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Independent processing of each polarization channel and three-dimensional multistage imaging ignore the correlation between data, resulting in the mismatch between scattering centers and the inaccurate acquisition of polarization scattering matrices. To address these issues, a full-polarization Synthetic Aperture Radar (SAR) joint multidimensional reconstruction method based on sparse reconstruction is proposed in this study. In this method, all polarization channels and dimensions are integrated by setting the joint sparse constraints, and the full-polarization SAR joint multidimensional reconstruction is modeled as a multichannel joint sparse reconstruction problem. After the model is simplified by data interpolation, an efficient model-solving method is proposed by combining the three-dimensional fast Fourier transform, conjugate gradient method, and Newton iteration method, where the polarization scattering matrix and three-dimensional information of the target can be obtained at the same time. The proposed method ensures that the sparse support sets of different polarization channels and dimensions are consistent and utilizes the additional information generated by the correlation between data. On the basis of the simulation and electromagnetic calculation data, the experimental results indicate that the proposed method is tolerant of noise and immune to the types of targets. Moreover, the proposed method can effectively obtain the multidimensional reconstruction results of the target, where both the resolution of the imaging results and the estimation accuracy of the polarization scattering matrix are high. Independent processing of each polarization channel and three-dimensional multistage imaging ignore the correlation between data, resulting in the mismatch between scattering centers and the inaccurate acquisition of polarization scattering matrices. To address these issues, a full-polarization Synthetic Aperture Radar (SAR) joint multidimensional reconstruction method based on sparse reconstruction is proposed in this study. In this method, all polarization channels and dimensions are integrated by setting the joint sparse constraints, and the full-polarization SAR joint multidimensional reconstruction is modeled as a multichannel joint sparse reconstruction problem. After the model is simplified by data interpolation, an efficient model-solving method is proposed by combining the three-dimensional fast Fourier transform, conjugate gradient method, and Newton iteration method, where the polarization scattering matrix and three-dimensional information of the target can be obtained at the same time. The proposed method ensures that the sparse support sets of different polarization channels and dimensions are consistent and utilizes the additional information generated by the correlation between data. On the basis of the simulation and electromagnetic calculation data, the experimental results indicate that the proposed method is tolerant of noise and immune to the types of targets. Moreover, the proposed method can effectively obtain the multidimensional reconstruction results of the target, where both the resolution of the imaging results and the estimation accuracy of the polarization scattering matrix are high.
Over the recent years, deep-learning technology has been widely used. However, in research based on Synthetic Aperture Radar (SAR) ship target detection, it is difficult to support the training of a deep-learning network model because of the difficulty in data acquisition and the small scale of the samples. This paper provides a SAR ship detection dataset with a high resolution and large-scale images. This dataset comprises 31 images from Gaofen-3 satellite SAR images, including harbors, islands, reefs, and the sea surface in different conditions. The backgrounds include various scenarios such as the near shore and open sea. We conducted experiments using both traditional detection algorithms and deep-learning algorithms and observed the densely connected end-to-end neural network to achieve the highest average precision of 88.1%. Based on the experiments and performance analysis, corresponding benchmarks are provided as a basis for further research on SAR ship detection using this dataset. Over the recent years, deep-learning technology has been widely used. However, in research based on Synthetic Aperture Radar (SAR) ship target detection, it is difficult to support the training of a deep-learning network model because of the difficulty in data acquisition and the small scale of the samples. This paper provides a SAR ship detection dataset with a high resolution and large-scale images. This dataset comprises 31 images from Gaofen-3 satellite SAR images, including harbors, islands, reefs, and the sea surface in different conditions. The backgrounds include various scenarios such as the near shore and open sea. We conducted experiments using both traditional detection algorithms and deep-learning algorithms and observed the densely connected end-to-end neural network to achieve the highest average precision of 88.1%. Based on the experiments and performance analysis, corresponding benchmarks are provided as a basis for further research on SAR ship detection using this dataset.
