
- Yepeng Ni
- School of Data Science and Media Intelligence, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China; (J.C.); (Y.W.)
- Correspondence: ; Tel.: +86-131-4649-7114
| | - Jianping Chai
- School of Data Science and Media Intelligence, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China; (J.C.); (Y.W.)
| | - Yan Wang
- School of Data Science and Media Intelligence, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China; (J.C.); (Y.W.)
| | - Weidong Fang
- Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Micro-System and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China;
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7
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A Power Spectrum Maps Estimation Algorithm Based on Generative Adversarial Networks for Underlay Cognitive Radio Networks. SENSORS 2020; 20:s20010311. [PMID: 31935903 PMCID: PMC6982877 DOI: 10.3390/s20010311] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/26/2019] [Accepted: 12/26/2019] [Indexed: 11/30/2022]
Abstract
In the underlay cognitive radio networks, the main challenge in detecting the idle radio resources is to estimate the power spectrum maps (PSMs), where the radio propagation characteristics are hard to obtain. For this reason, we propose a novel PSMs estimation algorithm based on the generative adversarial networks (GANs). First, we constructed the PSMs estimation model as a regression model in deep learning. Then, we converted the estimation task into an image reconstruction task by image color mapping. We fulfilled the above task by designing an image generator and an image discriminator in the proposed maps’ estimation GANs (MEGANs). The generator is trained to extract the radio propagation characteristics and generate the PSMs images. However, the discriminator is trained to identify the generated images and help to improve the generator’s performance. With the training process of MEGANs, the abilities of the generator and the discriminator are enhanced continually until reaching a balance, which means a high-accuracy PSMs estimation is achieved. The proposed MEGANs algorithm learns and utilizes accurate radio propagation features from the training process rather than making direct imprecise or biased propagation assumptions as in the traditional methods. Simulation results demonstrate that the MEGANs algorithm provides a more accurate estimation performance than the conventional methods.
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8
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Mendoza-Silva GM, Torres-Sospedra J, Huerta J. A Meta-Review of Indoor Positioning Systems. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4507. [PMID: 31627331 PMCID: PMC6832486 DOI: 10.3390/s19204507] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 09/24/2019] [Accepted: 10/14/2019] [Indexed: 11/16/2022]
Abstract
An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys.
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Affiliation(s)
- Germán Martín Mendoza-Silva
- Institute of New Imaging Technologies, Universitat Jaume I, Avda. Vicente Sos Baynat S/N, 12071 Castellón, Spain.
| | - Joaquín Torres-Sospedra
- Institute of New Imaging Technologies, Universitat Jaume I, Avda. Vicente Sos Baynat S/N, 12071 Castellón, Spain.
| | - Joaquín Huerta
- Institute of New Imaging Technologies, Universitat Jaume I, Avda. Vicente Sos Baynat S/N, 12071 Castellón, Spain.
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9
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Bi J, Wang Y, Li Z, Xu S, Zhou J, Sun M, Si M. Fast Radio Map Construction by using Adaptive Path Loss Model Interpolation in Large-Scale Building. SENSORS 2019; 19:s19030712. [PMID: 30744141 PMCID: PMC6387199 DOI: 10.3390/s19030712] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Revised: 02/04/2019] [Accepted: 02/07/2019] [Indexed: 11/17/2022]
Abstract
The radio map construction is usually time-consuming and labor-sensitive in indoor fingerprinting localization. We propose a fast construction method by using an adaptive path loss model interpolation. Received signal strength (RSS) fingerprints are collected at sparse reference points by using multiple smartphones based on crowdsourcing. Then, the path loss model of an access point (AP) can be built with several reference points by the least squares method in a small area. Afterwards, the RSS value can be calculated based on the constructed model and corresponding AP’s location. In the small area, all models of detectable APs can be built. The corresponding RSS values can be estimated at each interpolated point for forming the interpolated fingerprints considering RSS loss, RSS noise and RSS threshold. Through combining all interpolated and sparse reference fingerprints, the radio map of the whole area can be obtained. Experiments are conducted in corridors with a length of 211 m. To evaluate the performance of RSS estimation and positioning accuracy, inverse distance weighted and Kriging interpolation methods are introduced for comparing with the proposed method. Experimental results show that our proposed method can achieve the same positioning accuracy as complete manual radio map even with the interval of 9.6 m, reducing 85% efforts and time of construction.
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Affiliation(s)
- Jingxue Bi
- NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China.
- School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
| | - Yunjia Wang
- NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China.
- School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
| | - Zengke Li
- School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
| | - Shenglei Xu
- School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
| | - Jiapeng Zhou
- School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
| | - Meng Sun
- School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
| | - Minghao Si
- School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China.
