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According to the different concentration levels of Doppler spectrum between sea clutter and target, small target in sea clutter background can be detected using Shannon entropy. However, Shannon entropy is merely a special case of Tsallis entropy and cannot reflect the multifractality of sea clutter. In this paper, the relation between Tsallis entropy and the generalized fractal dimension is first presented, and then the Doppler spectrum’s concentrative level and multifractality of sea clutter are combined; finally an algorithm for detecting small target in sea clutter background based on Tsallis entropy of Doppler spectrum rather than of Shannon entropy is proposed. By comparison via IPIX dataset, the detection’s performance of Tsallis entropy is better than that of Shannon entropy and Hurst exponent as per short observations. According to the different concentration levels of Doppler spectrum between sea clutter and target, small target in sea clutter background can be detected using Shannon entropy. However, Shannon entropy is merely a special case of Tsallis entropy and cannot reflect the multifractality of sea clutter. In this paper, the relation between Tsallis entropy and the generalized fractal dimension is first presented, and then the Doppler spectrum’s concentrative level and multifractality of sea clutter are combined; finally an algorithm for detecting small target in sea clutter background based on Tsallis entropy of Doppler spectrum rather than of Shannon entropy is proposed. By comparison via IPIX dataset, the detection’s performance of Tsallis entropy is better than that of Shannon entropy and Hurst exponent as per short observations.
Abstract(64)
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For collocated Multiple-Input Multiple-Output (MIMO) radar, we investigate the target detection problem in Gaussian clutter with an unknown but random covariance matrix. An inverse complex Wishart distribution is chosen as prior knowledge for the random covariance matrix. We propose two detectors in the Bayesian framework based on the criteria of the Generalized Likelihood Ratio Test. The two main advantages of the proposed Bayesian detectors are as follows: (1) no training data are required; and (2) a prior knowledge about the clutter is incorporated in the decision rules to achieve detection performance gains. Numerical simulations show that the proposed Bayesian detectors outperform the current commonly used non-Bayesian counterparts, particularly when the sample number of the transmitted waveform is small. In addition, the performance of the proposed detector will decline in parameter mismatched situation. For collocated Multiple-Input Multiple-Output (MIMO) radar, we investigate the target detection problem in Gaussian clutter with an unknown but random covariance matrix. An inverse complex Wishart distribution is chosen as prior knowledge for the random covariance matrix. We propose two detectors in the Bayesian framework based on the criteria of the Generalized Likelihood Ratio Test. The two main advantages of the proposed Bayesian detectors are as follows: (1) no training data are required; and (2) a prior knowledge about the clutter is incorporated in the decision rules to achieve detection performance gains. Numerical simulations show that the proposed Bayesian detectors outperform the current commonly used non-Bayesian counterparts, particularly when the sample number of the transmitted waveform is small. In addition, the performance of the proposed detector will decline in parameter mismatched situation.
Abstract(35)
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This paper focuses on an improved imaging-algorithm for the spotlight Synthetic Aperture Radar (spotlight SAR) with continuous Pulse Repetition Frequency (PRF) variation in extremely high-resolution imaging-process. PRI variation is conventionally employed to resolve the problem of fixed blind ranges as well as the conflict of high-resolution and wide-swath; however, there are problems such as spectrum aliasing and ambiguous targets caused by nonuniform sampling. In this study, a novel sinc interpolation method is proposed to reconstruct a uniformly sampled signal from non-uniform Fourier Transform samples. Then a two-step processing approach combined with the novel sinc interpolation method is presented in the process of non-uniformly sampled echo imaging. The simulation proves the validity and accuracy of the proposed imaging algorithm. In addition, the computational cost of the novel sinc interpolation is further reduced compared to that of non-uniform Fourier transformation. This paper focuses on an improved imaging-algorithm for the spotlight Synthetic Aperture Radar (spotlight SAR) with continuous Pulse Repetition Frequency (PRF) variation in extremely high-resolution imaging-process. PRI variation is conventionally employed to resolve the problem of fixed blind ranges as well as the conflict of high-resolution and wide-swath; however, there are problems such as spectrum aliasing and ambiguous targets caused by nonuniform sampling. In this study, a novel sinc interpolation method is proposed to reconstruct a uniformly sampled signal from non-uniform Fourier Transform samples. Then a two-step processing approach combined with the novel sinc interpolation method is presented in the process of non-uniformly sampled echo imaging. The simulation proves the validity and accuracy of the proposed imaging algorithm. In addition, the computational cost of the novel sinc interpolation is further reduced compared to that of non-uniform Fourier transformation.