Synthetic Aperture Radar (SAR) is an all-weather and all-time imaging radar with high resolution, which is widely used for enemy reconnaissance to provide timely and accurate intelligence for taking decisions during wars. It has become a hot issue in the contemporary electronic warfare to suppress and disorder the reconnaissance imaging of SAR equipment for protecting high-value targets and important strategic areas. This study discusses the development and future trend of SAR jamming techniques. First, the history of development of SAR jamming techniques is discussed and explained in detail. Then, the advantages and disadvantages of the typical SAR jamming models are comparatively analyzed together with simulation experiments. Finally, the current defects of the SAR jamming techniques are summarized and the future trend of the SAR jamming techniques is also pointed out, providing some reference for experts and scholars. Synthetic Aperture Radar (SAR) is an all-weather and all-time imaging radar with high resolution, which is widely used for enemy reconnaissance to provide timely and accurate intelligence for taking decisions during wars. It has become a hot issue in the contemporary electronic warfare to suppress and disorder the reconnaissance imaging of SAR equipment for protecting high-value targets and important strategic areas. This study discusses the development and future trend of SAR jamming techniques. First, the history of development of SAR jamming techniques is discussed and explained in detail. Then, the advantages and disadvantages of the typical SAR jamming models are comparatively analyzed together with simulation experiments. Finally, the current defects of the SAR jamming techniques are summarized and the future trend of the SAR jamming techniques is also pointed out, providing some reference for experts and scholars.
An azimuth multichannel Synthetic Aperture Radar (SAR) can be used to obtain high-resolution wide-swath SAR images. Accurate estimation of the phase error between channels is the key to ensuring image quality. In this study, we present a channel phase error estimation method based on the error backpropagation algorithm. During the physical process of a multichannel SAR echo generation, this method constructs an observation matrix with the parameters to be estimated including the phase error between channels. The initial SAR echo is generated using the initial channel error matrix and initial target scattering coefficient matrix, and the error between the echo and measured multichannel SAR echo is calculated. Using the backpropagation algorithm commonly used in deep learning, the abovementioned parameters are continuously trained and optimized. Finally, the estimation of the phase error between channels is obtained along with the target scattering coefficient. This method combines the error backpropagation method with the principle of multichannel SAR channel error. Phase estimation and imaging are realized based on the sparsity assumption, which provides a new approach for estimating an error in a multichannel SAR. The effectiveness of the presented method is validated using multichannel SAR simulation data. An azimuth multichannel Synthetic Aperture Radar (SAR) can be used to obtain high-resolution wide-swath SAR images. Accurate estimation of the phase error between channels is the key to ensuring image quality. In this study, we present a channel phase error estimation method based on the error backpropagation algorithm. During the physical process of a multichannel SAR echo generation, this method constructs an observation matrix with the parameters to be estimated including the phase error between channels. The initial SAR echo is generated using the initial channel error matrix and initial target scattering coefficient matrix, and the error between the echo and measured multichannel SAR echo is calculated. Using the backpropagation algorithm commonly used in deep learning, the abovementioned parameters are continuously trained and optimized. Finally, the estimation of the phase error between channels is obtained along with the target scattering coefficient. This method combines the error backpropagation method with the principle of multichannel SAR channel error. Phase estimation and imaging are realized based on the sparsity assumption, which provides a new approach for estimating an error in a multichannel SAR. The effectiveness of the presented method is validated using multichannel SAR simulation data.
Suppression position of the traditional noise convolution modulation Synthetic Aperture Radar (SAR) jamming lags behind in range and suppression area in azimuth is uncontrollable. Considering this defect, an enhanced jamming method is proposed herein. First, the frequency of the intercepted signal is shifted in fast-time to control the suppression position in range. Then, the convolution with the noise is implemented, which has been filtered in slow-time, to control the suppression area in azimuth. Theoretical analysis and simulation results demonstrate that the proposed jamming method can efficiently control the jamming position in range and suppression area when compared with the traditional noise convolution modulation jamming. Even if some reconnaissance errors exist, the local scenario can still be shielded effectively. Furthermore, the utilization efficiency of jamming energy is also improved under the same condition, which will provide some reference values and inputs for engineering applications. Suppression position of the traditional noise convolution modulation Synthetic Aperture Radar (SAR) jamming lags behind in range and suppression area in azimuth is uncontrollable. Considering this defect, an enhanced jamming method is proposed herein. First, the frequency of the intercepted signal is shifted in fast-time to control the suppression position in range. Then, the convolution with the noise is implemented, which has been filtered in slow-time, to control the suppression area in azimuth. Theoretical analysis and simulation results demonstrate that the proposed jamming method can efficiently control the jamming position in range and suppression area when compared with the traditional noise convolution modulation jamming. Even if some reconnaissance errors exist, the local scenario can still be shielded effectively. Furthermore, the utilization efficiency of jamming energy is also improved under the same condition, which will provide some reference values and inputs for engineering applications.