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10
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Low-Cost Indoor Positioning Application Based on Map Assistance and Mobile Phone Sensors. SENSORS 2018; 18:s18124285. [PMID: 30563137 PMCID: PMC6308848 DOI: 10.3390/s18124285] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 11/26/2018] [Accepted: 12/03/2018] [Indexed: 11/29/2022]
Abstract
Current mainstream navigation and positioning equipment, intended for providing accurate positioning signals, comprise global navigation satellite systems, maps, and geospatial databases. Although global navigation satellite systems have matured and are widespread, they cannot provide effective navigation and positioning services in covered areas or areas lacking strong signals, such as indoor environments. To solve the problem of positioning in environments lacking satellite signals and achieve cost-effective indoor positioning, this study aimed to develop an inexpensive indoor positioning program, in which the positions of users were calculated by pedestrian dead reckoning (PDR) using the built-in accelerometer and gyroscope in a mobile phone. In addition, the corner and linear calibration points were established to correct the positions with the map assistance. Distance, azimuth, and rotation angle detections were conducted for analyzing the indoor positioning results. The results revealed that the closure accuracy of the PDR positioning was enhanced by more than 90% with a root mean square error of 0.6 m after calibration. Ninety-four percent of the corrected PDR positioning results exhibited errors of <1 m, revealing a desk-level positioning accuracy. Accordingly, this study successfully combined mobile phone sensors with map assistance for improving indoor positioning accuracy.
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Peng Y, Niu X, Tang J, Mao D, Qian C. Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning. SENSORS 2018; 18:s18103419. [PMID: 30322016 PMCID: PMC6210244 DOI: 10.3390/s18103419] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 10/05/2018] [Accepted: 10/09/2018] [Indexed: 11/30/2022]
Abstract
Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.
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Affiliation(s)
- Yitang Peng
- GNSS Research Center, Wuhan University, No.129, Luoyu Road, Wuhan 430079, China.
| | - Xiaoji Niu
- GNSS Research Center, Wuhan University, No.129, Luoyu Road, Wuhan 430079, China.
| | - Jian Tang
- GNSS Research Center, Wuhan University, No.129, Luoyu Road, Wuhan 430079, China.
| | - Dazhi Mao
- GNSS Research Center, Wuhan University, No.129, Luoyu Road, Wuhan 430079, China.
| | - Chuang Qian
- GNSS Research Center, Wuhan University, No.129, Luoyu Road, Wuhan 430079, China.
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Zhou B, Sun C, Ahn D, Kim Y. A Novel Passive Tracking Scheme Exploiting Geometric and Intercept Theorems. SENSORS 2018; 18:s18030895. [PMID: 29562621 PMCID: PMC5877299 DOI: 10.3390/s18030895] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 03/02/2018] [Accepted: 03/14/2018] [Indexed: 11/16/2022]
Abstract
Passive tracking aims to track targets without assistant devices, that is, device-free targets. Passive tracking based on Radio Frequency (RF) Tomography in wireless sensor networks has recently been addressed as an emerging field. The passive tracking scheme using geometric theorems (GTs) is one of the most popular RF Tomography schemes, because the GT-based method can effectively mitigate the demand for a high density of wireless nodes. In the GT-based tracking scheme, the tracking scenario is considered as a two-dimensional geometric topology and then geometric theorems are applied to estimate crossing points (CPs) of the device-free target on line-of-sight links (LOSLs), which reveal the target's trajectory information in a discrete form. In this paper, we review existing GT-based tracking schemes, and then propose a novel passive tracking scheme by exploiting the Intercept Theorem (IT). To create an IT-based CP estimation scheme available in the noisy non-parallel LOSL situation, we develop the equal-ratio traverse (ERT) method. Finally, we analyze properties of three GT-based tracking algorithms and the performance of these schemes is evaluated experimentally under various trajectories, node densities, and noisy topologies. Analysis of experimental results shows that tracking schemes exploiting geometric theorems can achieve remarkable positioning accuracy even under rather a low density of wireless nodes. Moreover, the proposed IT scheme can provide generally finer tracking accuracy under even lower node density and noisier topologies, in comparison to other schemes.
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Affiliation(s)
- Biao Zhou
- The School of Internet of Things (IoT) Engineering, Jiangnan University, Wuxi 214122, China.
| | - Chao Sun
- Electronic Engineering Department, Kwangwoon University, Seoul 01897, Korea.
| | - Deockhyeon Ahn
- Electronic Engineering Department, Kwangwoon University, Seoul 01897, Korea.
| | - Youngok Kim
- Electronic Engineering Department, Kwangwoon University, Seoul 01897, Korea.
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13
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Reference Device-Assisted Adaptive Location Fingerprinting. SENSORS 2016; 16:s16060802. [PMID: 27258284 PMCID: PMC4934228 DOI: 10.3390/s16060802] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 05/11/2016] [Accepted: 05/16/2016] [Indexed: 11/24/2022]
Abstract
Location fingerprinting suffers in dynamic environments and needs recalibration from time to time to maintain system performance. This paper proposes an adaptive approach for location fingerprinting. Based on real-time received signal strength indicator (RSSI) samples measured by a group of reference devices, the approach applies a modified Universal Kriging (UK) interpolant to estimate adaptive temporal and environmental radio maps. The modified UK can take the spatial distribution characteristics of RSSI into account. In addition, the issue of device heterogeneity caused by multiple reference devices is further addressed. To compensate the measuring differences of heterogeneous reference devices, differential RSSI metric is employed. Extensive experiments were conducted in an indoor field and the results demonstrate that the proposed approach not only adapts to dynamic environments and the situation of changing APs’ positions, but it is also robust toward measuring differences of heterogeneous reference devices.
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14
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Sensors for Indoor Mapping and Navigation. SENSORS 2016; 16:s16050655. [PMID: 27171079 PMCID: PMC4883346 DOI: 10.3390/s16050655] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 05/04/2016] [Accepted: 05/04/2016] [Indexed: 11/26/2022]
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