Optimal Configuration Analysis of AOA Localization and Optimal Heading Angles Generation Method for UAV Swarms

Wang, Weijia; Bai, Peng; Zhou, Yu; Liang, Xiaolong; Wang, Yubing

doi:10.1109/access.2019.2918299

Cited by 31 publications

(14 citation statements)

References 29 publications

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“…Among the different available indoor location techniques, those based on Received Signal Strength Indicator (RSSI) or Received Signal Strength (RSS) have proved their accuracy when positioning in limited areas [101,102], but their heavily depend on characteristics of the scenario (e.g., presence of metallic objects) and on the used UAV hardware (e.g., antennas) [103]. There are also positioning techniques based on computing the Angle of Arrival (AoA) of the received signals [104], or their time of arrival (through Time of Arrival (ToA) and Time Difference of Arrival (TDoA) techniques) [105].…”

Section: Positioning Subsystemmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

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Section: Positioning Subsystemmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

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“…The A-optimality criterion minimizes the trace of the inverse FIM, which minimizes the mean squared error (MSE) of the estimates. The Aoptimality criterion has been used to determine optimal flight paths for multiple UAVs using AoA measurements to localize a stationary target [20,21] or a moving target [22][23][24], and for 3D AoA target localization [25,26]. The diversity of the eigenvalues of the FIM has been used as an alternative criterion for the optimal placement of sensors for target position estimation [27].…”

Section: Related Workmentioning

confidence: 99%

Optimal Beacon Placement for Self-Localization Using Three Beacon Bearings

et al. 2020

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Autonomous vehicles need to localize themselves within the environment in order to effectively perform most tasks. In situations where a Global Navigation Satellite System such as the Global Positioning System cannot be used for localization, other methods are required. One self-localization method is to use signals transmitted by beacons at known locations to determine the relative distance and bearing of the vehicle from the beacons. Estimation performance is influenced by the beacon–vehicle geometry and the investigation into the optimal placement of beacons is of interest to maximize the estimation performance. In this article, a new solution to the optimal beacon placement problem for self-localization of a vehicle on a two-dimensional plane using angle-of-arrival measurements is proposed. The inclusion of heading angle in the estimation problem differentiates this work from angle-of-arrival target localization, making the optimization problem more difficult to solve. First, an expression of the determinant of the Fisher information matrix for an arbitrary number of beacons is provided. Then, a procedure for analytically determining the optimal angular separations for the case of three beacons is presented. The use of three beacons is motivated by practical considerations. Numerical simulations are used to demonstrate the optimality of the proposed method.

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“…Localization schemes based on TOA or TDOA offer high precision, but this comes at the cost of a very complex process of accurate time synchronization among all users [27]. This localization requires multiple receivers or stations [28]- [30] and its performance is also highly related to the receiver-target deployment [31]. Ultra-wide bandwidth-based systems are commonly used in the localization community to obtain desirable localization performance and simple multipath mitigation without costly estimators but at the cost of a large bandwidth [32].…”

Section: A Uav Positioning and Trackingmentioning

confidence: 99%

Nonlinearity-Based Single-Channel Monopulse Tracking Method for OFDM-Aided UAV A2G Communications

Yan²,

Zhang

2019

IEEE Access

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Unmanned aerial vehicles (UAVs) have attracted great interest in rapid deployment for both civilian and military applications. The conventional UAV monopulse tracking technique requires dedicated pulses which suffer from multipath effect in air-to-ground (A2G) link with low elevation angles, whereas the orthogonal frequency division multiplexing (OFDM) for A2G high rate communication under frequency selective fading channel was not yet investigated for UAV target tracking at ground station. In this paper, we propose a single-channel monopulse tracking (SCMT) angle estimation method in OFDM tracking receiver exploiting square-and absolute-value nonlinear detection of amplitude-modulated difference signals. Extensive simulations are conducted to validate the proposed estimation method in terms of estimation range and estimation root mean squared error (RMSE). Results show that, the proposed angle estimation method exhibits S-shaped curve characteristic under different angle errors, which is insensitive to channels and modulations. The proposed method obtains angle estimation RMSEs smaller than 0.18 • for antenna element distance d = 0.5 m with 10 MHz OFDM signals. For both square-and absolute-value methods, a maximum degradation of 0.02 • for angle estimation RMSEs is observed under Rician channels compared with the estimation under additive white Gaussian noise (AWGN) channels, whereas the absolute-value estimation outperforms the square-value estimation. INDEX TERMSUAV target tracking, air-to-ground communication, single-channel monopulse tracking, OFDM signals, frequency selective fading channel, nonlinear detection. CHAOXING YAN received the Ph.D. degree in communication and information system from the

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Optimal Configuration Analysis of AOA Localization and Optimal Heading Angles Generation Method for UAV Swarms

Cited by 31 publications

References 29 publications

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

Optimal Beacon Placement for Self-Localization Using Three Beacon Bearings

Nonlinearity-Based Single-Channel Monopulse Tracking Method for OFDM-Aided UAV A2G Communications

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