Abstract(65)
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This study investigates the influence of the target polarization scattering characteristics on the detection performance of Polarization Diversity Radar (PDR) system with three typical working modes: full polarization mode, mixed polarization mode, and single polarization mode. The statistical properties of target echo signal corresponding to the three modes are separately deduced based on the statistical model of target polarization scattering and the receiving voltage equations. Furthermore, the optimal polarization diversity multi-channel fusion detection algorithm is designed under the Neyman-Pearson criterion in the background of Gaussian distribution, and closed-form expressions of probability of false alarm and detection are derived. The simulation results show that the correlations between the polarization scattering components of the target, particularly the correlations between the matched and cross polarization scattering components of the target, have greater effect on the detection performance of PDR for a given system Signal-to-Noise Ratio (SNR). In addition, the detection performance of the full and single polarization modes is more robust than that of the mixed polarization mode. This study investigates the influence of the target polarization scattering characteristics on the detection performance of Polarization Diversity Radar (PDR) system with three typical working modes: full polarization mode, mixed polarization mode, and single polarization mode. The statistical properties of target echo signal corresponding to the three modes are separately deduced based on the statistical model of target polarization scattering and the receiving voltage equations. Furthermore, the optimal polarization diversity multi-channel fusion detection algorithm is designed under the Neyman-Pearson criterion in the background of Gaussian distribution, and closed-form expressions of probability of false alarm and detection are derived. The simulation results show that the correlations between the polarization scattering components of the target, particularly the correlations between the matched and cross polarization scattering components of the target, have greater effect on the detection performance of PDR for a given system Signal-to-Noise Ratio (SNR). In addition, the detection performance of the full and single polarization modes is more robust than that of the mixed polarization mode.
Abstract(42)
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Abstract(66)
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In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) image classification has been investigated extensively. The traditional PolSAR image terrain classification methods result in a weak feature representation. To overcome this limitation, this study aims to propose a terrain classification method based on deep Convolutional Neural Network (CNN) and Conditional Random Field (CRF). The pre-trained VGG-Net-16 model was used to extract more powerful image features, and then the terrain from the images was classified through the efficient use of multiple features and context information by conditional random fields. The experimental results show that the proposed method can extract more features effectively in comparison with the three methods using traditional classical features and it can also achieve a higher overall accuracy and Kappa coefficient. In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) image classification has been investigated extensively. The traditional PolSAR image terrain classification methods result in a weak feature representation. To overcome this limitation, this study aims to propose a terrain classification method based on deep Convolutional Neural Network (CNN) and Conditional Random Field (CRF). The pre-trained VGG-Net-16 model was used to extract more powerful image features, and then the terrain from the images was classified through the efficient use of multiple features and context information by conditional random fields. The experimental results show that the proposed method can extract more features effectively in comparison with the three methods using traditional classical features and it can also achieve a higher overall accuracy and Kappa coefficient.
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2019, 8(1): 1-16.
Abstract(48)
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2019, 8(1): 17-24.
