A Gaussian Process model for UAV localization using millimetre wave radar

Paredes, José A.; Álvarez, F.; Hansard, Miles; Rajab, Khalid Z.

doi:10.1016/j.eswa.2021.115563

Cited by 21 publications

(11 citation statements)

References 39 publications

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“…This method has the advantages of low power consumption, long detection distance, strong penetrating ability of dust and fog, and strong anti-interference ability. José A. Paredes et al [6] proposed A new 3D positioning system for UAV based on millimeter-wave radar. The system corrects the deviation through Gaussian process regression model, which can support UAV flight in complex or poor visibility environment.…”

Section: Millimeter Wave Radar Rangingmentioning

confidence: 99%

Research on key technologies of fire fighting UAV

Wang,

Guo,

Zhang

et al. 2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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In view of the problems existing in the fire prevention and rescue process, such as low efficiency and accuracy of fire inspection, difficult rescue operation, long operation time of traditional equipment, increasingly complex fire environment and high difficulty in fire area investigation, the research status of key technologies of fire fighting UAV at home and abroad is summarized from three aspects: flight control system, obstacle avoidance technology and fire monitoring and analysis technology. The development trend of firefighting UAVs is predicted as follows: (1) The stable flight control optimization of uAVs in complex fire environment: the size, shape, topography, topology and other basic factors affecting the stability of uAVs are optimized, and the matching of flight control system with the moment of inertia, dynamic redundancy and power system vector control technology of multi-rotor uAVs is improved; (2) Continuous improvement of endurance: through high energy laser and intelligent signal transmission and processing technology, realize the automation of remote energy supply, combined with intelligent visual tracking algorithm, real-time guarantee of UAV signal and laser undertaking; (3) Intelligent obstacle avoidance: multiple methods are combined to solve the obstacle avoidance problem of UAV under different circumstances. In addition, the scene model is established based on digital twin to obtain the best obstacle avoidance route, and the algorithm in the airborne computer is used to obtain the optimal path. (4) Cluster compatibility: The single firefighting UAV cannot adapt to the increasingly complex fire environment. The development of UAV swarm intelligence technology is helpful for the UAV cluster to carry the distributed load to complete the multi-point measurement task that the single drone cannot complete, and provide more accurate measurement data.

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Section: Millimeter Wave Radar Rangingmentioning

confidence: 99%

Research on key technologies of fire fighting UAV

Wang,

Guo,

Zhang

et al. 2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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“…When millimeter wave radar detects the same target, the detection points are unstable in the radar coordinate system due to the low angular accuracy. It is usually assumed that these points obey the Gaussian distribution shown in Figure 8 [30].…”

Section: Soft-threshold-multi-path-revised Correlative Scan Matchingmentioning

confidence: 99%

False Detections Revising Algorithm for Millimeter Wave Radar SLAM in Tunnel

Wei

Wang

et al. 2023

Remote Sensing

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Millimeter wave (MMW) radar simultaneous localization and mapping (SLAM) technology is an emerging technology in a tunnel vehicle accident rescue scene. It is a powerful tool for statistic-trapped vehicle detection with limited vision caused by darkness, heat, and smoke. A variety of SLAM frameworks have been proven to be able to obtain 3-degree-of-freedom (3-dof) pose estimation results using 2-dimention (2D) MMW radar in open space. In the application of millimeter wave radar for pose estimation and mapping in a closed environment, closed space structures and artificial targets together constitute high-intensity multi-path scattering measurement data, leading to radar false detections. Radar false detections caused by multi-path scattering are generally considered to be detrimental to radar applications, such as multi-target tracking. However, few studies analyze the mechanism of multi-path effects on radar SLAM, especially in closed spaces. In order to address the problem, this paper first presents a radar multi-path scattering theory to study the generation mechanism difference of false and radar true detection and their influences on radar SLAM 2D pose estimation and mapping in tunnel. According to the scattering mechanism differences on SLAM, a radar azimuth scattering angle signature is proposed, which allows distinguishing radar false detections from real ones. This is useful in avoiding using unreliable radar false detections to solve a radar SLAM problem. In addition, two different radar false detection revising methods combined with the CSM (correlative scan matching) algorithm are proposed in this paper. The HTMR-CSM (hard-threshold-multi-path-revised correlative scan matching) algorithm only depends on a hard threshold of radar azimuth scattering angle signature to eliminate all radar false detections as much as possible before CSM. Another idea is the STMR-CSM (soft-threshold-multi-path-revised correlative scan matching) algorithm. All the radar false detections are classified according to the distribution model of radar azimuth accuracy, and part of more reliable radar false detections are retained to estimate a more accurate pose. All the ideas in this paper are validated by using an MMW 2D radar mounted on a rail-guided robot in a tunnel. Two cars on fire were set as the targets. The experimental results show that the STMR-CSM algorithm that keeps the reliable radar false detections improves the positioning accuracy by 20% compared with CSM.

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“…An example of mmWave Radar with a precise detection and 3D localization system for drones can be observed in [ 10 ]. The positions of drones are estimated from spatial heatmaps of the received radar signals, obtained by applying a super-resolution algorithm.…”

Section: Review Of the State-of-the-art Technologymentioning

confidence: 99%

Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

Besada

Campaña

Carramiñana

et al. 2021

Sensors

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Noncollaborative surveillance of airborne UAS (Unmanned Aerial System) is a key enabler to the safe integration of UAS within a UTM (Unmanned Traffic Management) ecosystem. Thus, a wide variety of new sensors (known as Counter-UAS sensors) are being developed to provide real-time UAS tracking, ranging from radar, RF analysis and image-based detection to even sound-based sensors. This paper aims to discuss the current state-of-the art technology in this wide variety of sensors (both academically and commercially) and to propose a set of simulation models for them. Thus, the review is focused on identifying the key parameters and processes that allow modeling their performance and operation, which reflect the variety of measurement processes. The resulting simulation models are designed to help evaluate how sensors’ performances affect UTM systems, and specifically the implications in their tracking and tactical services (i.e., tactical conflicts with uncontrolled drones). The simulation models cover probabilistic detection (i.e., false alarms and probability of detection) and measurement errors, considering equipment installation (i.e., monostatic vs. multistatic configurations, passive sensing, etc.). The models were integrated in a UTM simulation platform and simulation results are included in the paper for active radars, passive radars, and acoustic sensors.

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A Gaussian Process model for UAV localization using millimetre wave radar

Cited by 21 publications

References 39 publications

Research on key technologies of fire fighting UAV

Research on key technologies of fire fighting UAV

False Detections Revising Algorithm for Millimeter Wave Radar SLAM in Tunnel

Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

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