Target Aircraft Research Articles

Aircraft target detection in SAR images based on MA-DETR

Aircraft target detection in SAR images based on MA-DETR

Military Aircraft Target Detection Algorithm Based on Improved YOLOv8s

Military Aircraft Target Detection Algorithm Based on Improved YOLOv8s

Issue of VHF Continuous Emission Radars Coordinate Measurement Discrepancy

The research delves into the significance of continuous emission radars of very high frequency (VHF) range in detecting small unmanned aircraft and marine targets for navigation safety. Recognising the need for improved radars and reduced coordinate measurement errors, the study aims to analyse discrepancies in azimuth and range determination in radars with fixed-phased antenna arrays. By developing effective tools with low computational complexity for target azimuth determination, the research seeks to enhance radar performance. Employing analytical, classification, functional, and statistical methods, the study comprehensively examines the peculiarities and differences of radars. It meticulously analyses coordinate measurement errors and investigates their causes. The impact of these discrepancies on radar performance and their relevance in various applications, particularly maritime navigation, is carefully evaluated. The findings emphasise the critical role of continuous emission radars in ensuring shipping safety and economic efficiency. The recommendations derived from the study offer valuable insights for improving radar effectiveness, addressing operational limitations, and enhancing overall functionality. By tackling coordinate measurement errors and providing accurate azimuth determination tools, this research contributes to advancing continuous emission radar technology and its practical applications. Through its findings and recommendations, the study aims to optimise radar performance, enhance navigation safety, and improve economic efficiency in diverse sectors.

End-to-end Remote Sensing Image Aircraft Target Detection and Fine-grained Recognition Framework

In view of the practical application of target detection and recognition tasks for remote sensing, an end-to-end aircraft target detection and fine-grained recognition framework is proposed. It can accurately and quickly implement detection and recognition in an end-to-end way. The main network of the framework adopts the design ideas of target detection and fine-grained recognition methods using candidate region extract and visual attention, making sure the accuracy of detection and recognition. Then, to solve the problem of high false detection rate and missed detection rate of densely arranged targets, we propose the re-detection mechanism. To minimize the large amounts of calculations of deep networks and improve real-time performance, we introduce depthwise separable convolution to optimize networks. Finally, a weight mapping idea based on transfer learning is adopted to solve the problem of data labeling and also helps the detection and fine-grained recognition. The results prove that the proposed framework has good robustness, versatility, and efficiency in aircraft detection and fine-grained recognition tasks.

On-orbit monitoring flying aircraft day and night based on SDGSAT-1 thermal infrared dataset

Aerial moving target detection is an important technology to ensure the safety of civil flight and airspace. Different from ground-based active radar and space-based visible imager, on-orbit thermal infrared sensor (TIS), which records the temperature difference between background and targets, is proved to be a stable means of monitoring the aircraft throughout the day. Limited by low spatial resolution and dataset scarcity, there are few existing the research of space-based aircraft detection based on TIS. In this paper, a new multi-model joint method to detect on-orbit flying aircrafts with only 1 × 1 to 2 × 2 pixels is proposed and validated. And the first three-band thermal infrared flying aircrafts dataset (TIFAD) with 30 m spacial resolution is produced from SDGSAT-1 thermal infrared images, including the analysis of its infrared feature. Since the point target cannot by distinguished by the shape, all samples are selected by an improved aircraft contrails detection algorithm based on progressive probabilistic Hough transform, from more than 325GB SDGSAT-1 TIS remote sensing images (RSIs). And training by TIFAD, state-of-the-art YOLOv8 algorithm is applied to more than 17,767 medium spatial resolution thermal infrared images, and the potential aircrafts (with contrails or non-contrails) in RSIs are effectively detected. Moreover, the giga floating point operations (GFLOPs) of detecting flying targets from RSIs with 300 km width range is only 8.9. This paper is believed to provide a technical support of detecting aircraft targets around the globe throughout the day, and supplement on-orbit flying targets dataset of TIS images.

Aircraft target type recognition technology based on deep learning and structure feature matching

In order to solve the problems with the traditional aircraft target type recognition algorithm, such as difficulty in feature selection, weak generalization ability, slow recognition speed, and low recognition accuracy, this paper put forward a new method that could detect and recognize aircraft targets in aerial images quickly and accurately. The aircraft targets in the images were detected rapidly and located through YOLOv3-tiny, and after image denoising, shadow detection, and positioning, then we used the Sobel operator to calculate the edge gradient of the target; the image of the aircraft target was segmented by using the region growth method, and then the principal component analysis (PCA)was used to obtain the central axis of the aircraft target. The projected distance from the edge contour to the central axis was sampled at equal intervals along the direction of the central axis, and its ratio to the length of the central axis was calculated to construct the feature vector. Finally, the Spearman rank correlation method was used to match the feature vectors to realize the recognition of the aircraft type. Experiments showed that the proposed method had strong adaptability and small computation and could quickly detect and accurately recognize aircraft targets in aerial images.