In the Medium-Earth-Orbit Synthetic Aperture Radar (MEO SAR), the curved trajectory and long synthetic aperture time lead to a two-dimensional spatial variation in the signals. Traditional methods usually process the range and azimuth variations separately, and the computational complexities are high. Herein, we study the Doppler rate distribution across a large scene and propose a non-orthogonal and nonlinear coordinate system wherein the MEO SAR signals satisfy the azimuth-shift–invariant property. Thus, the efficiency of the image formation processor can be significantly improved. The higher-order Doppler parameters are addressed by the Doppler linearization. Then, more precise focusing can be achieved, and the azimuth time-shift caused by the changes in signal distribution is addressed. Finally, the processing results of simulated stripmap-mode data with a 2-m resolution are presented to validate the effectiveness of the proposed algorithm. In the Medium-Earth-Orbit Synthetic Aperture Radar (MEO SAR), the curved trajectory and long synthetic aperture time lead to a two-dimensional spatial variation in the signals. Traditional methods usually process the range and azimuth variations separately, and the computational complexities are high. Herein, we study the Doppler rate distribution across a large scene and propose a non-orthogonal and nonlinear coordinate system wherein the MEO SAR signals satisfy the azimuth-shift–invariant property. Thus, the efficiency of the image formation processor can be significantly improved. The higher-order Doppler parameters are addressed by the Doppler linearization. Then, more precise focusing can be achieved, and the azimuth time-shift caused by the changes in signal distribution is addressed. Finally, the processing results of simulated stripmap-mode data with a 2-m resolution are presented to validate the effectiveness of the proposed algorithm.
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Special Topic Papers: Novel Intelligent Radar Detecting Technology
Clutter is a term used for unwanted echoes in electronic systems, particularly in reference to radars. Such echoes are typically returned from ground, sea, rain, animals/insects, chaff, and atmospheric turbulences, and can cause serious performance issues with radar systems. Ionospheric clutter is a time-varying, nonstationary, and non-Gaussian complex clutter in High-Frequency Surface-Wave Radar (HFSWR) system and its suppression is a daunting task. Extensive research on intelligent classification systems and suppression techniques of ionospheric clutter was conducted to solve the universal problem of single clutter suppression algorithm. After a complete analysis of the characteristics of ionospheric clutter, the present work proposes an intelligent ionospheric clutter processing method based on clustering and greedy algorithms for the classification and suppression of ionospheric clutter. Experimental results showed that the proposed method has a better performance than the traditional algorithm in suppressing ionospheric clutter. Clutter is a term used for unwanted echoes in electronic systems, particularly in reference to radars. Such echoes are typically returned from ground, sea, rain, animals/insects, chaff, and atmospheric turbulences, and can cause serious performance issues with radar systems. Ionospheric clutter is a time-varying, nonstationary, and non-Gaussian complex clutter in High-Frequency Surface-Wave Radar (HFSWR) system and its suppression is a daunting task. Extensive research on intelligent classification systems and suppression techniques of ionospheric clutter was conducted to solve the universal problem of single clutter suppression algorithm. After a complete analysis of the characteristics of ionospheric clutter, the present work proposes an intelligent ionospheric clutter processing method based on clustering and greedy algorithms for the classification and suppression of ionospheric clutter. Experimental results showed that the proposed method has a better performance than the traditional algorithm in suppressing ionospheric clutter.