Abstract(71)
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Resolution performance is an important performance criteria of the radar systems. Typically, the Ambiguity Function (AF) of signals is used to define the range and Doppler limits. In this study Some new opinions are proposed—First, the AF is based on the signals processed with matched filter, which can guarantee the maximization of the output of the Signal-to-Noise Ratio (SNR). Thus, the AF is optimal for target detection. However, the AF is unsuitable for the resolution of multiple targets. Second, the AF cannot reflect the effect of random factors, such as noise, target fluctuation, and mutual interference of close targets. Third, the AF can only handle two equal-powered targets and provide the conclusion of the limits. However, the AF fails to distinguish multiple unequal-powered targets, which is often the case in reality. Therefore, the hypothesis testing theory is applied to resolve the range resolution of two closely spaced targets for radars, and our study is based on the original echoes of the signals. With the definition of the correct resolution and false alarm rates in the statistical standpoint, we derive the expression of the range Statistical Resolution Limit (SRL). The simulation results indicate that the SRL can exceed the Rayleigh limit. With the false alarm and correct resolution rates being 0.001 and 0.5, respectively, for the two uncorrelated-amplitude linear-frequency-modulated signals, the range SRL can be as low as 0.3 times of the Rayleigh limit. Resolution performance is an important performance criteria of the radar systems. Typically, the Ambiguity Function (AF) of signals is used to define the range and Doppler limits. In this study Some new opinions are proposed—First, the AF is based on the signals processed with matched filter, which can guarantee the maximization of the output of the Signal-to-Noise Ratio (SNR). Thus, the AF is optimal for target detection. However, the AF is unsuitable for the resolution of multiple targets. Second, the AF cannot reflect the effect of random factors, such as noise, target fluctuation, and mutual interference of close targets. Third, the AF can only handle two equal-powered targets and provide the conclusion of the limits. However, the AF fails to distinguish multiple unequal-powered targets, which is often the case in reality. Therefore, the hypothesis testing theory is applied to resolve the range resolution of two closely spaced targets for radars, and our study is based on the original echoes of the signals. With the definition of the correct resolution and false alarm rates in the statistical standpoint, we derive the expression of the range Statistical Resolution Limit (SRL). The simulation results indicate that the SRL can exceed the Rayleigh limit. With the false alarm and correct resolution rates being 0.001 and 0.5, respectively, for the two uncorrelated-amplitude linear-frequency-modulated signals, the range SRL can be as low as 0.3 times of the Rayleigh limit.
2019, 8(1): 25-35.
Abstract(129)
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Existing track-to-track association methods are mainly based on statistics and fuzzy mathematics. However, most methods based on statistics depend on thresholds, and parameters based on fuzzy mathematics are complex to set. In addition, most methods only consider the information of a single track point in comparison. To solve the existing problems, this paper presents a distance distribution histogram feature to extract the similarity features of a trajectory and measure them using the standardized Euclidean distances; this method effectively utilizes the characteristics of the whole trajectory and has a good anti-noise performance and accuracy. The motion features of ships and the location accuracy of different data sources were fully considered. After obtaining the histogram features of velocity difference and the source features of sensors, the authors combined them and trained association models using machine learning, which effectively avoids the problem of manually setting thresholds and complex parameter settings. Finally, a real ship data set was constructed. The experimental results show that compared with the traditional distance feature, the overall association accuracy was improved by 3.23%～11.65% using the distance distribution histogram feature, and by 0.068% using the combination feature, which verifies the effectiveness of the proposed method. Existing track-to-track association methods are mainly based on statistics and fuzzy mathematics. However, most methods based on statistics depend on thresholds, and parameters based on fuzzy mathematics are complex to set. In addition, most methods only consider the information of a single track point in comparison. To solve the existing problems, this paper presents a distance distribution histogram feature to extract the similarity features of a trajectory and measure them using the standardized Euclidean distances; this method effectively utilizes the characteristics of the whole trajectory and has a good anti-noise performance and accuracy. The motion features of ships and the location accuracy of different data sources were fully considered. After obtaining the histogram features of velocity difference and the source features of sensors, the authors combined them and trained association models using machine learning, which effectively avoids the problem of manually setting thresholds and complex parameter settings. Finally, a real ship data set was constructed. The experimental results show that compared with the traditional distance feature, the overall association accuracy was improved by 3.23%～11.65% using the distance distribution histogram feature, and by 0.068% using the combination feature, which verifies the effectiveness of the proposed method.
2019, 8(1): 36-43.