Satellite-Aerial Communications With Multi-Aircraft Interference

Satellite-aerial communication (SAC) is envisioned as a fundamental component of the sixth-generation (6G) wireless networks. Motivated by its importance, we investigate a SAC system including a geostationary satellite (S), a target aircraft (TA), and a set of interfering aircraft (IA). Specifically, TA sends signals to S in the presence of IA interference. Considering the trajectory, hierarchy, and safety distance of the aircraft’s flight routes, we propose a novel three-dimensional stacked Poisson line hardcore point process. That is, we introduce safety distances to the stacked Poisson line Cox process in order to describe the locations of IA in the sky. We also propose two approximations, namely, the equi-dense model and the discretization model, to maintain the tractability of the analysis. To this end, the uplink coverage probability is studied by using the two proposed mathematical models. Moreover, we investigate the coverage probability of the aviation use case with predefined flight altitudes. Finally, numerical results and Monte Carlo simulations are presented to validate the accuracy of the proposed analysis.

Reconstruction of target scattering centres based on characteristics analysis of inverse synthetic aperture radar images

AbstractDue to the aircraft targets’ uncooperative character, acquiring the complete scattering characteristics of enemy aircraft is challenging. An effective reconstruction method of target scattering centres based on characteristics analysis of inverse synthetic aperture radar (ISAR) images is proposed in this letter. First, a positive definite manifold characterizing ISAR images of different attitudes is constructed. Then, Kullback–Leibler divergence is used as a measure of the variability between ISAR images. Finally, an ISAR image interpolation method based on the characteristics analysis of ISAR images is proposed to obtain the scattering characteristics with more attitudes. Furthermore, simulation results confirm that the proposed method is effective.

Terminal attitude selection method of missile attack aircraft

The appropriate terminal attitude of missile is important to improve the damage effectiveness of fragmentation warhead against aircraft targets. In this paper, three missile terminal attitude selection methods are proposed to solve the problem of terminal attitude selection in different situations according to their respective evaluation indexes. The MVE-based method uses the damage probability of each detonation point around the aircraft at a given attitude of missile to calculate the Mean Volume of Effectiveness (MVE) at the corresponding attitude, and then uses the MVE for different attitudes to select the terminal strike attitude. The detonation position-based method addresses the case where the missile detonation position can be assessed in advance. Given the effects of missile guidance errors and fuze activation position errors, Monte Carlo simulations are used to calculate the damage probability of aircraft with different strike attitudes, from which the terminal strike attitude is selected. The BP-ANN model-based method uses the constructed Back Propagation Artificial Neural Network (BP-ANN) model instead of simulation to calculate the evaluation indexes in the corresponding cases of MVE-based method and Detonation position-based method, which can improve the efficiency of attitude selection. Simulations are conducted for different scenarios to verify the feasibility and effectiveness of the proposed method.

A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head

Synthetic Aperture Radar (SAR) image target detection is of great significance in civil surveillance and military reconnaissance. However, there are few publicly released SAR image datasets of typical non-cooperative targets. Aiming to solve this problem, a fast facet-based SAR imaging model is proposed to simulate the SAR images of non-cooperative aircraft targets under different conditions. Combining the iterative physical optics and the Kirchhoff approximation, the scattering coefficient of each facet on the target and rough surface can be obtained. Then, the radar echo signal of an aircraft target above a rough surface environment can be generated, and the SAR images can be simulated under different conditions. Finally, through the simulation experiments, a dataset of typical non-cooperative targets can be established. Combining the YOLOv5 network with the convolutional block attention module (CBAM) and another detection head, a SAR image target detection model based on the established dataset is realized. Compared with other YOLO series detectors, the simulation results show a significant improvement in precision. Moreover, the automatic target recognition system presented in this paper can provide a reference for the detection and recognition of non-cooperative aircraft targets and has great practical application in situational awareness of battlefield conditions.

