UAV Tracking System Using Integrated Sensor Fusion with RTK-GPS

Abdelfatah, Reham; Moawad, Ahmed W; Alshaer, Nancy; Ismail, Tawfik

doi:10.1109/miucc52538.2021.9447646

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…UAV placement a priori in large paths. Dynamic placement of UAVs for any trajectory [19,21] Integrated sensor fusion with GNSS.…”

Section: Uav Deployment and Managementmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta¹,

Fernando²

2022

Preprint

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<p>This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.</p>

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“…UAV placement a priori in large paths. Dynamic placement of UAVs for any trajectory [19,21] Integrated sensor fusion with GNSS.…”

Section: Uav Deployment and Managementmentioning

confidence: 99%

“…Utilizing mmWave for localization also yields better accuracy [18,19]. As GPS precision is in the order of a few centimeters, it is an active area of research to capture objects larger than an inch in UAV based mobile mapping systems [20], as well as to correlate mapping with UAV localization using GPS [21].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta¹,

Fernando²

2022

Preprint

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“…Sensor fusion using an accelerometer, a gyroscope, a magnetometer, and a global positioning system (GPS) is implemented to reduce the uncertainty of position and attitude angles and define the UAV location more precisely. Furthermore, an extended Kalman filter (EKF) is being developed to combine the data sensing from multiple sensors [44] [45].…”

Section: Tracking Systemmentioning

confidence: 99%

Reliability and Security Analysis of an Entanglement-Based QKD Protocol in a Dynamic Ground-to-UAV FSO Communications System

Alshaer

Moawad

Ismail³

2021

IEEE Access

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Quantum cryptography is a promising technology that achieves unconditional security, which is essential to a wide range of sensitive applications. In contrast to optical fiber, the free-space optical (FSO) link is efficiently used as a quantum channel without affecting the polarization of transmitted photons. However, the FSO link has several impairments, such as atmospheric turbulence and pointing errors, which affect the performance of the quantum channel. This paper proposes a quantum key distribution (QKD) scheme that uses a time-bin entanglement protocol over the FSO channel that suffers from various channel impairments. Due to the interest in unmanned aerial vehicles (UAVs) and their usefulness for many social, internet-of-things (IoT), civil, and military applications, the proposed QKD-FSO system is integrated with the ground-to-UAV platform. Furthermore, variances in the position and orientation of the UAV are investigated using a tracking system. These variances are considered when evaluating the overall performance of the proposed integrated system. For this purpose, closed-form expressions are obtained for the system average symbol error rate (ASER) and outage probability. The Monte Carlo simulation is used to verify the validity of the proposed expressions. The system security is investigated assuming photon number splitting (PNS) attack. Moreover, the transmit power, time-bin number, and modulation order are optimized to maximize the raw and secret key rates. The results show that for 500 m link, ASER < 10 −1 and link outage probability < 10 −1 with tolerating boresight displacement up to 30 cm, the system should be configured at receiver's field-of-view > 22 mrad and signal-to-noise ratio > 7.5 dB which leads SNR th < 2.5 dB and raw key rate maximized by adjusting the number of time bins according to the received SNR.INDEX TERMS FSO communication, channel fluctuations, quantum cryptography, entanglement photon, E91 protocol, raw key rate, unmanned aerial vehicles (UAVs), tracking system, Kalman filter, performance evaluation, photon number splitting (PNS).

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“…Drone path planning involves navigating the drone to a desired destination travelling over a predefined path that constitutes obstacles and other environment constraints. The work in [35] shows how the sensor fusion along with real-time kinematic GPS sensors is used to accurately calculate the altitude and position of the drone. They generate a data-set using instantaneous positions of the drone in different directions along with the roll, pitch and yaw angles.…”

Section: Sensor Fusion For Uav Localizationmentioning

confidence: 99%

“…Further, they compare this data with the output of the sensor fusion model estimations that are carried out using an EKF to produce position and altitude estimates of drones. [30] Extended Kalman Filter Non-Linear Estimation Localization of UAVs Localization without GPS Kraft et al [32] Unscented Kalman Filter Linearized Estimation Localization of UAVs Orientation Computation Abdelfatah et al [35] Sensor Fusion Non-Linear Estimation Localization of UAVs Altitude, Position Estimation Chapter 3…”

Section: Sensor Fusion For Uav Localizationmentioning

confidence: 99%

Location prediction and trajectory optimization in multi-UAV application missions

Singh¹

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Unmanned aerial vehicles (a.k.a. drones) have a wide range of applications in e.g., aerial surveillance, mapping, imaging, monitoring, maritime operations, parcel delivery, and disaster response management. Their operations require reliable networking environments and location-based services in air-to-air links with cooperative drones, or air-to-ground links in concert with ground control stations. When equipped with high-resolution video cameras or sensors to gain environmental situation awareness through object detection/tracking, precise location predictions of individual or groups of drones at any instant possible is critical for continuous guidance. The location predictions then can be used in trajectory optimization for achieving efficient operations (i.e., through effective resource utilization in terms of energy or network bandwidth consumption) and safe operations (i.e., through avoidance of obstacles or sudden landing) within application missions. In this thesis, we explain a diverse set of techniques involved in drone location prediction, position and velocity estimation and trajectory optimization involving: (i) Kalman Filtering techniques, and (ii) Machine Learning models such as reinforcement learning and deep-reinforcement learning. These techniques facilitate the drones to follow intelligent paths and establish optimal trajectories while carrying out successful application missions under given resource and network constraints. We detail the techniques using two scenarios. The first scenario involves location prediction based intelligent packet transfer between drones in a disaster response scenario using the various Kalman Filtering techniques. The second scenario involves a learning-based trajectory optimization that uses various reinforcement learning models for maintaining high video resolution and effective network performance in a civil application scenario such as aerial monitoring of persons/objects. We conclude with a list of open challenges and future works for intelligent path planning of drones using location prediction and trajectory optimization techniques.

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UAV Tracking System Using Integrated Sensor Fusion with RTK-GPS

Cited by 7 publications

References 12 publications

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Reliability and Security Analysis of an Entanglement-Based QKD Protocol in a Dynamic Ground-to-UAV FSO Communications System

Location prediction and trajectory optimization in multi-UAV application missions

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