Aircraft Target Recognition Research Articles

An improved SSD lightweight network with coordinate attention for aircraft target recognition in scene videos

Accurate identification and monitoring of aircraft on the airport surface can assist managers in rational scheduling and reduce the probability of aircraft conflicts, an important application value for constructing a "smart airport." For the airport surface video monitoring, there are small aircraft targets, aircraft obscuring each other, and affected by different weather, the aircraft target clarity is low, and other complex monitoring problems. In this paper, a lightweight model network for video aircraft recognition in airport field video in complex environments is proposed based on SSD network incorporating coordinate attention mechanism. First, the model designs a lightweight feature extraction network with five feature extraction layers. Each feature extraction layer consists of two modules, Block_A and Block_I. The Block_A module incorporates the coordinate attention mechanism and the channel attention mechanism to improve the detection of obscured aircraft and to enhance the detection of small targets. The Block_I module uses multi-scale feature fusion to extract feature information with rich semantic meaning to enhance the feature extraction capability of the network in complex environments. Then, the designed feature extraction network is applied to the improved SSD detection algorithm, which enhances the recognition accuracy of airport field aircraft in complex environments. It was tested and subjected to ablation experiments under different complex weather conditions. The results show that compared with the Faster R-CNN, SSD, and YOLOv3 models, the detection accuracy of the improved model has been increased by 3.2%, 14.3%, and 10.9%, respectively, and the model parameters have been reduced by 83.9%, 73.1%, and 78.2% respectively. Compared with the YOLOv5 model, the model parameters are reduced by 38.9% when the detection accuracy is close, and the detection speed is increased by 24.4%, reaching 38.2fps, which can well meet the demand for real-time detection of aircraft on airport surfaces.

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.

Micro-Doppler Feature Extraction of Rotating Structures of Aircraft Targets with Terahertz Radar

The micro-Doppler features formed by the micro-motion of rotating blades of rotors and turbines are of great significance for aircraft target detection and recognition. Mastering the micro-motion features is the premise of radar target identification. The blades’ length and rotation rate are vital parameters for classifying aircraft targets. One can instantly judge the type and state of the targets by extracting micro-Doppler features. To extract the micro-Doppler features of rotating blades of the turbine target, we utilized microwave-band and terahertz-band radar to simulate the target and extract the Doppler frequency-shift information. For a turbine model with an obvious blade tip structure, we propose an algorithm based on wavelet coefficient enhancement and inverse Radon transform, integrating the time–frequency analysis with image processing. Under low SNR, this method allows for a high-accuracy parameter estimate. For a two-bladed rotor model without an obvious blade tip structure, we conducted an actual measurement experiment on the model utilizing a 120 GHz radar, and we propose a parameter estimation algorithm based on the fitting of the time–frequency distribution. By fitting the data of the time–frequency diagram, the micro-motion characteristic parameters of the rotor target were obtained. The simulation and experimental results demonstrate the benefits of terahertz radar in target detection, and indicate that the proposed algorithms have the characteristics of high extraction precision and insensitivity to noise.

Aircraft Target Recognition Based on Zero Polarization Feature Set

Aircraft Target Recognition Based on Zero Polarization Feature Set

Air Target Recognition Algorithm based on Mixed Depth Features in the Interference Environment

Infrared aircraft target recognition technologies under complex interference conditions have been a research hotspot. The development of infrared countermeasure technologies has made the air combat environment increasingly polymorphic, thus hindering the accurate recognition of infrared targets. In particular, a complex artificial interference can severely obscure target, resulting in the loss of continuity and saliency of target features, and the inability to fully describe the characteristics of a target object, thus making aerial target recognition inaccurate. To overcome this problem, we propose an anti-interference recognition algorithm for air targets based on mixed depth features. First, to improve the ability of the algorithm accurately in selecting candidate regions in a full trajectory, an improved fuzzy C-means (FCM) adaptive segmentation algorithm is developed to extract the target candidate region. Second, a deep convolutional feature (convolutional neural network) and the histogram of gradient feature are used to construct hybrid depth features for a more comprehensive representation of the target. Finally, to consider the diverse confrontation scenarios between the target and the interference in a combat scene, a three-class support vector machine model is proposed to deal with the target, interference, and interference adhesion states. The experimental results show that in a complex interference environment, the air target anti-interference recognition algorithm based on the mixed depth features exhibits an accuracy rate of 92.29%, which represents an accurate target anti-interference recognition.