In this paper, a target’s electromagnetic scattering phenomenon is characterized by the Three Dimensional Parametric Electromagnetic Part Model (3D-PEPM) and a novel Synthetic Aperture Radar (SAR) target recognition method is proposed based on the model. The proposed method projects the individual scatterers in the 3D-PEPM to the 2D image plane to predict the location and appearance for each scatterer according to the radar parameters firstly. Then based on the prior information provided by the 3D-PEPM, the similarities between the 3D-PEPM and SAR data are evaluated. Finally, a view angle adjusting method is utilized to optimize the whole process to produce the final match score between the model and SAR data, and the recognition decision is made according to the match score. The proposed recognition method identifies clearly the correspondences of the scatterers between SAR data and 3D-PEPM and enjoys the explicit physical interpretability, so it can deal with SAR recognition problems under various extended operating conditions. Experiments on simulated data reveal the effectiveness of the proposed method. In this paper, a target’s electromagnetic scattering phenomenon is characterized by the Three Dimensional Parametric Electromagnetic Part Model (3D-PEPM) and a novel Synthetic Aperture Radar (SAR) target recognition method is proposed based on the model. The proposed method projects the individual scatterers in the 3D-PEPM to the 2D image plane to predict the location and appearance for each scatterer according to the radar parameters firstly. Then based on the prior information provided by the 3D-PEPM, the similarities between the 3D-PEPM and SAR data are evaluated. Finally, a view angle adjusting method is utilized to optimize the whole process to produce the final match score between the model and SAR data, and the recognition decision is made according to the match score. The proposed recognition method identifies clearly the correspondences of the scatterers between SAR data and 3D-PEPM and enjoys the explicit physical interpretability, so it can deal with SAR recognition problems under various extended operating conditions. Experiments on simulated data reveal the effectiveness of the proposed method.
With the application of deep learning technology in the radar target recognition field, the automatic extraction of the target feature greatly improves the accuracy and robustness of the recognition, but its robustness in noisy environments needs to be further investigated. This paper proposes a robust target recognition method for radar High Resolution Range Profile (HRRP) data based on Convolutional Neural Networks (CNN). By enhancing training set and using the residual block, inception structure, and denoising sparse autoencoder layer to enhance the network structure, a higher recognition rate is achieved in a wider SNR range, under the condition of 0 dB Rayleigh noise, the recognition rate reaches 96.14%, and the influence of the network structure and noise type on results is analyzed. With the application of deep learning technology in the radar target recognition field, the automatic extraction of the target feature greatly improves the accuracy and robustness of the recognition, but its robustness in noisy environments needs to be further investigated. This paper proposes a robust target recognition method for radar High Resolution Range Profile (HRRP) data based on Convolutional Neural Networks (CNN). By enhancing training set and using the residual block, inception structure, and denoising sparse autoencoder layer to enhance the network structure, a higher recognition rate is achieved in a wider SNR range, under the condition of 0 dB Rayleigh noise, the recognition rate reaches 96.14%, and the influence of the network structure and noise type on results is analyzed.
In this paper, a fast parameter estimation algorithm for near-field non-circular signals is proposed, which is based on symmetric, uniform, and linear array, that decouple the near-field steering vector via non-circularity and symmetry. Then, the position parameters are estimated based upon polynomial rooting technique instead of traditional spectral search. After fixing the array structure, the parameters for estimation equation are ascertained. Then the values of position parameters are obtained by solving the polynomial equations; consequently, computational difficulty is alleviated effectively. In addition, the Degree-Of-Freedom (DOF) is increased by utilizing the non-circularity characteristics of the impinging sources. It can be seen from the performance analysis and computer simulation experiments that the proposed algorithm can resolve more near-field non-circular signals and can improve parameter estimation performance, which is closer to the Cramer–Rao Bound (CRB) estimation. In this paper, a fast parameter estimation algorithm for near-field non-circular signals is proposed, which is based on symmetric, uniform, and linear array, that decouple the near-field steering vector via non-circularity and symmetry. Then, the position parameters are estimated based upon polynomial rooting technique instead of traditional spectral search. After fixing the array structure, the parameters for estimation equation are ascertained. Then the values of position parameters are obtained by solving the polynomial equations; consequently, computational difficulty is alleviated effectively. In addition, the Degree-Of-Freedom (DOF) is increased by utilizing the non-circularity characteristics of the impinging sources. It can be seen from the performance analysis and computer simulation experiments that the proposed algorithm can resolve more near-field non-circular signals and can improve parameter estimation performance, which is closer to the Cramer–Rao Bound (CRB) estimation.
