FADS based aerodynamic parameters estimation for mars entry considering fault detection and tolerance

Jiang, Xiuqiang; Li, Shuang; Gu, Long; Li, Maodeng; Ji, Yuandong

doi:10.1016/j.actaastro.2020.11.046

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…A S UAVs are widely used in remote sensing imagery, agricultural rescue, disaster relief, video shooting, disaster surveillance, industrial target detection [1], detection of small targets in UAV capture scenes is gradually becoming a recent popular task. UAVs detect a large percentage of small targets and can provide limited resolution [2]. One aspect poses a great challenge to target recognition due to the realistic capture, where there are occlusions and overlaps between targets, and due to the high speed and low altitude flight of UAVs that produce motion blur so that the imaging images can be used with fewer features.…”

Section: Introductionmentioning

confidence: 99%

UAV Target Detection Algorithm Based on Improved YOLOv8

Wang,

Qin

et al. 2023

IEEE Access

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Since UAVs usually fly at higher altitudes, resulting in a more significant proportion of small targets after imaging, this poses a challenge to the target detection algorithm at this stage; in addition, the high-speed flight of UAVs causes a sense of blurring on the detected objects, which leads to difficulties in target feature extraction. To address the two problems presented above, we propose a UAV target detection algorithm based on improved YOLOv8. First, the small target detection structure (STC) is embedded in the network, which acts as a bridge between shallow and deep features to improve the collection of semantic information of small targets and enhance detection accuracy. Second, using the feature of global information of UAV imaging-focused targets, the global attention GAM is introduced to the bottom layer of YOLOv8m's backbone to prevent the loss of image feature information during sampling and thus increase the algorithm's detection performance by feeding back feature information of different dimension. The modified model effectively increases the detection of tiny targets with an mAP value of 39.3%, which is 4.4% higher than the baseline approach, according to experimental results on the VisDrone2021 dataset, and outperforms mainstream algorithms such as SSD and YOLO series, effectively increasing the detection performance of UAVs for small targets.INDEX TERMS UAV target detection; Global attention mechanism; Small target detection

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Section: Introductionmentioning

confidence: 99%

UAV Target Detection Algorithm Based on Improved YOLOv8

Wang,

Qin

et al. 2023

IEEE Access

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Large Angle of Attack Prediction for Tail-Sitter Using ANN-Based Flush Air Data Sensing

Tianchun

et al. 2023

Lecture Notes in Electrical Engineering

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Flush Airdata System on a Flying Wing Based on Machine Learning Algorithms

et al. 2023

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By using an array of pressure sensors distributed on the surface of an aircraft to measure the pressure of each port, the flush airdata sensing (FADS) system is widely applied in many modern aircraft and unmanned aerial vehicles (UAVs). Normally, the pressure transducers of the FADS system should be mounted on the leading edge of the aircraft, where they are sensitive to changes in pressure. For UAVs, however, the leading edge of the nose and wing may not be available for pressure transducers. In addition, the number of transducers is limited to 8–10, making it difficult to maintain accuracy in the normal method for FADS systems. An FADS system model for an unmanned flying wing was developed, and the pressure transducers were all located outside the regions of the leading edge areas. The locations of the transducers were selected by using the mean impact value (MIV), and ensemble neural networks were developed to predict the airdata with a very limited number of transducers. Furthermore, an error detection method was also developed based on artificial neural networks and random forests. The FADS system model can accurately detect the malfunctioning port and use the correct pressure combination to predict the Mach number, angle of attack, and angle of sideslip with high accuracy.

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FADS based aerodynamic parameters estimation for mars entry considering fault detection and tolerance

Cited by 3 publications

References 21 publications

UAV Target Detection Algorithm Based on Improved YOLOv8

UAV Target Detection Algorithm Based on Improved YOLOv8

Large Angle of Attack Prediction for Tail-Sitter Using ANN-Based Flush Air Data Sensing

Flush Airdata System on a Flying Wing Based on Machine Learning Algorithms

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