Indoor Drone Positioning: Accuracy and Cost Trade-Off for Sensor Fusion

Gerwen, Jono Vanhie-Van; Geebelen, Kurt; Wan, Jia; Joseph, Wout; Hoebeke, Jeroen; Poorter, Eli De

doi:10.1109/tvt.2021.3129917

Cited by 33 publications

(20 citation statements)

References 36 publications

(31 reference statements)

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“…The drone (i.e., the flying RFID reader) is tracked by a sensor fusion-based scheme that has fused the results from Decawave UWB and other low-cost directional sensors (IMU, magnetometer, etc.) based on an extended Kalman filter (EKF) [12,13]. For the RFID interrogation, a lightweight reader ThingMagic M6E-Micro [4]) is utilized.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Deep Learning Enables Robust Drone-based UHF-RFID Localization in Warehouses

Tanghe

Suanet³

et al. 2022

2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC)

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Radio frequency identification (RFID) localization technology has attracted great attention in stocktaking in warehouses. In this paper, we investigate drone-based RFID localization for fast and accurate inventory management. Considering the drone trajectory errors, we propose a robust RFID lateral localization method based on the unwrapped phase, in which a temporal convolutional network (TCN) with non-causal convolutions is designed for the phase unwrapping. The tagged assets are localized via the nonlinear optimization upon the unwrapped phases. The experiment results in a logistic warehouse show that the proposed algorithm achieves RFID localization with 0.17-meter mean absolute errors and 0.4-meter 90-th percentile errors, respectively.

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Section: Experimental Evaluationmentioning

confidence: 99%

Deep Learning Enables Robust Drone-based UHF-RFID Localization in Warehouses

Tanghe

Suanet³

et al. 2022

2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC)

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“…They used acoustic, image/video, and wireless RF signals as system inputs. As UAV indoor flights place greater emphasis on stability and localization accuracy, Gerwen emphet al in [8] used a flexible sensor fusion platform. The deployed system targeted the influence of multiple sensors on 3D indoor location accuracy when faced with different-sized UAVs.…”

Section: B Contributionsmentioning

confidence: 99%

“…According to Lykou et al in [5], around 6% commercial UAV detection systems designed on acoustic sensors, 26% are using radio frequency (RF), 28% are radar-based, and 40% adopted visual sensors for detection. Mostly those sensors adopted for drone detection and classification are: radar (radio detection and ranging on several different frequency bands for both active and passive sensing) [6], cameras for the visible spectrum sensing [4], cameras detecting thermal or infrared (IR) emissions [7], LIDAR (light detection and ranging) [8], microphones/acoustic sensors for acoustic vibration detection [2], and RF [9], [10] monitoring sensors for the detection of radio signals [11].…”

Section: Introductionmentioning

confidence: 99%

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SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

dil¹,

khan²,

Alam³

et al. 2022

Preprint

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<p>Unmanned air vehicles (UAVs) popularity is on the rise as it enables the services like traffic monitoring, emergency communications, deliveries, and surveillance. However, the unauthorized usage of UAVs (a.k.a drone) may violate security and privacy protocols for security-sensitive national and international institutions. The presented challenges require fast, efficient, and precise detection of UAVs irrespective of harsh weather conditions, the presence of different objects, and their size to enable SafeSpace. Recently, there has been significant progress in using the latest deep learning models, but those models have shortcomings in terms of computational complexity, precision, and non-scalability. To overcome these limitations, we propose a precise and efficient multiscale and multifeature UAV detection network for SafeSpace, i.e., \textit{MultiFeatureNet} (\textit{MFNet}), an improved version of the popular object detection algorithm YOLOv5s. In \textit{MFNet}, we perform multiple changes in the backbone and neck of the YOLOv5s network to focus on the various small and ignored features required for accurate and fast UAV detection. To further improve the accuracy and focus on the specific situation and multiscale UAVs, we classify the \textit{MFNet} into small (S), medium (M), and large (L): these are the combinations of various size filters in the convolution and the bottleneckCSP layers, reside in the backbone and neck of the architecture. This classification helps to overcome the computational cost by training the model on a specific feature map rather than all the features. The results show significant performance gain even for unseen feature maps with minimal loss in accuracy. Results show a significant reduction in training parameters, inference, and increased pattern in FPS and GFLOPs for \textit{MFNet} compared to YOLOv5s. \textit{MFNet-M} performance evaluation in terms of precision, recall, mean average-precision (mAP), and IOU increased around 1.8\%, 2.2\%, 0.9\%, 1.7\% compared to YOLOv5s. Furthermore, \textit{MFNet-M} achieves the best performance with 96.8\% precision, 88.4\% recall, 95.9\% mAP, and 51.1\% IoU for UAV detection. The dataset and code are available as an open source: github.com/ZeeshanKaleem/MultiFeatureNet.</p>

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“…The distance between anchors and tags can be determined using two-way-ranging (TWR) [ 15 ]. Precise distance measurement enables accurate estimation of both 2D and 3D positions [ 16 , 17 , 18 ]. While the accuracy of TOA is generally superior among time-based methods, the latency of target positioning tends to increase with an increasing number of tags [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System

Huang

Chen

Wei

et al. 2023

Sensors

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The Internet of Things (IoT) for smart industry requires the surveillance and management of people and objects. The ultra-wideband positioning system is an attractive solution for achieving centimeter-level accuracy in target location. While many studies have focused on improving the accuracy of the anchor coverage range, it is important to note that in practical applications, positioning areas are often limited and obstructed by furniture, shelves, pillars, or walls, which can restrict the placement of anchors. Furthermore, some positioning regions are located beyond anchor coverage, and a single group with few anchors may not be able to cover all rooms and aisles on a floor due to non-line-of-sight errors causing severe positioning errors. In this work, we propose a dynamic-reference anchor time difference of arrival (TDOA) compensation algorithm to enhance accuracy beyond anchor coverage by eliminating local minima of the TDOA loss function near anchors. We designed a multidimensional and multigroup TDOA positioning system with the aim of broadening the coverage of indoor positioning and accommodating complex indoor environments. By employing an address-filter technique and group-switching process, tags can seamlessly move between groups with a high positioning rate, low latency, and high accuracy. We deployed the system in a medical center to locate and manage researchers with infectious medical waste, demonstrating its usefulness for practical healthcare institutions. Our proposed positioning system can thus facilitate precise and wide-range indoor and outdoor wireless localization.

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Indoor Drone Positioning: Accuracy and Cost Trade-Off for Sensor Fusion

Cited by 33 publications

References 36 publications

Deep Learning Enables Robust Drone-based UHF-RFID Localization in Warehouses

Deep Learning Enables Robust Drone-based UHF-RFID Localization in Warehouses

SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System

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