Abstract(284)
HTML(263) 873KB(113)
The rapid advances in positioning technology have created huge spatio-temporal trajectory data, and there are always obvious aberrant outliers in trajectory data. Detecting outliers in the trajectory is critical to improving data quality and the accuracy of subsequent trajectory data mining tasks. In this paper, we propose a trajectory outlier detection algorithm based on a Bidirectional Long Short-Term Memory (Bi-LSTM) model. First, a six-dimensional motion feature vector is extracted for each trajectory point, and then we construct a Bi-LSTM model. The model input is the trajectory data feature vector of a certain sequence length, and its output is the class type of the current track point. In addition, a combination method of undersampling and oversampling is applied to mitigate the effect of data distribution imbalance on detection performance. The Bi-LSTM model can automatically learn the difference between the normal points and adjacent abnormal points in the motion characteristics by combining the LSTM unit and the bidirectional network. Experimental results based on a real ship trajectory annotation data show that the detection performance of our proposed algorithm significantly exceeds those of the constant velocity threshold algorithm, non-sequential classical machine learning classification algorithms, and convolutional neural network model. Especially, the recall value of the proposed algorithm reaches 0.902, which verifies its effectiveness. The rapid advances in positioning technology have created huge spatio-temporal trajectory data, and there are always obvious aberrant outliers in trajectory data. Detecting outliers in the trajectory is critical to improving data quality and the accuracy of subsequent trajectory data mining tasks. In this paper, we propose a trajectory outlier detection algorithm based on a Bidirectional Long Short-Term Memory (Bi-LSTM) model. First, a six-dimensional motion feature vector is extracted for each trajectory point, and then we construct a Bi-LSTM model. The model input is the trajectory data feature vector of a certain sequence length, and its output is the class type of the current track point. In addition, a combination method of undersampling and oversampling is applied to mitigate the effect of data distribution imbalance on detection performance. The Bi-LSTM model can automatically learn the difference between the normal points and adjacent abnormal points in the motion characteristics by combining the LSTM unit and the bidirectional network. Experimental results based on a real ship trajectory annotation data show that the detection performance of our proposed algorithm significantly exceeds those of the constant velocity threshold algorithm, non-sequential classical machine learning classification algorithms, and convolutional neural network model. Especially, the recall value of the proposed algorithm reaches 0.902, which verifies its effectiveness.
2019, 8(1): 44-53.
Abstract(101)
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With the continuous advancement of modern technology, more types of radar and related technologies are continuously being developed, and the identification of radar emitter signals has gradually become a very important research field. This paper focuses on the identification of modulation types in radar emitter signal identification. We propose a weighted normalized Singular-Value Decomposition (SVD) feature extraction algorithm, which is based on the perspective of data energy and SVD. The filtering effect of complex SVD is analyzed, as well as the influence of the number of rows of data matrix on the decomposition results, and the recognition effect of different classification models. The experimental results show that the algorithm has better filtering and recognition effects on common radar signals. Under –20 dB, the cosine similarity values of the reconstructed and original signals remain at about 0.94, and the recognition accuracy remains above 97% under a confidence level \begin{document}$\alpha$\end{document} of 0.65. In addition, experiments show that the weighted normalized SVD feature extraction algorithm has better robustness than the traditional Principal Component Analysis (PCA) algorithm. With the continuous advancement of modern technology, more types of radar and related technologies are continuously being developed, and the identification of radar emitter signals has gradually become a very important research field. This paper focuses on the identification of modulation types in radar emitter signal identification. We propose a weighted normalized Singular-Value Decomposition (SVD) feature extraction algorithm, which is based on the perspective of data energy and SVD. The filtering effect of complex SVD is analyzed, as well as the influence of the number of rows of data matrix on the decomposition results, and the recognition effect of different classification models. The experimental results show that the algorithm has better filtering and recognition effects on common radar signals. Under –20 dB, the cosine similarity values of the reconstructed and original signals remain at about 0.94, and the recognition accuracy remains above 97% under a confidence level \begin{document}$\alpha$\end{document} of 0.65. In addition, experiments show that the weighted normalized SVD feature extraction algorithm has better robustness than the traditional Principal Component Analysis (PCA) algorithm.
