FM-based indoor localization via automatic fingerprint DB construction and matching

Yoon, Sung Ho; Lee, Kyunghan; Rhee, Injong

doi:10.1145/2462456.2464445

Cited by 60 publications

(29 citation statements)

References 22 publications

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“…To evaluate the performance of navigation, 10 different reference traces were generated by randomly selected users who held their phones 4 The resolution of vertical distance changes depends on the threshold value ptr. In our current implementation, we empirically set ptr = 0.3. in front of their bodies.…”

Section: Discussionmentioning

confidence: 99%

“…If a gap |pn − pm|, indicating a level change, is greater than the threshold ptr, we conduct a forward search afterwards to determine the altitude-changing period. 4 The algorithm outputs a value r l that indicates whether the altitude increases/decrease (r l = −1/1) or not (r l = 0). Particularly, if less than 5 steps are taken by the user during the level-change period, we record an elevator up/down.…”

Section: Level-change Detectionmentioning

confidence: 99%

“…(e.g., using GPS, GLONASS, etc. ), and numerous approaches have been proposed to localize users in indoor environments [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Last-Mile Navigation Using Smartphones

Shu

Shin

et al. 2015

Proceedings of the 21st Annual International Conference on Mobile Computing and Networking

154

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Although GPS has become a standard component of smartphones, providing accurate navigation during the last portion of a trip remains an important but unsolved problem. Despite extensive research on localization, the limited resolution of a map imposes restrictions on the navigation engine in both indoor and outdoor environments. To bridge the gap between the end position obtained from legacy navigation services and the real destination, we propose FOLLOWME, a "last-mile" navigation system to enable plugand-play navigation in indoor and semi-outdoor environments. FOLLOWME exploits the ubiquitous, stable geomagnetic field and natural walking patterns to navigate the users to the same destination taken by an earlier traveler. Unlike existing localization and navigation systems, FOLLOWME is infrastructure-free, energyefficient and cost-saving. We implemented FOLLOWME on smartphones, and evaluated it in a four-story campus building with a testing area of 2000m 2 . Our experimental results with 5 users show that 95% of spatial errors during navigation were 2m or less with at least 50% energy savings over a benchmark system.

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Section: Discussionmentioning

confidence: 99%

Section: Level-change Detectionmentioning

confidence: 99%

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Last-Mile Navigation Using Smartphones

Shu

Shin

et al. 2015

Proceedings of the 21st Annual International Conference on Mobile Computing and Networking

154

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show abstract

“…Indoor localization has attracted a growing research attention and various techniques have been proposed, including Wi-Fi [14,33,34], Bluetooth [35], radiofrequency identification (RFID) [7,36], FM radio [37], acoustic signals [38], magnetic field [39], UWB [40], and light [41]. Among these signals, the use of Wi-Fi signal has attracted continuous attention due to the pervasive deployment of WLANs and Wi-Fi enabled mobile devices.…”

Section: Indoor Localizationmentioning

confidence: 99%

A Novel Indoor Localization Algorithm for Efficient Mobility Management in Wireless Networks

Xiao

Zhang

Zhu

2018

Wireless Communications and Mobile Computing

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Along with the penetration of smart devices and mobile applications in our daily life, how to effectively manage the mobility issues in wireless networks becomes a challenging task. The ability to continuously and accurately track the target object's position plays a vital role in mobility management. In this paper, we propose a novel indoor localization algorithm that fuses multiple signal features as the location fingerprints. The rationale that motivates our algorithm design stems from the following observation: although using one special signal feature (e.g., channel state information (CSI)) might achieve statistically higher accuracy than using another signal feature (e.g., received signal strength (RSS)), the accuracy for individual position estimations is usually diversified when only one signal feature is used in localization. For example, using RSS can obtain more accurate location estimation than using CSI for some individual positions. Thus, we propose a novel indoor localization algorithm that fuses multiple types of signal features as fingerprint of positions, which can effectively improve localization accuracy. We designed several fusion schemes and evaluated their performance. Experiments show that our algorithm achieves localization error below 0.5m and 1.1m in two typical indoor environments, about 30% lower than the accuracy of algorithms by fusing multiple signal features.

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“…A comprehensive study is presented on design and hardware selection of robots [14,15]. Different researchers have come up with different localization technologies for indoor environment; WiFi [16], ultrasonic [17,18], RFID tags [19][20][21], FM [22], magnetic field [23,24], IMU [25], cameras, lasers and sonar [26,27]. Most of these technologies and solutions have some disadvantages and limitations especially when they are used in the indoor environment.…”

Section: Introductionmentioning

confidence: 99%

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

Ullah

Zhang

et al. 2018

Symmetry

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Abstract:A novel approach is proposed for the path tracking of a Wheeled Mobile Robot (WMR) in the presence of an unknown lateral slip. Much of the existing work has assumed pure rolling conditions between the wheel and ground. Under the pure rolling conditions, the wheels of a WMR are supposed to roll without slipping. Complex wheel-ground interactions, acceleration and steering system noise are the factors which cause WMR wheel slip. A basic research problem in this context is localization and slip estimation of WMR from a stream of noisy sensors data when the robot is moving on a slippery surface, or moving at a high speed. DecaWave based ranging system and Particle Filter (PF) are good candidates to estimate the location of WMR indoors and outdoors. Unfortunately, wheel-slip of WMR limits the ultimate performance that can be achieved by real-world implementation of the PF, because location estimation systems typically partially rely on the robot heading. A small error in the WMR heading leads to a large error in location estimation of the PF because of its cumulative nature. In order to enhance the tracking and localization performance of the PF in the environments where the main reason for an error in the PF location estimation is angular noise, two methods were used for heading estimation of the WMR (1): Reinforcement Learning (RL) and (2): Location-based Heading Estimation (LHE). Trilateration is applied to DecaWave based ranging system for calculating the probable location of WMR, this noisy location along with PF current mean is used to estimate the WMR heading by using the above two methods. Beside the WMR location calculation, DecaWave based ranging system is also used to update the PF weights. The localization and tracking performance of the PF is significantly improved through incorporating heading error in localization by applying RL and LHE. Desired trajectory information is then used to develop an algorithm for extracting the lateral slip along X-and Y-axis from the PF estimated position of the WMR, the lateral slip along X-and Y-axis is then used to take some corrective measures. Lateral slip information is also used to find the direction along which WMR has to move to get back along the desired trajectory. Simulation results show that our proposed LHE and RL heading estimation methods significantly improve the PF localization and tracking performance on a slippery surface in both indoor and outdoor environments. The simulation results also show that the accurate locations of WMR and desired path information are used to estimate and compensate the lateral slip.

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FM-based indoor localization via automatic fingerprint DB construction and matching

Cited by 60 publications

References 22 publications

Last-Mile Navigation Using Smartphones

Last-Mile Navigation Using Smartphones

A Novel Indoor Localization Algorithm for Efficient Mobility Management in Wireless Networks

A Robust Localization, Slip Estimation, and Compensation System for WMR in the Indoor Environments

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