Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization

Liu, Xu; Zhou, Baoding; Huang, Panpan; Xue, Weixing; Li, Qingquan; Zhu, Jiasong; Li, Qiu

doi:10.1109/jsen.2021.3050456

Cited by 88 publications

(37 citation statements)

References 32 publications

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“…In order to remove most of the range outliers, and making use of the redundancy provided by the up-to eight ranges, we robustified the Kalman filter using the innovation [28]. When computing the innovation, a measurement is taken into account whenever the innovation is lower than three times the standard deviation.…”

Section: Results In Positioningmentioning

confidence: 99%

Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

Jiménez

Seco

2021

Sensors

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The location of people, robots, and Internet-of-Things (IoT) devices has become increasingly important. Among the available location technologies, solutions based on ultrawideband (UWB) radio are having much success due to their accuracy, which is ideally at a centimeter level. However, this accuracy is degraded in most common indoor environments due to the presence of obstacles which block or reflect the radio signals used for ranging. One way to circumvent this difficulty is through robust estimation algorithms based on measurement redundancy, permitting to minimize the effect of significantly erroneous ranges (outliers). This need for redundancy often conflicts with hardware restraints put up by the location system’s designers. In this work, we present a procedure to increase the redundancy of UWB systems and demonstrate it with the help of a commercial system made by Decawave. This system is particularly easy to deploy, by configuring a network of beacons (anchors) and devices (tags) to be located; however, its architecture presents a major disadvantage as each tag to be located can only measure ranges to a maximum of four anchors. This limitation is embedded in the Positioning and Networking Stack (PANS) protocol designed by Decawave, and therefore is not easy to bypass without a total redesign of the firmware. In this paper, we analyze the strategies that we have been able to identify in order to provide this equipment with multiple range measurements, and thus enable each tag to be positioned with more than four measured ranges. We will see the advantages and disadvantages of each of these strategies, and finally we will adopt a solution that we implemented to be able to measure up to eight ranges for each mobile device (tag). This solution implies the duplication of the tags at the mobile user, and the creation of a double interleaved network of anchors. The range among tags and the eight beacons is obtained through an API via a wireless BLE protocol at a 10 Hz rate. A robustified Extended Kalman filter (EKF) is designed to estimate, by trilateration, the position of the pair of mobile tags, using eight ranges. Two different scenarios are used to make localization experimentation: a laboratory and an apartment. Our position estimation, which exploits redundant information and performs outlier removal, is compared with the commercial solution limited to four ranges, demonstrating the need and advantages of our multi-range approach.

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Section: Results In Positioningmentioning

confidence: 99%

Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

Jiménez

Seco

2021

Sensors

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“…In addition, some experts consider using sensors embedded in mobile phones for locating, such as WiFi or accelerometer ( Zhou et al, 2015 ). Instead of single tracking method, multiple sources combination is a promising way to improve camera tracking ( Li et al, 2016 ; Liu et al, 2021 ). Ma et al (2020) proposed a multi-sensor fusion-based algorithm to improve the precision of indoor localization.…”

Section: Related Workmentioning

confidence: 99%

Mobile augmented reality based indoor map for improving geo-visualization

Zhang

Huang

2021

PeerJ Computer Science

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Unlike traditional visualization methods, augmented reality (AR) inserts virtual objects and information directly into digital representations of the real world, which makes these objects and data more easily understood and interactive. The integration of AR and GIS is a promising way to display spatial information in context. However, most existing AR-GIS applications only provide local spatial information in a fixed location, which is exposed to a set of problems, limited legibility, information clutter and the incomplete spatial relationships. In addition, the indoor space structure is complex and GPS is unavailable, so that indoor AR systems are further impeded by the limited capacity of these systems to detect and display location and semantic information. To address this problem, the localization technique for tracking the camera positions was fused by Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). The multi-sensor fusion-based algorithm employs a particle filter. Based on the direction and position of the phone, the spatial information is automatically registered onto a live camera view. The proposed algorithm extracts and matches a bounding box of the indoor map to a real world scene. Finally, the indoor map and semantic information were rendered into the real world, based on the real-time computed spatial relationship between the indoor map and live camera view. Experimental results demonstrate that the average positioning error of our approach is 1.47 m, and 80% of proposed method error is within approximately 1.8 m. The positioning result can effectively support that AR and indoor map fusion technique links rich indoor spatial information to real world scenes. The method is not only suitable for traditional tasks related to indoor navigation, but it is also promising method for crowdsourcing data collection and indoor map reconstruction.

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“…Several state-of-art approaches used a fine timing measurement (FTM) protocol supported by a Wi-Fi system, which is a method of using a measurement of the distance based on the round-trip time by exchanging packets when both the AP and the device support the FTM protocol [ 19 ]. In particular, such approaches showed high ranging accuracy under LOS conditions [ 20 , 21 ], and these studies using a fusion of FTM and other sensors showed high accuracy in tracking the path of a pedestrian [ 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi

Lee

Park

Kim

et al. 2021

Sensors

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In this paper, we propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. We replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error compared to the existing methods. In addition, we verified that the proposed method was robust to changes in the indoor structure.

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Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization

Cited by 88 publications

References 32 publications

Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

Mobile augmented reality based indoor map for improving geo-visualization

Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi

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