Aircraft Target Interpretation Based on SAR Images

The focus of the current research is how to effectively identify aircraft targets in SAR images. There are only 2000 SAR images and 6556 aircraft instances in our dataset. SAR images have complex backgrounds, and the sizes of aircraft targets are multi-scale. How to improve the detection accuracy of aircraft targets is the research topic of this paper, especially for small target detection. We proposed four improved methods based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion. It is applied at the shallow layer of the backbone of YOLOv5s. It can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four decoupled detection heads to replace the original part in YOLOv5s. It can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multiple data-augmentation methods, which can enhance the diversity and generalization of the network. Finally, this paper proposed the K-means++ to replace the original K-means to improve the network convergence speed and detection accuracy. Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

CONSIDERATIONS FOR OPTIMIZING THE FLIGHT PERFORMANCE OF miniUAVs

Unmanned aerial vehicles benefit from a series of mature technologies that transform them into data acquisition, processing and dissemination tools with high levels of impact on areas of interest and great utility for users. The paper exposes the stages of analysis regarding the upgrade process of UAV (ATM-1) from the variant of equipping a target aircraft to a superior variant that can be used for data collection, from areas of interest, in the form of static or dynamic images. The proposed variant has implications both on the air vector balance and on the performance of use due to the new onboard equipment.

Aircraft Target Detection from Remote Sensing Images under Complex Meteorological Conditions

Taking all-day, all-weather airport security protection as the application demand, and aiming at the lack of complex meteorological conditions processing capability of current remote sensing image aircraft target detection algorithms, this paper takes the YOLOX algorithm as the basis, reduces model parameters by using depth separable convolution, improves feature extraction speed and detection efficiency, and at the same time, introduces different cavity convolution in its backbone network to increase the perceptual field and improve the model’s detection accuracy. Compared with the mainstream target detection algorithms, the proposed YOLOX-DD algorithm has the highest detection accuracy under complex meteorological conditions such as nighttime and dust, and can efficiently and reliably detect the aircraft in other complex meteorological conditions including fog, rain, and snow, with good anti-interference performance.

Double filter iterative ghost imaging for high quality edge and image acquisition.

Improving imaging quality and reducing time consumption are the key problems that need to be solved in the practical application of ghost imaging. Hence, we demonstrate a double filter iterative ghost imaging method, which adopts the joint iteration of projected Landweber iterative regularization and double filtering based on block matching three dimensional filtering and guided filtering to achieve high-quality image reconstruction under low measurement and low iteration times. This method combines the advantages of ill-posed problem solution of projected Landweber iterative regularization with double filtering joint iterative de-noising and edge preservation. The numerical simulation results show that our method outperforms the comparison method by 4 to 6 dB in terms of peak signal-to-noise ratio for complex binary target 'rice' and grayscale target 'aircraft' after 1500 measurements. The comparison results of experiments and numerical simulations using similar aircraft targets show that this method is superior to the comparison method, especially in terms of richer and more accurate edge detection results. This method can simultaneously obtain high quality reconstructed image and edge feature information under low measurement and iteration times, which is of great value for the practical application fields of imaging and edge detection at the same time, such as intelligent driving, remote sensing and other fields.

Assessment of global and local neural network’s performance for model-free estimation of flow angles

AbstractA synthetic flow angle sensor, able to estimate angle-of-attack and angle-of-sideslip, can exploit different methods to solve a set of equations modelling data fusion from other onboard systems. In operative scenarios, measurements used for data fusion are characterised by several uncertainties that would significantly affect the synthetic sensor performance. The off-line use of neural networks is not a novelty to model deterministic synthetic flow angle sensors and to mitigate issues arising from real flight applications. A common practice is to train the neural network with corrupted data that are representative of uncertainties of the current application. However, this approach requires accurate tuning on the target aircraft and extensive flight test campaigns, therefore, making the neural network tightly dependent on the specific aircraft. In order to overcome latter issues, this work proposes the use of neural networks to solve a model-free scheme, derived from classical flight mechanics, that is independent from the target aircraft, flight regime and avionics. It is crucial to make use of a training dataset that is not related to any specific aircraft or avionics to preserve the generality of the scheme. Under these circumstances, global and local neural networks are herein compared with an iterative method to assess the neural capabilities to generalise the proposed model-free solver. The final objective of the present work, in fact, is to select the neural technique that can enable a flow angle synthetic sensor to be used on board any flying body at any flight regime without any further training sessions.

Research on Individual Performance Index of Air Cluster Combat Aircraft Based on Differential Game Theory

UAV cluster has the characteristics of low cost, large-scale and high autonomy, and has gradually become a new research direction in the field of military intelligence, and is widely used in combat tasks such as penetration reconnaissance, lure interference, large-scale saturation attack and regional sealing and control. Therefore, air cluster confrontation will become one of the main melodies of air combat in the process of confrontation, cluster aircraft individual performance index determines the overall cluster level, poor individual performance will make the cluster cannot effectively complete the task, excellent individual performance completes the task, but will bring the increase in the cost of cluster, so in the air cluster confrontation scenario, according to the target performance reasonable our individual performance has become important. Based on the innovative differential game theory, this paper studies the effective interception of the aircraft in the cluster high dynamic pursuit scenario, studies the relationship between the individual performance of the interceptor aircraft and the target aircraft, and effectively provides reference for the overall index design of the aircraft in the military field.