A marine radar device is a major navigation tool for boaters and ships. The images produced by marine radars detect not only hard targets such as ships and coastlines, but also reflections from the sea surface, known as sea clutter. The strong sea clutter and the complex characteristics of marine targets result in transmission of weak echo signals of the images to the radar, which makes difficult for radars to distinguish and analyze. So, effective sea clutter suppression and robust, fast target detection mechanisms are needed for radar to detect marine targets efficiently. However, the existing marine target detection algorithms have limited performance for target detection under complex environments, and have poor adaptability to environment and target characteristics. In this paper, an Integrated Network (INet) for clutter suppression and target detection algorithm is proposed and designed to optimize the signals received from the targets. The layer normalization algorithm integrated with transfer function is used to extract key target features, and the spatial attention network is used to suppress the clutter and to enhance the target signals, and a local cross-scale residual network is built to ensure the weightlessness of the system and accuracy of the detection network. Based on the echo data collected by the navigation radar under various observation conditions, radar images with marine target dataset were constructed. INet was optimized through pre-training of the model and inter-frame accumulation of Plan Position Indicator (PPI) images to obtain the Optimized INet (O-INet). The measured data were verified, tested, and compared with data obtained through various algorithms such as YOLOv3, YOLOv4, two-parameter CFAR, and two-dimensional CA-CFAR. The results obtained prove that the proposed method has superior advantages over other methods in improving detection probability, reducing false alarm rate, and strong generalization ability under complex conditions. A marine radar device is a major navigation tool for boaters and ships. The images produced by marine radars detect not only hard targets such as ships and coastlines, but also reflections from the sea surface, known as sea clutter. The strong sea clutter and the complex characteristics of marine targets result in transmission of weak echo signals of the images to the radar, which makes difficult for radars to distinguish and analyze. So, effective sea clutter suppression and robust, fast target detection mechanisms are needed for radar to detect marine targets efficiently. However, the existing marine target detection algorithms have limited performance for target detection under complex environments, and have poor adaptability to environment and target characteristics. In this paper, an Integrated Network (INet) for clutter suppression and target detection algorithm is proposed and designed to optimize the signals received from the targets. The layer normalization algorithm integrated with transfer function is used to extract key target features, and the spatial attention network is used to suppress the clutter and to enhance the target signals, and a local cross-scale residual network is built to ensure the weightlessness of the system and accuracy of the detection network. Based on the echo data collected by the navigation radar under various observation conditions, radar images with marine target dataset were constructed. INet was optimized through pre-training of the model and inter-frame accumulation of Plan Position Indicator (PPI) images to obtain the Optimized INet (O-INet). The measured data were verified, tested, and compared with data obtained through various algorithms such as YOLOv3, YOLOv4, two-parameter CFAR, and two-dimensional CA-CFAR. The results obtained prove that the proposed method has superior advantages over other methods in improving detection probability, reducing false alarm rate, and strong generalization ability under complex conditions.
