• ETRI Journal
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• v.41 no.6
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• pp.750-759
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• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.491-498
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• 2015

Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. A novel method based on singular value decomposition (SVD) and the main ridge slice of ambiguity function (AF) is presented for attaining a higher correct recognition rate of radar emitter signals in case of low signal-to-noise ratio. This method calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, SVD is employed to eliminate the influence of noise on the main ridge slice envelope. The rotation angle and symmetric Holder coefficients of the main ridge slice envelope are extracted as the elements of the feature vector. And kernel fuzzy c-means clustering is adopted to analyze the feature vector and classify different types of radar signals. Simulation results indicate that the feature vector extracted by the proposed method has satisfactory aggregation within class, separability between classes, and stability. Compared to existing methods, the proposed feature recognition method can achieve a higher correct recognition rate.

• ETRI Journal
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• v.43 no.6
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• pp.991-1003
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• 2021

Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM-CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at -2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4808-4824
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• 2016

Signal individual reconnaissance and identification is an extremely important research topic in non-cooperative domains such as electronic countermeasures and intelligence reconnaissance. Facing the characteristics of the complexity and changeability of current communication environment, how to realize radiation source signal individual identification under the low SNR conditions is an emphasis of research. A novel emitter individual identification method combined bi-spectrum analysis with wavelet feature is presented in this paper. It makes a feature fusion of bi-spectrum slice characteristics and energy variance characteristics of the secondary wavelet transform coefficient to identify MFSK signals under the low SNR (signal-to-noise ratios) environment. Theoretical analyses and computer simulation results show that the proposed algorithm has good recognition performance with the ability to suppress noise and interference, and reaches the recognition rate of more than 90% when the SNR is -6dB.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.644-647
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• 1996

In this study the theoretical minimum resolution analysis of an active vision system using laser range finder is performed for surrounding recognition and 3D data acquisition in unknown environment. The laser range finder consists of a slitted laser beam generator, a scanning mechanism, CCD camera, and a signal processing unit. A laser beam from laser source is slitted by a set of cylindrical lenses and the slitted laser beam is emitted up and down and rotates by the scanning mechanism. The image of laser beam reflected on the surface of an object is engraved on the CCD array. In the result, the resolution of range data in laser range finder is depend on distance between lens center of CCD camera and light emitter, view and beam angles, and parameters of CCD camera.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.82-87
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• 1998

In this study, the theoretical resolution analysis of an active vision system using laser range finder is performed for surrounding recognition and 3D data acquisition in unknown environment. In the result, the resolution of range data in laser range finder is depend on the distance between lens center of CCD camera and light emitter, view angle, beam angle, and parameters of CCD camera. The theoretical resolutions of the laser range finders of various types which are based on parameters effected resolution are calculated and experimental results are obtained in real system.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.43-50
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• 2021

In this paper, we design and implement KITNESS, a windows application that feeds back the accuracy of fitness motions based on Kinect sensors. The feature of this application is to use Kinect's camera and joint recognition sensor to give feedback to the user to exercise in the correct fitness position. At this time, the distance between the user and the Kinect is measured using Kinect's IR Emitter and IR Depth Sensor, and the joint, which is the user's joint position, and the Skeleton data of each joint are measured. Using this data, a certain distance is calculated for each joint position and posture of the user, and the accuracy of the posture is determined. And it is implemented so that users can check their posture through Kinect's RGB camera. That is, if the user's posture is correct, the skeleton information is displayed as a green line, and if it is not correct, the inaccurate part is displayed as a red line to inform intuitively. Through this application, the user receives feedback on the accuracy of the exercise position, so he can exercise himself in the correct position. This application classifies the exercise area into three areas: neck, waist, and leg, and increases the recognition rate of Kinect by excluding positions that Kinect does not recognize due to overlapping joints in the position of each exercise area. And at the end of the application, the last exercise is shown as an image for 5 seconds to inspire a sense of accomplishment and to continuously exercise.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.80-85
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• 2015

In this paper, we present, an indstrial RFID layered microstrip patch antenna is designed using an inset feed method in order to improve recognition rates in a long distance as tags are attached to metal object by improving a problem of feeding power in fabricating metal tags and reducing effects of metallic object. The inset feed shows a distinctive characteristic that has no separation between emitters and feed lines differing from a structure with the conventional inductive coupling feed. This structure makes possible to produce a type that presents a low antenna height and enables impedance coupling for tag chips. Although it shows a difficulty in the impedance coupling due to increases in the parasite capacitance between a ground plane and an emitter in an antenna according to decreases in the height of a tag antenna, it may become a merit in designing the tag antenna because the antenna impedance can be determined as an inductive manner if a shorted structure is used for feeding power. Therefore, in this paper the microstrip patch antenna is designed as a modified type and applies the inset feed in order to reduce effects of metallic objects where the antenna is be attached. Also, the antenna uses a multi-layer structure that includes a metal plate between radiator and ground instead of using a single layer.