2019, 8(1): 54-63.
Abstract(124)
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2019, 8(1): 64-72.
Abstract(93)
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Range Cell Migration Correction (RCMC) represents an important advance in moving target imaging in the airborne single antenna high-resolution SAR system. In this paper, we propose a new four-step RCMC approach combined with parameter estimation that overcomes the drawbacks of high computation and low accuracy in high-resolution. First, we use the Hough transform and the energy balancing method to estimate the range velocity and correct the range walk. Next, we perform a range curvature correction in the range-Doppler domain by using the initial Doppler rate. Thirdly, we accurately estimate the Doppler rate using Map-drift technology. Finally, we correct the residual range curvature by the accurate Doppler rate. Compared with traditional algorithms, the proposed method requires less computation and is robust in the high-resolution SAR system. In this paper, we present a mathematical model and validate its effectiveness using both simulation and real data. Range Cell Migration Correction (RCMC) represents an important advance in moving target imaging in the airborne single antenna high-resolution SAR system. In this paper, we propose a new four-step RCMC approach combined with parameter estimation that overcomes the drawbacks of high computation and low accuracy in high-resolution. First, we use the Hough transform and the energy balancing method to estimate the range velocity and correct the range walk. Next, we perform a range curvature correction in the range-Doppler domain by using the initial Doppler rate. Thirdly, we accurately estimate the Doppler rate using Map-drift technology. Finally, we correct the residual range curvature by the accurate Doppler rate. Compared with traditional algorithms, the proposed method requires less computation and is robust in the high-resolution SAR system. In this paper, we present a mathematical model and validate its effectiveness using both simulation and real data.
2019, 8(1): 73-81.
Abstract(62)
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This paper discusses anti-deceptive jamming methods based on single-channel and fixed waveform assumptions for synthetic aperture radar imaging. Using the essential defects of the deceptive-jamming theory, the information acquisition ability of the Synthetic Aperture Radar (SAR) system in a complicated electromagnetic environment is effectively improved with limited degrees of freedom in spatial and time domains. Geometric and signal models of SAR imaging and deceptive jamming are established and the different characteristics between them are analyzed according to their working mechanisms. Upon extracting their characteristic differences via different imaging processes and enhancing them based on statistical information, the degree of separation between the true and false targets is increased. Therefore, identification on the deceptive jamming is realized. Moreover, an approach for the dynamic synthetic aperture is used to formulate an optimization problem for the reconstruction of true and false targets. By solving such a problem, the true and false targets are separately reconstructed with super-resolution, achieving the goal of deceptive-jamming suppression. The effectiveness of the proposed methods is verified by simulations. This paper discusses anti-deceptive jamming methods based on single-channel and fixed waveform assumptions for synthetic aperture radar imaging. Using the essential defects of the deceptive-jamming theory, the information acquisition ability of the Synthetic Aperture Radar (SAR) system in a complicated electromagnetic environment is effectively improved with limited degrees of freedom in spatial and time domains. Geometric and signal models of SAR imaging and deceptive jamming are established and the different characteristics between them are analyzed according to their working mechanisms. Upon extracting their characteristic differences via different imaging processes and enhancing them based on statistical information, the degree of separation between the true and false targets is increased. Therefore, identification on the deceptive jamming is realized. Moreover, an approach for the dynamic synthetic aperture is used to formulate an optimization problem for the reconstruction of true and false targets. By solving such a problem, the true and false targets are separately reconstructed with super-resolution, achieving the goal of deceptive-jamming suppression. The effectiveness of the proposed methods is verified by simulations.
2019, 8(1): 82-89.
Abstract(111)
HTML(80) 1205KB(33)
2019, 8(1): 90-99.