Owing to the complicated characteristics of high-resolution sea clutter and the diversity of sea-surface small targets, there is no precise parameter model to describe sea clutter and returns with targets. As a result, target detection faces many obstacles. To distinguish sea clutter and target returns, it is effective to extract their features to transform the detection problem into a classification problem in feature space. Feature-based detection is a binary hypothesis test in the feature space that encounters two intrinsic difficulties: one difficulty is insufficient target returns versus sufficient sea clutter; the other difficulty is an uncontrolled false alarm rate in detection. To solve the first difficulty, a generator of typical targets returns that can generate sufficient simulated targets returns is used to balance the number of samples between two classes and assist to design the detector. K Nearest Neighbors (K-NN) is the type of classification method that is simple and effective; however, it cannot be used to detect small targets directly because of the uncontrolled false alarm rate. This paper proposes a modified K-NN method with a controlled false alarm rate for detecting small targets. Experimental results on the IPIX radar database indicate that the proposed detector attains 85.1% and 89.2% rates of target detection for the observation time of 0.512 s and 1.024 s, respectively, compared with other existing feature-based detectors, the proposed detector exhibits 7% and 5% improvement, respectively. Thus, the proposed detector exhibits more stable and effective detection performance than other existing feature-based detectors. Owing to the complicated characteristics of high-resolution sea clutter and the diversity of sea-surface small targets, there is no precise parameter model to describe sea clutter and returns with targets. As a result, target detection faces many obstacles. To distinguish sea clutter and target returns, it is effective to extract their features to transform the detection problem into a classification problem in feature space. Feature-based detection is a binary hypothesis test in the feature space that encounters two intrinsic difficulties: one difficulty is insufficient target returns versus sufficient sea clutter; the other difficulty is an uncontrolled false alarm rate in detection. To solve the first difficulty, a generator of typical targets returns that can generate sufficient simulated targets returns is used to balance the number of samples between two classes and assist to design the detector. K Nearest Neighbors (K-NN) is the type of classification method that is simple and effective; however, it cannot be used to detect small targets directly because of the uncontrolled false alarm rate. This paper proposes a modified K-NN method with a controlled false alarm rate for detecting small targets. Experimental results on the IPIX radar database indicate that the proposed detector attains 85.1% and 89.2% rates of target detection for the observation time of 0.512 s and 1.024 s, respectively, compared with other existing feature-based detectors, the proposed detector exhibits 7% and 5% improvement, respectively. Thus, the proposed detector exhibits more stable and effective detection performance than other existing feature-based detectors.
Polarization is a property applying to transverse waves that specifies the geometrical orientation of the oscillations. This paper proposes a method for detecting small targets on the sea surface based on the combination of polarization features of two models. The scattering mechanism of sea clutter is random scattering at low glazing angle or glancing angle and the randomness is high as the angles do not have any specified shape. However, a target has a specific shape, and thus, the randomness of scattering will be less. Clutter is a term used for unwanted echoes in electronic systems, particularly in reference to radars. Such echoes typically return from ground, sea, rain, and animals/insects. In this literature, the randomness of a scattering mechanism in an echo is obtained from the probability density functions of polarization entropy using the Cloude decomposition model. Further, the proportion of scattering at spherical, dihedral, and helicoid angles from the target echoes will be different in the sea clutter. Therefore, the relative coefficient of power of these three scattering components in each echo is extracted based on Krogager polarization decomposition. Then, polarization features with good separability and complementarity are selected to form the polarization feature vector, and the characteristics are verified by Principle Component Analysis (PCA). Finally, One Class Support Vector Machine (OCSVM) is used for classification and recognition based on the polarization decomposition feature vector. Instead of single-polarization detection methods, our method uses two polarization modes to extract the decomposition features with separability and complementarity through polarization coherent decomposition and incoherent decomposition, respectively. The experimental results of the IPIX data show the effectiveness of our method. Thus, the detection performance of our model is better than those methods based on single-polarization decomposition in complex and difficult sea conditions. Polarization is a property applying to transverse waves that specifies the geometrical orientation of the oscillations. This paper proposes a method for detecting small targets on the sea surface based on the combination of polarization features of two models. The scattering mechanism of sea clutter is random scattering at low glazing angle or glancing angle and the randomness is high as the angles do not have any specified shape. However, a target has a specific shape, and thus, the randomness of scattering will be less. Clutter is a term used for unwanted echoes in electronic systems, particularly in reference to radars. Such echoes typically return from ground, sea, rain, and animals/insects. In this literature, the randomness of a scattering mechanism in an echo is obtained from the probability density functions of polarization entropy using the Cloude decomposition model. Further, the proportion of scattering at spherical, dihedral, and helicoid angles from the target echoes will be different in the sea clutter. Therefore, the relative coefficient of power of these three scattering components in each echo is extracted based on Krogager polarization decomposition. Then, polarization features with good separability and complementarity are selected to form the polarization feature vector, and the characteristics are verified by Principle Component Analysis (PCA). Finally, One Class Support Vector Machine (OCSVM) is used for classification and recognition based on the polarization decomposition feature vector. Instead of single-polarization detection methods, our method uses two polarization modes to extract the decomposition features with separability and complementarity through polarization coherent decomposition and incoherent decomposition, respectively. The experimental results of the IPIX data show the effectiveness of our method. Thus, the detection performance of our model is better than those methods based on single-polarization decomposition in complex and difficult sea conditions.
