Robust WiFi-based indoor localization using multipath component analysis

Zayets, Alexandra; Steinbach, Eckehard

doi:10.1109/ipin.2017.8115943

Cited by 38 publications

(30 citation statements)

References 14 publications

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“…According to a propagation model of Wi-Fi RSSIs [43][44][45], the RSSI value decreases exponentially when the distance between a transmitter and a receiver increases linearly. In reality, however, the interruption of the multipath generates uncertain Wi-Fi RSSIs because of the existence of a number of walls and the interference from other radio signals.…”

Section: Nonlinearity and Uncertaintymentioning

confidence: 99%

“…where µ TL is the trajectory among h 1:n closest to x k . The variance Σ TL is set to the estimated covariance Σ h of the learn trajectory, which is obtained from Kalman smoothing in Equation (43). The covariance Σ X , which is also estimated in Equation (43), indicates how far the samples' trajectory is apart from the learned trajectory.…”

Section: Trajectory Learning From a Crowdmentioning

confidence: 99%

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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: Nonlinearity and Uncertaintymentioning

confidence: 99%

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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“…However, it requires a non-trivial algorithm to proses the environment information so that the position of the robot is obtained. Example of this method can be found in (2), (3), (4), (5), (6), (7), (8), and (9).…”

Section: Introductionmentioning

confidence: 99%

A low cost indoor localization system for mobile robot experimental setup

Adinandra

Syarif

2018

J. Phys.: Conf. Ser.

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“…During the online positioning phase, the fingerprint-based positioning algorithm compares the current Wi-Fi observation with the recorded fingerprint in the database to obtain the target position using the optimum matching criterion. Compared with the fingerprint-based positioning algorithm, the RSSI-based ranging positioning algorithm struggles to meet high positioning accuracy due to the complex multipath and dynamic characteristics of signal propagation in indoor environments [15,16].Current Wi-Fi fingerprint-based indoor localization mainly adopts either deterministic or probabilistic techniques [17][18][19][20]. The deterministic Wi-Fi positioning methods employ different deterministic machine learning algorithms to estimate the target location based on the shortest distance (such as Euclidean distance) criterion, such as KNN (K-NearestNeighbor) [21][22][23], linear discriminant analysis [24], and SVM (Support Vector Machine) [25].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

Wang

Luo

et al. 2019

Remote Sensing

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With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset. present, due to the wide deployment and availability of Wi-Fi infrastructure, Wi-Fi fingerprint-based localization has become one of the most dominant indoor positioning techniques.There are two main types of Wi-Fi-based indoor positioning technologies: RSSI (Received Signal Strength Indicator)-based ranging positioning algorithm [9][10][11], and fingerprint-based positioning algorithm [12][13][14]. The RSSI-based ranging positioning algorithm [11] usually adopts the received Wi-Fi signal to estimate the distance between the target (its location is unknown) and the access point (its location is known) using the wireless radio signal propagation model, and then estimates the target position using trilateration or multilateration methods. The fingerprint-based positioning algorithm [14] adopts the signal matching algorithm to estimate the user location. It first collects environmental Wi-Fi signals and constructs a Wi-Fi fingerprint database during the offline phase. During the online positioning phase, the fingerprint-based positioning algorithm compares the current Wi-Fi observation with the recorded fingerprint in the database to obtain the target position using the optimum matching criterion. Compared with the fingerprint-based positioning algorithm, the RSSI-based ranging positioning algorithm struggles to meet...

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Robust WiFi-based indoor localization using multipath component analysis

Cited by 38 publications

References 14 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

A low cost indoor localization system for mobile robot experimental setup

A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

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