Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

Zhang, Liye; Valaee, Shahrokh; Xu, Yong; Ma, Lin; Vedadi, Farhang

doi:10.3390/app7050467

Cited by 18 publications

(9 citation statements)

References 34 publications

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“…To generalize map learning, suppose that we sample a set of n learned trajectories h 1:n = {h (1) , h (2) , · · · , h (n) }, where each h (·) might have different start and end points and each h 1:n is exploited to obtain P TL in Equation (41). We assume that P TL follows the Gaussian distribution given by…”

Section: Trajectory Learning From a Crowdmentioning

confidence: 99%

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

Yoo

Park

2019

Applied Sciences

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This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.Wi-Fi RSSI based indoor localization [1-4] is one of the standard approaches for indoor localization. It is able to utilize the RSSI measurements received from a large number of access points (APs) that are already built in construction. However, the Wi-Fi RSSI as a function of distance between a receiver (smartphone) and a transmitter (wireless AP) is nonlinear and varying due to interference of the indoor environments such as the other radio signals, walls, and obstacles. To address this problem, many machine learning based localization methods [5-8] have been developed, which learn a pattern of the RSSI measurements corresponding locations across the interested positioning area. In addition, due to its unbiased estimation capability, it is likely to be combined with other kinds of localization, such as pedestrian localization using inertial measurement unit (IMU) [9,10], visual localization [11,12], and magnetic sensor-based localization [13,14].In particular, semisupervised learning algorithms have been recently suggested for efficient indoor localization, which reduce the human effort necessary for collecting training data [15][16][17][18][19][20]. For example, for indoor localization, a large amount of unlabeled data can be easily collected by recording only Wi-Fi RSSI measurements without requiring position labels, which can save resources for collection and calibration. By contrast, labeled training data have to be created manually. Adding a large amount of Appl. Sci. 2019, 9, 3930 2 of 21 unlabeled data in the semisupervised learning framework can prevent the decrement in the estimation accuracy that occurs when using only a small amount of labeled data.Given the advantage of semisupervised learning, this study describes (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless loc...

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Section: Trajectory Learning From a Crowdmentioning

confidence: 99%

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

Yoo

Park

2019

Applied Sciences

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“…Since the advent of least square regression, a large number of LSR-based methods have been proposed, such as weighted LSR [1], partial LSR [2], local LSR [3], kernel LSR [4], support vector machine (SVM) [5], non-negative least squares (NNLS) [6,7] and so on. Moreover, a series of methods based on LSR have been successfully and efficiently applied to face recognition, speech recognition, image retrieval, and so on [8][9][10][11][12][13][14]. For example, in order to improve the performance of retargeted least squares regression (ReLSR), Wang et al [8] proposed the groupwise retargeted least squares regression (GReLSR) algorithm, which utilized an additional regularization to restrict the translation values of ReLSR so that similar values are within the same class.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in order to improve the performance of retargeted least squares regression (ReLSR), Wang et al [8] proposed the groupwise retargeted least squares regression (GReLSR) algorithm, which utilized an additional regularization to restrict the translation values of ReLSR so that similar values are within the same class. For the sake of solving the device diversity problem in crowdsourcing system, Zhang et al [9] introduced a linear regression (LR) approach to obtain the uniform received signal strength (RSS) values. In [10], an elastic-net regularized linear regression (ENLR) framework was developed, in which two particular strategies were proposed to enlarge the margins of different classes.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation

Chen

Dai

et al. 2018

Applied Sciences

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In order to overcome the drawbacks of the ridge regression and label propagation algorithms, we propose a new semi-supervised classification method named semi-supervised ridge regression with adaptive graph-based label propagation (SSRR-AGLP). Firstly, we present a new adaptive graph-learning scheme and integrate it into the procedure of label propagation, in which the locality and sparsity of samples are considered simultaneously. Then, we introduce the ridge regression algorithm into label propagation to solve the "out of sample" problem. As a consequence, the proposed SSSRR-AGLP integrates adaptive graph learning, label propagation and ridge regression into a unified framework. Finally, an effective iterative updating algorithm is designed for solving the algorithm, and the convergence analysis is also provided. Extensive experiments are conducted on five databases. Through comparing the results with some well-known algorithms, the effectiveness and superiority of the proposed algorithm are demonstrated.

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“…For example, the densely deployed ultra-wide bandwidth (UWB) [15] and radio frequency identification (RFID) [16] anchors may provide distance information from the landmarks to the pedestrian, through time of arrival (ToA) and received signal strength (RSS) measurements, respectively. Wireless local area network (WLAN) or magnetic fingerprints [17,18] can also be regarded as landmarks to aid pedestrian navigation. Traditional landmark-based methods may improve positioning accuracy significantly.…”

Section: Introductionmentioning

confidence: 99%

Heading estimation fusing inertial sensors and landmarks for indoor navigation using a smartphone in the pocket

Deng

et al. 2017

J Wireless Com Network

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Principal component analysis (PCA)-based approach for user heading estimation using a smartphone in the pocket suffers from an inaccurate estimation of device attitude, which plays a central role in both obtaining acceleration signals in the horizontal plane and the ultimate global walking direction extraction. To solve this problem, we propose a novel heading estimation approach based on two unscented Kalman filters (UKFs) fusing inertial sensors and landmarks. The first UKF is developed for the recalibration of device attitude estimation. We mathematically derive the measurement equation connecting observed user heading from landmarks with the quaternion vector representing device attitude. To decrease the nonlinearity of the measurement equation and make the filter more robust, we deploy the difference between user heading derived from the landmark and estimation result of PCA-based approach as the observation variable. The second UKF is developed for user heading estimation fusing estimation results of PCA-based approach and observed user headings from landmarks. Besides, we develop a robust landmark identification method by exploiting the acceleration and device pitch patterns, while noisy barometers are no longer required as previous methods. Experiments show that the proposed landmark-aided user heading estimation approach may improve accuracy performance significantly, which is very useful for continuous indoor navigation.

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Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

Cited by 18 publications

References 34 publications

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation

Heading estimation fusing inertial sensors and landmarks for indoor navigation using a smartphone in the pocket

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