Abstract(38)
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A novel approach using two-dimensional mixed baseline based on Multi-Input Multi-Output Synthetic Aperture Radar (MIMO-SAR) has been proposed for range delay and azimuth Doppler frequency modulation used in deceptive jamming. Based on MIMO-SAR phase coding method, which makes the multi-channel signal orthogonal, we propose the detection of deceptive jamming phenomenon by employing multi-dimensional phase information, and suppress the jamming targets via phase compensation to improve the ability of countering deceptive jamming. Moreover, we utilize radar anti-jamming improvement factor as a quantitative evaluating index. In the limited platform space, the radar anti-jamming improvement factor of the proposed method is three times greater than that of conventional single-input multi-output systems. Experimental results demonstrate the validity of our method. A novel approach using two-dimensional mixed baseline based on Multi-Input Multi-Output Synthetic Aperture Radar (MIMO-SAR) has been proposed for range delay and azimuth Doppler frequency modulation used in deceptive jamming. Based on MIMO-SAR phase coding method, which makes the multi-channel signal orthogonal, we propose the detection of deceptive jamming phenomenon by employing multi-dimensional phase information, and suppress the jamming targets via phase compensation to improve the ability of countering deceptive jamming. Moreover, we utilize radar anti-jamming improvement factor as a quantitative evaluating index. In the limited platform space, the radar anti-jamming improvement factor of the proposed method is three times greater than that of conventional single-input multi-output systems. Experimental results demonstrate the validity of our method.
2019, 8(1): 100-106.
Abstract(45)
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Through partial intercepting and multiple forwarding of a radar transmitting signal, Digital Radio Frequency Memory (DRFM)-based Interrupted Sampling Repeater Jamming (ISRJ) possesses advantages of small size, light weight, and flexibility. Thus, DRFM-ISRJ can be equipped on targets to perform multi-point source main-lobe jamming, posing a serious threat to modern radars. In this study, a time-frequency domain recognition and suppression method was analyzed. First, the expression of pulse compression and Time-Frequency Distribution (TFD) of the jamming signal were deduced. Then, the differences of TFD between target echo and jamming signal were analyzed. On this basis, a jamming recognition program and a time-frequency domain filter to suppress the jamming were proposed. Simulation results show that the recognition rate is better than 90% when the jamming-to-noise ratio is over –3 dB for the received signal. Based on correct recognition, a signal to jamming-and-noise ratio improvement of 18 dB can be achieved using the time-frequency filter. Through partial intercepting and multiple forwarding of a radar transmitting signal, Digital Radio Frequency Memory (DRFM)-based Interrupted Sampling Repeater Jamming (ISRJ) possesses advantages of small size, light weight, and flexibility. Thus, DRFM-ISRJ can be equipped on targets to perform multi-point source main-lobe jamming, posing a serious threat to modern radars. In this study, a time-frequency domain recognition and suppression method was analyzed. First, the expression of pulse compression and Time-Frequency Distribution (TFD) of the jamming signal were deduced. Then, the differences of TFD between target echo and jamming signal were analyzed. On this basis, a jamming recognition program and a time-frequency domain filter to suppress the jamming were proposed. Simulation results show that the recognition rate is better than 90% when the jamming-to-noise ratio is over –3 dB for the received signal. Based on correct recognition, a signal to jamming-and-noise ratio improvement of 18 dB can be achieved using the time-frequency filter.
2019, 8(1): 107-116.