Remote Aircraft Target Recognition Method Based on Superpixel Segmentation and Image Reconstruction

Satellite images are always with complex background and shadow areas. These factors can lead to target segmentation break up and recognition with a low accuracy. Aiming at solving these problems, we proposed an aircraft recognition method based on superpixel segmentation and reconstruction. First, we need to estimate the orientation of an aircraft by using histograms of oriented gradients. And then, an improved Simple Linear Iterative Cluster (SLIC) superpixel segmentation algorithm is provided. By comparing texture feature instead of color feature space, we cluster the pixels that are with the same features. Last, through target template images and orientation, we reconstruct the superpixels. Also, the lowest error matching ratio is the recognized target. The test results show that the algorithm is robust to noise and recognize more aircrafts. Especially, when the satellite images with complex background and shadow areas, our method recognizes accuracy better than other methods. It can satisfy the demand of satellite image aircraft recognition.

Robust and fast algorithm for extracting the periodic feature from jet engine modulation signals

Jet engine modulation (JEM), a modulation phenomenon induced by the rotating structure of jet engines, is a representative feature extracted from the radar returns for aircraft target recognition. As one of the most significant periodic characteristics of JEM, the chopping frequency plays an important role in aircraft target recognition. However, existing methods either need complicated logical algorithms to get an accurate estimation of the chopping frequency or exploit the chopping frequency as priori information for further feature extraction, and neither of these two scenarios is practical. Here, the authors propose a robust and fast algorithm for extracting the chopping frequency, which uses wavelet decomposition combined with autocorrelation to process the analytic form of the JEM signal. Application results of measured JEM signals demonstrate that the proposed algorithm is effective and practical for stable and accurate extraction of the periodic feature from JEM signals.

Research on Radar Micro-Doppler Feature Parameter Estimation of Propeller Aircraft

The micro-motion modulation effect of the rotated propellers to radar echo can be a steady feature for aircraft target recognition. Thus, micro-Doppler feature parameter estimation is a key to accurate target recognition. In this paper, the radar echo of rotated propellers is modelled and simulated. Based on which, the distribution characteristics of the micro-motion modulation energy in time, frequency and time-frequency domain are analyzed. The micro-motion modulation energy produced by the scattering points of rotating propellers is accumulated using the Inverse-Radon (I-Radon) transform, which can be used to accomplish the estimation of micro-modulation parameter. Finally, it is proved that the proposed parameter estimation method is effective with measured data. The micro-motion parameters of aircraft can be used as the features of radar target recognition.

Automatic target recognition system for unmanned aerial vehicle via backpropagation artificial neural network

PurposeThe purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network (BP-ANN) algorithm, with the objective of optimizing the structure of backpropagation network, to increase the efficiency and decrease the recognition time. A hardware-in-the-loop system for UAV target automatic recognition is also developed.Design/methodology/approachThe hybrid model of BP-ANN structure is established for aircraft automatic target recognition. This proposed method identifies controller parameters and reduces the computational complexity. Approaching speed of the network is faster and recognition accuracy is higher. This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment. with advantages of two networks and improves the speed and accuracy of identification. Finally, a hardware-in-the-loop system for UAV target automatic recognition is also developed.FindingsThe double hidden level backpropagation artificial neural can easily increase the speed of recognition process and get a good performance for recognition accuracy.Research limitations/implicationsThe proposed backpropagation artificial neural algorithm can be ANN easily applied to practice and can help the design of the aircraft automatic target recognition system. The standard backpropagation algorithm has some obvious drawbacks, namely, converging slowly and falling into the local minimum point easily. In this paper, an improved algorithm based on the standard backpropagation algorithm is constructed to make the aircraft target recognition more practicable.Originality/valueA double hidden levels backpropagation artificial neural algorithm is presented for automatic target recognition system of UAV.

A METHOD OF AIRCRAFT TARGET RECOGNITION BASED ON LLE AND HMM

Aiming to the problem of low recognition rate for aircraft targetrecognition caused by the change of postures, an aircraft targetrecognition based on locally linear embedding (LLE) and hiddenMarkov model (HMM) is proposed in this paper.

Robust and fast algorithm for estimating fundamental periodicity of jet engine modulation signals

Jet engine modulation (JEM), a modulation phenomenon induced by a rotating jet engine compressor, is a representative feature extracted from the radar for aircraft target recognition. In this study, the authors present a robust and fast algorithm for extracting the spool rate, which imparts fundamental periodicity to JEM signals. First, a wavelet decomposition with Meyer wavelet was applied to the analytic form of the JEM signal. Then, the decomposition components were combined to achieve a sufficient energy and minimise the mean of the JEM auto-correlation. Finally, the peak detection algorithm was employed for automatic estimation of the spool rate. Application results of measured JEM signals demonstrated that the proposed algorithm is effective for stable and accurate extraction of the jet engine information.

