Cooperative polynomial guidance law with collision avoidance and flight path angle coordination

Chen, Sai; Ma, Dongying; Yao, Yuhan; Wang, Xiaodong; Li, Chaoyong

doi:10.1016/j.ast.2022.107809

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Different types of drones have different flying speeds and heights, making it necessary to identify them to monitor and manage airspace. Once the UTM system recognizes the drone types, it can plan flight routes for cooperative drones [25,26] and process uncooperative drones. Note that radar may become the vital sensor in future UTM systems as it can provide active detection and sensing intelligence.…”

Section: Discussionmentioning

confidence: 99%

Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types

Yan

Hu²,

Gong

et al. 2023

Drones

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In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar.

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Section: Discussionmentioning

confidence: 99%

Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types

Yan

Hu²,

Gong

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…Compared with the literature relying on GPS information [9,36,37] or MCS [38][39][40] for positioning, the method in this study uses only a range sensor and its odometer to achieve navigation and positioning at a minimum deployment cost. Compared with relative positioning based on visual search, the method here has wider-range measurements, shorter searching times and requires fewer computing resources.…”

Section: Remarkmentioning

confidence: 99%

A Unmanned Aerial Vehicle (UAV)/Unmanned Ground Vehicle (UGV) Dynamic Autonomous Docking Scheme in GPS-Denied Environments

Cheng,

Li,

Xie

et al. 2023

Drones

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This study designs a navigation and landing scheme for an unmanned aerial vehicle (UAV) to autonomously land on an arbitrarily moving unmanned ground vehicle (UGV) in GPS-denied environments based on vision, ultra-wideband (UWB) and system information. In the approaching phase, an effective multi-innovation forgetting gradient (MIFG) algorithm is proposed to estimate the position of the UAV relative to the target using historical data (estimated distance and relative displacement measurements). Using these estimates, a saturated proportional navigation controller is developed, by which the UAV can approach the target, making the UGV enter the field of view (FOV) of the camera deployed in the UAV. Then, a sensor fusion estimation algorithm based on an extended Kalman filter (EKF) is proposed to achieve accurate landing. Finally, a numerical example and a real experiment are used to support the theoretical results.

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