A Robust Passive Target Localization for Substation Inspection of UAV in a GPS-denied Environment

Suh, Ui-Suk; Kim, Tae‐Wan; Kang, D.; Lee, Kang‐Min; Ra, Won‐Sang; Kim, Tae-Wook

doi:10.1109/icit46573.2021.9453470

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…To effectively solve the localization problem with an uncertain measurement model, the robust weighted least-squares (RWLS) estimation theory summarized in TABLE 1 is considered [18]. The RWLS algorithm adopts estimation design parameters W k and V k to remove estimation errors caused by parametric uncertainty ∆H k .…”

Section: B Constrained Robust Weighted Least-squares Localization Est...mentioning

confidence: 99%

“…The nonlinear relationship between the RD measurement and relative UAV position is modeled as the uncertain linear measurement equation according to the linear state estimation theory. Then, the recently developed constrained robust weighted leastsquares (C-RWLS) estimation scheme is applied to estimate the UAV position [18]. This method can efficiently solve the nonlinear localization problem with low computation and holds a fast convergence property due to its linear filter structure.…”

Section: Introductionmentioning

confidence: 99%

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Passive IR-UWB Localization System for UAV-Based Electric Facility Inspection During GPS Outage

et al. 2023

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A practical localization system is proposed for a substation inspection unmanned aerial vehicle (UAV) performing its mission under global positioning system (GPS) signal blockage. The sensor hardware consists of a single transmitter and a cruciform receiver array, which produces the range difference (RD) information used for UAV positioning. The use of impulse-radio ultra wide-band (IR-UWB) devices secures the robustness against RF interference often caused by an exogenous electromagnetic field. With this hardware configuration, the UAV localization can be formulated as the state estimation problem for an uncertain linear state space model and be solved by using the computationally efficient robust weighted least squares (RWLS) estimator. Since the standard RWLS estimator could be sensitive to the imperfectness of prior knowledge on the noise statistic, a geometric constraint expressed in terms of a UAV position is exploited to secure the reliable localization performance in practice. Through experimental results, it is verified that the proposed solution provides reliable UAV positioning performance despite using the imperfect measurement noise statistics for the localization system.

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Section: B Constrained Robust Weighted Least-squares Localization Est...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Passive IR-UWB Localization System for UAV-Based Electric Facility Inspection During GPS Outage

et al. 2023

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“…In this paper, deep learning techniques based on CRNN model for Multivariate Time Series Prediction, combined with a method based on DAE to learn noise characteristics, have been exploited to generate synthetic sensor data with real world-like noise characteristics, incorporating measurements from sensors and heterogeneous information with the aim of generating synthetic data that can be used to augment real-world data. Specifically, we focused on two sensor classes: UWBs which are used in robotics for localization and SLAM tasks as widely employed in [78], [79], [80], [81], and [82] and UHF-RFID that have been used in autonomous agents for localization [83] and SLAM tasks as in [84] and [85].…”

Section: A Related Workmentioning

confidence: 99%

Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

Romanelli,

Martinelli

2023

IEEE Access

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Deep learning has transformed data generation, particularly in creating synthetic sensor data. This capability is invaluable in fields like autonomous driving, robotics, and computer science. To achieve this, we train models using real data, enabling them to replicate sensor data closely. These models introduce variations and noise, enhancing diversity and realism. Prominent techniques, including generative adversarial networks (GANs), variational autoencoders (VAEs), and recurrent neural networks (RNNs), excel in generating synthetic sensor data. Our paper focuses on Autoregressive Convolutional Recurrent Neural Networks (CRNN) for Multivariate Time Series Prediction. We incorporate Denoising Autoencoders (DAE) to mimic real-world noise characteristics. Our model is trained and validated using Ultra Wide Band (UWB) and Ultra High-Frequency Radio Frequency Identification (UHF-RFID) sensor data. It integrates sensor measurements and diverse information sources to produce synthetic data complementing realworld data. While demonstrated with UHF-RFID and UWB sensors, these techniques extend to industrial automation, healthcare and environmental monitoring. While our methodology exhibits broad potential, we present practical demonstrations with UHF-RFID and UWB sensors. Our deep neural network model allows researchers to construct datasets for algorithm validation, eliminating the need for costly and time-consuming data collection.

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“…At present, there are two ways to control the inspection operation of multi-rotor UAVs in the substation electrical equipment live inspection: one is the professional remote control multi-rotor UAV flight operation, and the other is the preset inspection flight path after the multi-rotor UAV independent flight to carry out inspection operations [7]. However, even if the second control mode is adopted, since multi-rotor UAVs need to frequently round-trip to inspect airspace and cabin, professionals still need to be deeply involved in the control or operation from cabin takeoff to inspection airspace, from inspection airspace to cabin landing, and in the cabin charging operation.…”

Section: Introductionmentioning

confidence: 99%

Research on the Design of UAV Relay Charging High-rise Platform for Electrical Equipment Live Detection in Substation

Zhang

Ning

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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The multi-rotor UAV inspection technology has received extensive attention in the live detection of electrical equipment in substations and has begun to be practically applied. However, due to the short endurance and the characteristics of intermittent operations, the UAV needs to frequently travel between the operational airspace and the cabin, which seriously affects the operational efficiency of the inspection. This paper first introduces the traditional substation electrical equipment live detection technology and then summarizes the application of UAV inspection in the substation electrical equipment live detection. Finally, in view of the shortcomings of the existing technology, this paper proposes a design scheme of a relay-charging high-rise platform for live detection and inspection of electrical equipment in substations. The relay charging high-rise platform can be divided into six parts according to the functional structure: high-altitude apron, UAV landing guidance system, UAV automatic binding system, high insulation strength power supply system, and platform monitoring system. In this paper, the overall structure of the relay charging high-level platform and the technical solutions of each component are described in detail. The technical solution can effectively improve the efficiency and safety of multi-rotor UAV inspection operations for live detection of electrical equipment in substations.

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A Robust Passive Target Localization for Substation Inspection of UAV in a GPS-denied Environment

Cited by 5 publications

References 11 publications

Passive IR-UWB Localization System for UAV-Based Electric Facility Inspection During GPS Outage

Passive IR-UWB Localization System for UAV-Based Electric Facility Inspection During GPS Outage

Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

Research on the Design of UAV Relay Charging High-rise Platform for Electrical Equipment Live Detection in Substation

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