Aircraft recognition based on improved iterative threshold selection and skeleton Zernike moment

Using the visible optics images to identify targets is an important part in the development of remote sensing technology. In this paper, a new aircraft recognition method based on the improved iterative threshold selection and the skeleton Zernike moment is presented. The method segment aircraft targets under complex background using iterative threshold selection with between-class distance and scatter, and calculate the skeleton Zernike moment for the aircraft target recognition using template matching method. The experimental results show that the new method can effectively achieve the target segmentation under complex backgrounds, and provide a satisfactory performance both in recognition rate and recognition speed.

Classification of aircraft targets with low-resolution radars based on multifractal spectrum features

The multifractal spectrum characteristics of aircraft echoes from low-resolution radars offer a fine description of dynamic characteristics which induce the echo structure; therefore, they can provide a new way for aircraft target classification and recognition with low-resolution radars. On basis of introducing the scattering mechanism of aircraft echoes from low-resolution radars, the paper analyzed the multifractal characteristics of aircraft echoes as well as the extraction method of their multifractal spectrum features by means of the multifractal analysis of measures and proposed the classification method for different types of aircraft targets from the viewpoint of pattern recognition. The classification experiment results with real recorded echo data showed the multifractal spectrum feature-based classification method can classify different types of aircraft targets effectively, so the defined characteristic parameters could be used as effective features for aircraft target classification and recognition.

CLASSIFICATION OF AIRCRAFT TARGETS WITH SURVEILLANCE RADARS BASED ON FUZZY FRACTAL FEATURES

The fuzzy fractal characteristics of return signals from aircraft targets in conventional radars ofier a description of dynamic features which induce the echo structure of targets, therefore they can provide a new way for aircraft target classiflcation and recognition with low-resolution surveillance radars. On basis of introducing fuzzy fractal theory, the paper analyzes the fuzzy fractal characteristics of return signals from aircraft targets in a VHF-band surveillance radar by means of the fuzzy fractal analysis, and puts forward a fuzzy- fractal-feature-based classiflcation method for aircraft targets with a low-resolution radar from the viewpoint of pattern recognition. The analysis shows that the fuzzy fractal characteristic parameters such as the local fuzzy fractal dimension (LFFD) and local degree of fractality (LGF) can be used as efiective features for aircraft target classiflcation and recognition. The results of classiflcation experiments validate the proposed method.

A retrieval system from inverse synthetic aperture radar images: Application to radar target recognition

This paper presents an approach to aircraft target recognition using Inverse Synthetic Aperture Radar (ISAR) images. The goal of this work is to develop a robust algorithm to add Automated Target Recognition (ATR) capabilities to extract efficient feature vectors and ensure a comprehensive recognition process.In the first part on this paper, we used a database of ISAR images reconstructed from anechoic chamber simulations in order to extract efficient feature vectors. In the second part of this work, we proposed a recognition architecture to perform recognition tasks and provide a human operator with useful information for target recognition tasks.Several kinds of descriptor can be used to acquire information about target characteristics from radar signals and images. Indeed, a number of methods are currently used in automatic target recognition; notably based on ISAR imaging. However, target characteristic extraction from radar echoes remains a difficult task, and the methods are generally specific to either aircraft or ship recognition. In this work, we describe our approach to designing faster, more effective retrieval systems and a comprehensive architecture. Our approach uses global feature vectors for both ship and aircraft recognition.Firstly, the global feature vectors are described by two types of descriptor which have shown to be efficient in radar target recognition. The first type defines target shape obtained by watershed transformation, facilitating interpretation for the human operator. The second type of vector descriptor is based on so-called polar signatures, obtained using the polar mapping procedure. The latter are highly discriminative and thus significantly improve and facilitate target recognition, leading to greater precision and accuracy. Secondly, we describe the retrieval architecture suitable for the second type of vector descriptor, which checks invariance in relation to target rotation and scale. It is, in itself, more efficient and processing time at the retrieval step is reduced and controlled by the human operator. Finally, in order to validate our proposed feature vectors and architecture, the Support Vector Machine (SVM) classifier will be implemented; the results of which will be tested and presented in the last section of this paper.

