LTE‐based passive radar for drone detection and its experimental results

Dan, Yangpeng; Yi, Jianxin; Wan, Xianrong; Yunhua, Rao; Wang, Benjing

doi:10.1049/joe.2019.0583

Cited by 11 publications

(13 citation statements)

References 13 publications

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“…The micro-Doppler signature was also adopted for drone classification in a non-cooperative drone surveillance system [21] with support vector machine (SVM) and decision tree (DT) classifiers. Recently, a passive radar with longterm evolution (LTE) downlink signals [22] was exploited to detect small UAVs. Geng et al [23] utilized frequencymodulated continuous waveform to specify an effective LTE downlink signal feature, which in turn increases the accuracy of drone detection.…”

Section: State-of-the-art Techniques a Radar-based Techniquementioning

confidence: 99%

RF-UAVNet: High-Performance Convolutional Network for RF-Based Drone Surveillance Systems

et al. 2022

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In recent years, the increasing popularity of unmanned aerial vehicles (UAVs) has arisen from the emergence of cutting-edge technologies deployed in small and low-cost devices. With the great capability of friendly uses and wide applications for multiple purposes, amateur drones can be piloted to effortlessly access any geographical area. This poses some difficulties in monitoring and managing drones that may invade private or limited-access areas. In this paper, we propose a radio-frequency (RF)-based surveillance solution to effectively detect and classify drones, and recognize operations by leveraging a high-performance convolutional neural network. The proposed network, namely RF-UAVNet, is specified with grouped one-dimensional convolution to significantly reduce the network size and computational cost. Besides, a novel structure of multi-level skip-connection, for the preservation of gradient flow, incorporating multi-level pooling, for the collection of informative deep features, is proposed to achieve high accuracy via learning efficiency improvement. In the experiments, RF-UAVNet yields the accuracy of 99.85% for drone detection, 98.53% for drone classification, and 95.33% for operation mode recognition, numbers which outperform the current state-of-the-art deep learning-based methods on DroneRF, a publicly available dataset for RF-based drone surveillance systems.

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Section: State-of-the-art Techniques a Radar-based Techniquementioning

confidence: 99%

RF-UAVNet: High-Performance Convolutional Network for RF-Based Drone Surveillance Systems

et al. 2022

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“…In Dan et al (2019), an LTE Passive radar for UAV detection using an SDR has been proposed. A bandwidth of 15 MHz for a 10 m range resolution has been considered in Dan et al (2019).…”

Section: Cp Ofdm Based Conceptmentioning

confidence: 99%

“…In Dan et al (2019), an LTE Passive radar for UAV detection using an SDR has been proposed. A bandwidth of 15 MHz for a 10 m range resolution has been considered in Dan et al (2019). In the receiver system, 18 dBi gain 18-elements Yagi-uda antenna with 32 � beam-width in both the reference and target antennas have been used in SDR Krátký and Fuxa (2015); Vinogradov, Kovalev, and Pollin (2018b).…”

Section: Cp Ofdm Based Conceptmentioning

confidence: 99%

LTE-based passive radars and applications: a review

Kumar

Khan

et al. 2021

International Journal of Remote Sensing

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This paper provides an overview of the most recent passive radars based on long-term evolution (LTE). To begin, this paper investigates the various characteristics and requirements of 4 G LTE signals for radar, taking performance aspects such as range, velocity, range resolution, and velocity resolution into account. An ambiguity function analysis is performed on a measured LTE signal using the synchronization and reference signal components to evaluate key performance parameters such as Doppler and range characteristics. We also discuss how LTE passive radar can be used in a variety of applications. The detailed analysis of the LTE downlink signal, its structural overview, and the effect on cross-and self-ambiguity functions are all discussed. The paper investigates related standard development proposals, with a focus on performance evaluation criteria for existing passive radar technologies. As a result, this survey paper serves as a starting point for evaluating the performance of current and future passive radar innovations, including an emerging 5 G radar.

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“…This study not only described the corresponding radar system platform but also verified the effectiveness of the MIMO radar in detecting and tracking UAVs, combined with the data of multiple sensors. References [6–8] carry out detection and positioning of UAVs in urban environments based on signals from existing radiation sources in space, such as LTE, radio and television signals. Reference [8] uses broadband LTE downlink signals to detect UAVs based on radio equipment and external emitter radars.…”

Section: Introductionmentioning

confidence: 99%

“…At present, scholars at home and abroad are carrying out a large number of studies on the detection and location of small UAVs in urban environments, and they have a series of achievements, mainly including active radar detection scheme [3][4][5], external emitter radar method [6][7][8][9] and passive detection system [10][11][12]. Reference [3] puts forward an active radar UAV detection scheme based on MIMO.…”

mentioning

confidence: 99%

Single‐station radar‐based UAV position estimation in complex urban environments

Bai

Cui

2021

IET Radar Sonar & Navi

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In the complex electromagnetic environment of cities, detection and positioning of slow and small unmanned aerial vehicles (UAVs) have always been a very challenging research topic. Usually, multiple radar observation stations are required to detect and track targets. Moreover, each radar observation station requires strict synchronisation, and the system is complex and costly. In view of this, under the condition of known prior information of urban geography, this study proposes a method to locate UAVs in complex electromagnetic environments, which use multiple virtual mirror synchronous radars constructed by multipaths and created by the complex buildings in urban environments. First, the simulation model is constructed, and the corresponding simulation parameters are given. Then, the algorithm for solving the single-station radar location is proposed, and the algorithm is verified by the simulation of a ray propagation model. Finally, the proposed algorithm is further verified based on the measured data captured by the radar system in Section 2. The results show that the proposed algorithm based on the singlestation radar is able to locate the UAV in both line-of-sight and non-line-of-sight site, and its positioning accuracy is better than 1 m.

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LTE‐based passive radar for drone detection and its experimental results

Cited by 11 publications

References 13 publications

RF-UAVNet: High-Performance Convolutional Network for RF-Based Drone Surveillance Systems

RF-UAVNet: High-Performance Convolutional Network for RF-Based Drone Surveillance Systems

LTE-based passive radars and applications: a review

Single‐station radar‐based UAV position estimation in complex urban environments

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