WiMag: Multimode Fusion Localization System based on Magnetic/WiFi/PDR

Xumeng, Guo; Shao, Wenhua; Zhao, Fang; Wang, Qu; Li, Dongmeng; Luo, Haiyong

doi:10.1109/ipin.2016.7743700

Cited by 31 publications

(21 citation statements)

References 10 publications

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“…Of course, the successes would decrease consistently if there was a large difference between the number of people detected by each technology. The error is, however, lower than previous exclusive WiFi fingerprinting-based systems, such as [2] (1.67 m), [20] (1.5 m), or [23] (1 m). The algorithm presented could be improved and integrated with these previous WPS methods or others which make use of inertial sensors or Kalman filters.…”

Section: Discussionmentioning

confidence: 60%

“…The comparison between the results obtained and the existing ones has to be made with other systems that are able to identify people, such as WiFi fingerprinting-based systems. Concretely, the error obtained (0.53m) is lower than previous exclusive WiFi fingerprinting-based systems, such as [2] (1.67m), [20] (1.5m) or [23] (1m). It should be…”

Section: Experiments and Discussionmentioning

confidence: 63%

“…Infrastructure-free solutions do not require modifications in the scenario. In IPIN 2016, Guo et al [20] presented a WiFi and a Pedestrian Dead Reckoning-based system (PDR), which considers factors such as speed to calculate the next location starting from a fixed known position. An error of 1.5 m was obtained.…”

Section: Overview Of Related Workmentioning

confidence: 99%

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Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Domingo

Gómez-García-Bermejo

Zalama

et al. 2019

Sensors

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This work presents an integrated Indoor Positioning System which makes use of WiFi signals and RGB cameras, such as surveillance cameras, to track and identify people navigating in complex indoor environments. Previous works have often been based on WiFi, but accuracy is limited. Other works use computer vision, but the problem of identifying concrete persons relies on such techniques as face recognition, which are not useful if there are many unknown people, or where the robustness decreases when individuals are seen from different points of view. The solution presented in this paper is based on an accurate combination of smartphones along with RGB cameras, such as those used in surveillance infrastructures. WiFi signals from smartphones allow the persons present in the environment to be identified uniquely, while the data coming from the cameras allow the precision of location to be improved. The system is nonintrusive, and biometric data about subjects is not required. In this paper, the proposed method is fully described and experiments performed to test the system are detailed along with the results obtained.

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Section: Discussionmentioning

confidence: 60%

Section: Experiments and Discussionmentioning

confidence: 63%

See 1 more Smart Citation

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Domingo

Gómez-García-Bermejo

Zalama

et al. 2019

Sensors

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show abstract

“…Indoor positioning technology, such as Wi-Fi [1,2], magnetic [3,4], pedestrian dead reckoning [5,6] and visible light [7,8] technologies, have become increasingly more important in people's daily life, and positioning services have gradually become an indispensable mobile application. Most Wi-Fi based positioning technologies do not require deploying additional hardware because they only utilize Wi-Fi hotspots and existing wireless LANs (Wireless Local Area Networks) to obtain position estimation.…”

Section: Introductionmentioning

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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“…Therefore, in addition to providing more accurate motion level estimation, precise stride length estimation based on built-in smartphone inertial sensors enhances positioning accuracy of PDR. Most visible light positioning [5,6], Wi-Fi positioning [7][8][9], and magnetic positioning [10][11][12] critically depend on PDR. Hence, motion level estimation based on smartphones contributes to assisting and supporting patients undergoing health rehabilitation and treatment, activity monitoring of daily living, navigation, and numerous other applications [13].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Wang

Luo

et al. 2019

Remote Sensing

Self Cite

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Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes.

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WiMag: Multimode Fusion Localization System based on Magnetic/WiFi/PDR

Cited by 31 publications

References 10 publications

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

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

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

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