TARGET CLASSIFICATION WITH LOW-RESOLUTION SURVEILLANCE RADARS BASED ON MULTIFRACTAL FEATURES

The multifractal characteristics of return signals from aircraft targets in conventional radars ofier a flne description of dynamic characteristics which induce the targets' echo structure; therefore they can provide a new way for aircraft target classiflcation and recognition with low-resolution surveillance radars. On basis of introducing the mathematical model of return signals from aircraft targets in conventional radars, the paper analyzes the multifractal characteristics of the return signals as well as the extraction method of their multifractal features by means of the multifractal analysis of measures, and puts forward a multifractal-feature-based classiflcation method for three types of aircraft targets (including jet aircrafts, propeller aircrafts and helicopters) from the viewpoint of pattern classiflcation. The analysis shows that the conventional radar return signals from the three types of aircraft targets have signiflcantly difierent multifractal characteristics, and the deflned characteristic parameters can be used as efiective features for aircraft target classiflcation and recognition. The results of classiflcation experiments validate the proposed method.

Automatic Aircraft Target Recognition by ISAR Image Processing based on Neural Classifier

This work proposes a new automatic target classifier, based on a combined neural networks' system, by ISAR image processing. The novelty introduced in our work is twofold. We first present a novel automatic classification procedure, and then we discuss an improved multimedia processing of ISAR images for automatic object detection. The classifier, composed by a combination of 20 feed-forward artificial neural networks, is used to recognize aircraft targets extracted from ISAR images. A multimedia processing by two recently introduced image processing techniques is exploited to improve the shape and features extraction process. Performance analysis is carried out in comparison with conventional multimedia techniques and standard detectors. Numerical results obtained from wide simulation trials evidence the efficiency of the proposed method for the application to automatic aircraft target recognition.

Microwave imaging of aircraft

Three methods of imaging aircraft from the ground with microwave radar with quality suitable for aircraft target recognition are described. The imaging methods are based on a self-calibration procedure called adaptive beamforming that compensates for the severe geometric distortion inherent in any imaging system that is large enough to achieve the high angular resolution necessary for two-dimensional target imaging. The signal processing algorithm is described and X-band (3-cm)-wavelength experiments demonstrate its success on commercial aircraft flying into Philadelphia International Airport.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Facilitation of Aircraft Recognition by Selective Attentional Allocation

When attempting to recognize a potential airborne target in a tactical setting, a field commander is confronted with a variety of variables that may affect the quality of the recognition process. Many of these variables; such as target distance, contrast, and optical countermeasures, are perceptual in nature. Others, like command and control oriented information, are more cognitively based. The present experiments explored the effects of selective attentional allocation on aircraft target recognition. This was done by manipulating a variety of factors that one might expect to be present in a tactical command and control scenario. The major intent of these experiments was the achievement of insight into the nature of the internal representation upon which aircraft recognition is based and the degree to which subjects can alter attentional algorithms in order to faciliate stimulus processing. Two alternative accounts of the nature of the internal representation were evaluated. The template model assumes that the representation consists of a template of the target to be recognized. When exposed to a display, the subject maps the display elements onto the template and responds on the basis of the degree of similarity between the display and the internal template. Proponents of this global view assume that processing takes place in a top-down manner (Navon, 1977). The feature model, on the other hand, assumes that recognition is based on a scan through a list of criterial features. Upon encountering a display the subject extracts a list of such features and compares these to a list held in memory. Evidence for such a bottom-up processing algorithm has been advanced by Hoffman (1980). The experiments were carried out within the context of a two alternative forced-choice paradigm. Subjects viewed a series of displays consisting of silhouettes of two aircraft. One of these was designated as the target (either a MIG-23 or an FB-111). The other silhouette served as a distractor (A-10, F-14, MIG-25 or SU-20). All stimuli were presented on a three channel tachistoscope and subject's responses were recorded with respect to both recognition accuracy and reaction times. Several variables were manipulated over the course of several experiments; for instance, the contrast of the displays, the presence and type of cue preceeding a stimulus display, the orientation of the silhouettes in each display, and the stimulus onset asynchrony of cue and display. The specific effects of each of these variables on aircraft silhouette recognition performance will be discussed. Briefly, data indicated that recognition performance degrades under reduced contrast conditions; performance differs depending on from which orientation the display is viewed; and the type of pre-exposure cueing affects the speed of recognition. In addition to this, several interesting interactions among these variables occurred. These variables, and their effects on aircraft recognition, will be discussed within the context of the nature of the internal representation and the use of selective attentional algorithms by the subject. Subjects apparently were not limited to a single attentional algorithm. Rather, several strategies were potentially available (Dixon, 1981). Subjects altered these processing strategies depending on target orientation, the nature of pre-exposure cueing, and the time available for selection and preparation of an algorithm.