Target Local Matching Recognition Algorithm based on Improved Harris Key Point Detection Operator

In the process of attacking fighters, helicopters and other targets, infrared imaging guided air-to-air missiles once they find themselves intercepted, they will release interference to induce the missile to deviate from the trajectory, which brings great difficulties to the missile to successfully destroy the target. When infrared interference occludes aircraft targets, a target local matching recognition algorithm based on the improved Harris key point detection operator is proposed. The algorithm first uses the Gaussian smoothing window to count the gradients in the x direction and y direction of each pixel, forms an autocorrelation feature matrix and calculates its feature values. Because smaller eigenvalues can be used to characterize the prominence of the curve, the minimum eigenvalue is used as the corner response function value, and the point when greater than the given threshold is marked as the key point of the aircraft target. In the process of interference occlusion of aircraft targets, the local matching recognition of targets is achieved by calculating the Euclidean distance of the feature descriptors between key points. Experimental results show that the proposed improved algorithm can achieve local matching recognition of infrared aircraft targets when infrared interference occludes aircraft targets.

SAR Image Aircraft Target Recognition Based on Improved YOLOv5

Synthetic aperture radar (SAR) is an active ground-surveillance radar system, which can observe targets regardless of time and weather. Passenger aircrafts are important targets for SAR, as it is of great importance for accurately recognizing the type of aircraft. SAR can provide dynamic monitoring of aircraft flights in civil aviation, which is helpful for the efficient management of airports. Due to the unique imaging characteristics of SAR, traditional target-detection algorithms have poor generalization ability, low detection accuracy, and a cumbersome recognition process. Target detection in high-resolution SAR images based on deep-learning methods is currently a major research hotspot. You Only Look Once v5 (YOLOv5) has the problems of missed detection and false alarms. In this study, we propose an improved version of YOLOv5. A multiscale feature adaptive fusion module is proposed to adaptively assign different weights to each scale of the feature layers, which can extract richer semantic and textural information. The SIOU loss function is proposed to replace the original CIOU loss function to speed up the convergence of the algorithm. The improved Ghost structure is proposed to optimize the YOLOv5 network to decrease the parameters of the model and the amount of computation. A coordinate attention (CA) module is incorporated into the backbone section to help extract useful information. The experimental results demonstrate that the improved YOLOv5 performs better in terms of detection without affecting calculation speed. The mean average precision (mAP) value of the improved YOLOv5 increased by 5.8% compared with the original YOLOv5.

Aircraft-LBDet: Multi-Task Aircraft Detection with Landmark and Bounding Box Detection

With the rapid development of artificial intelligence and computer vision, deep learning has become widely used for aircraft detection. However, aircraft detection is still a challenging task due to the small target size and dense arrangement of aircraft and the complex backgrounds in remote sensing images. Existing remote sensing aircraft detection methods were mainly designed based on algorithms employed in general object detection methods. However, these methods either tend to ignore the key structure and size information of aircraft targets or have poor detection effects on densely distributed aircraft targets. In this paper, we propose a novel multi-task aircraft detection algorithm. Firstly, a multi-task joint training method is proposed, which provides richer semantic structure features for bounding box localization through landmark detection. Secondly, a multi-task inference algorithm is introduced that utilizes landmarks to provide additional supervision for bounding box NMS (non-maximum suppression) filtering, effectively reducing false positives. Finally, a novel loss function is proposed as a constrained optimization between bounding boxes and landmarks, which further improves aircraft detection accuracy. Experiments on the UCAS-AOD dataset demonstrated the state-of-the-art precision and efficiency of our proposed method compared to existing approaches. Furthermore, our ablation study revealed that the incorporation of our designed modules could significantly enhance network performance.

Research on Aircraft Object Detection Based on Deep Learning

At present, the hot topic in academia is how to extract information in remote sensing images quickly. According to the existing methods, to identify the aircraft targets in remote sensing images more effectively, the RSOD-Dataset annotated by Wuhan University was selected and constructed into PASCAL VOC format in this paper. Faster R-CNN and YOLOv3 were used to test the dataset respectively, and their underlying network structures are studied and the results are evaluated using four evaluation metrics. It was found that the metrics obtained with YOLOv3 were Pr=94.72%, Re=80.25%, AP=89.45%, and mAP=89.45% while the metrics obtained with Faster R-CNN were Pr=49.00%, Re=68.06%, AP=60.59%, and mAP=60.59%. For the dataset of this paper, YOLOv3 can be used to identify aircraft targets in remote sensing images more effectively.