Abstract(83)
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Fast construction of the 3-D scattering centers of ship targets on the sea surface is important for many radar applications, including the fast signature prediction, feature extraction, and automatic recognition of targets. Combining the " four-path” model for target-surface coupling scattering with modified Fresnel reflection coefficient model in the stochastic sea surface and ray tube integration method, we propose a 3-D image formation method for ship-surface compound targets. Using the CLEAN technique on 3-D image, we develop a fast algorithm for establishing 3-D scattering center model for ship targets on the sea surface. Because this algorithm realizes 3D imaging of targets at a single frequency and single aspect angle, and adopts simplified surface model to avoid the need to construct a large number of surface elements, the computational efficiency of the proposed alogrithm is greatly increased to meet the needs of practical engineering applications. Simulation experiments of a typical ship target show that the proposed algorithm can increase the speed by four orders of magnitude under typical conditions, as compared with the traditional FFT-based 3D imaging method. We validate the accuracy of this algorithm by comparing reconstructed 1-D range profiles and ISAR images obtain by the scattering center model with the ones that are directly simulated. Fast construction of the 3-D scattering centers of ship targets on the sea surface is important for many radar applications, including the fast signature prediction, feature extraction, and automatic recognition of targets. Combining the " four-path” model for target-surface coupling scattering with modified Fresnel reflection coefficient model in the stochastic sea surface and ray tube integration method, we propose a 3-D image formation method for ship-surface compound targets. Using the CLEAN technique on 3-D image, we develop a fast algorithm for establishing 3-D scattering center model for ship targets on the sea surface. Because this algorithm realizes 3D imaging of targets at a single frequency and single aspect angle, and adopts simplified surface model to avoid the need to construct a large number of surface elements, the computational efficiency of the proposed alogrithm is greatly increased to meet the needs of practical engineering applications. Simulation experiments of a typical ship target show that the proposed algorithm can increase the speed by four orders of magnitude under typical conditions, as compared with the traditional FFT-based 3D imaging method. We validate the accuracy of this algorithm by comparing reconstructed 1-D range profiles and ISAR images obtain by the scattering center model with the ones that are directly simulated.
2019, 8(1): 117-124.
Abstract(96)
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A robust quaternion-valued wideband adaptive beamformer is proposed, in which a quaternion is utilized to arrange the output of the array element. By exploiting the augmented envelope alignment technique, adopting the three involutions of quaternion, and incorporating the noncircular information of the signal simultaneously, a quaternion-valued wideband augmented signal model is established to achieve the robust adaptive beamforming based on signal subspace projection. Compared with other wideband beamformers, the proposed scheme exhibits a better performance in extracting noncircular signals by array aperture extension, and is insensitive to the pointing error. The simulation results verify the efficiency of the proposed beamformer. A robust quaternion-valued wideband adaptive beamformer is proposed, in which a quaternion is utilized to arrange the output of the array element. By exploiting the augmented envelope alignment technique, adopting the three involutions of quaternion, and incorporating the noncircular information of the signal simultaneously, a quaternion-valued wideband augmented signal model is established to achieve the robust adaptive beamforming based on signal subspace projection. Compared with other wideband beamformers, the proposed scheme exhibits a better performance in extracting noncircular signals by array aperture extension, and is insensitive to the pointing error. The simulation results verify the efficiency of the proposed beamformer.
2019, 8(1): 125-139.
Abstract(46)
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Monopulse is a mainstream technique used to acquire the angle information about active radar systems that are widely used in air defense warning, target tracking, and precision guidance. This study briefly reviews the development history of the monopulse theory and technology for the main-lobe multi-source condition. The importance of several key technologies within multi-source parameters estimation and multi-source jamming mitigation is also summarized. Finally, the future development of monopulse technology to resolve the problem of multi-source jamming is considered. Monopulse is a mainstream technique used to acquire the angle information about active radar systems that are widely used in air defense warning, target tracking, and precision guidance. This study briefly reviews the development history of the monopulse theory and technology for the main-lobe multi-source condition. The importance of several key technologies within multi-source parameters estimation and multi-source jamming mitigation is also summarized. Finally, the future development of monopulse technology to resolve the problem of multi-source jamming is considered.
2019, 8(1): 140-153.
Abstract(34)
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Cross-eye jamming is an effective angular deception jamming technique used for countering monopulse radars. With the need of countermeasure against active radar seekers, the research on cross-eye jamming becomes a hot research topic in electronic war. This study overviews the cross-eye jamming with regard to jamming theories, equipment, application problems, and current research trends to offer comprehensive knowledge and future research ideas. Cross-eye jamming is an effective angular deception jamming technique used for countering monopulse radars. With the need of countermeasure against active radar seekers, the research on cross-eye jamming becomes a hot research topic in electronic war. This study overviews the cross-eye jamming with regard to jamming theories, equipment, application problems, and current research trends to offer comprehensive knowledge and future research ideas.
2019, 8(1): 154-170.
Abstract(29)
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