Mini UAVs detection by radar

Krátký, Miroslav; Fuxa, Lubos

doi:10.1109/miltechs.2015.7153647

Cited by 19 publications

(5 citation statements)

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“…conventional radars. The selection of the radar wavelength is very important, and certain conditions must be fulfilled, which in some cases do not correspond to real-life situations [3], [4]. In certain areas, such as airports, the operation of additional radars could be dangerous.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

et al. 2020

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“…In the existing approaches for 3D indoor localisation, hardware is required to achieve (H) dimension localisation, and an RSSI fingerprint recognition algorithm is required to achieve (X, Y) dimension localisation. In addition, existing hardware-based solutions, including inertial measurement units, UWB, vision systems [17], and radar [18], achieve indoor positioning with additional hardware and complex fusion algorithms. While these technologies already offer mature indoor positioning solutions, high-precision indoor positioning technologies incur various costs and require additional auxiliary equipment or extensive analogue processing.…”

Section: Referencementioning

confidence: 99%

Three-Dimensional Indoor Positioning Scheme for Drone with Fingerprint-Based Deep-Learning Classifier

Liu,

Lu,

Hwang

2024

Drones

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Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. Therefore, this study was aimed at developing a Wi-Fi-based three-dimensional (3D) indoor positioning scheme tailored to time-varying environments, involving human movement and uncertainties in the states of wireless devices. Specifically, we established an innovative 3D indoor positioning system to meet the localisation demands of UAVs in indoor environments. A 3D indoor positioning database was developed using a deep-learning classifier, enabling 3D indoor positioning through Wi-Fi technology. Additionally, through a pioneering integration of fingerprint recognition into wireless positioning technology, we enhanced the precision and reliability of indoor positioning through a detailed analysis and learning process of Wi-Fi signal features. Two test cases (Cases 1 and 2) were designed with positioning height intervals of 0.5 m and 0.8 m, respectively, corresponding to the height of the test scene for positioning simulation and testing. With an error margin of 4 m, the simulation accuracies for the (X, Y) dimension reached 94.08% (Case 1) and 94.95% (Case 2). When the error margin was 0 m, the highest simulation accuracies for the H dimension were 91.84% (Case 1) and 93.61% (Case 2). Moreover, 40 real-time positioning experiments were conducted in the (X, Y, H) dimension. In Case 1, the average positioning success rates were 50.8% (Margin-0), 72.9% (Margin-1), and 81.4% (Margin-2), and the corresponding values for Case 2 were 52.4%, 74.5%, and 82.8%, respectively. The results demonstrated that the proposed method can facilitate 3D indoor positioning based only on Wi-Fi technologies.

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“…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). In this, CP OFDM-based passive radar concept has been used.…”

Section: Cp Ofdm Based Conceptmentioning

confidence: 99%

“…The slots duration is in milliseconds, and also depends upon symbol time adjustment which is obtained by identifying PSS & SSS like synchronization signals (E. Parkvall, Dahlman, and Sköld. 2018;M Krátký and Fuxa 2015). These synchronization signals can be used for object detection and range estimation as seen in Section 5.4.4.…”

Section: Introduction To 5 G-nr Radarmentioning

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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Mini UAVs detection by radar

Abstract: The issues of using, and in recent time misusing of unmanned aerial vehicles is becoming the hot & topical problem. This paper deals with possible ways their detection, especially within the radar frequency spectrum.

Cited by 19 publications

References 0 publications

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

Three-Dimensional Indoor Positioning Scheme for Drone with Fingerprint-Based Deep-Learning Classifier

LTE-based passive radars and applications: a review

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