Regarding the target characteristics in marine radar detection, this paper introduces classic radar target characteristics and models, and the main problems associated with the measurement and computation of these target characteristics. From three perspectives, i.e., the target, environment, and sensor, we discuss the target characteristic that have attracted much attention in the field of marine target detection. We discuss the diversity of marine target characteristics, the variety and complexity of the marine environment, coupling effects between the target and the environment, and the main requirements of the typical marine radar in target detection applications. The techniques used in the measurements and computation of target characteristic are also introduced. We propose a multidimensional representation of the target characteristics and briefly discuss its applications. Regarding the target characteristics in marine radar detection, this paper introduces classic radar target characteristics and models, and the main problems associated with the measurement and computation of these target characteristics. From three perspectives, i.e., the target, environment, and sensor, we discuss the target characteristic that have attracted much attention in the field of marine target detection. We discuss the diversity of marine target characteristics, the variety and complexity of the marine environment, coupling effects between the target and the environment, and the main requirements of the typical marine radar in target detection applications. The techniques used in the measurements and computation of target characteristic are also introduced. We propose a multidimensional representation of the target characteristics and briefly discuss its applications.
Radar target detection in sea clutter is of significance to both civilian and military. With the miniaturization and invisibility of sea targets, floating small targets with slow speed have become the focus of radar detection. However, the detection of floating small targets in the background of sea clutter has always been a problem. Floating small targets usually have a weak Radar Cross Section (RCS) and slow speed, making it difficult to detect such targets in sea clutter. Traditional target detection methods exhibit poor performance in the detection of floating small targets. For the detection of small and weak targets on the sea surface, a high-Doppler-resolution and high-range-resolution system (double high system) is an effective way to solve this problem. In the double high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry and a subject of constant innovation. In recent years, under the double high system, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed a variety of feature-based target detection methods to alleviate the difficulty in detecting floating small targets when relying only on energy information and considerably improve detection performance. To ensure that relevant radar practitioners better understand the development and future trend of this field in recent years, this paper summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods. Radar target detection in sea clutter is of significance to both civilian and military. With the miniaturization and invisibility of sea targets, floating small targets with slow speed have become the focus of radar detection. However, the detection of floating small targets in the background of sea clutter has always been a problem. Floating small targets usually have a weak Radar Cross Section (RCS) and slow speed, making it difficult to detect such targets in sea clutter. Traditional target detection methods exhibit poor performance in the detection of floating small targets. For the detection of small and weak targets on the sea surface, a high-Doppler-resolution and high-range-resolution system (double high system) is an effective way to solve this problem. In the double high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry and a subject of constant innovation. In recent years, under the double high system, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed a variety of feature-based target detection methods to alleviate the difficulty in detecting floating small targets when relying only on energy information and considerably improve detection performance. To ensure that relevant radar practitioners better understand the development and future trend of this field in recent years, this paper summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods.
Papers
In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance. In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance.
Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results. Track initiation is the first important step in group target tracking, and it has a direct effect on the quality of the overall procedure. Traditional radar target tracking methods only utilize information about the target position to detect group numbers, but they do not use information relating to echo amplitude. Tracks are thus easily lost, as the numbers of detected groups and equivalent measurements are inaccurate. This paper proposes a group target track initiation method aided by echo amplitude information to ameliorate these problems. In this respect, target position and echo amplitude information is used to detect the number of target groups, and equivalent measurements are then computed using amplitude weighting and position weighting. Echo amplitude information is employed in the step of detecting group target numbers and computing the equivalent measurements, and group target tracks are subsequently initialized using the modified logic method. The proposed method can be used to correctly detect the number of target groups when the number is previously unknown. Furthermore, the method reduces the rate of track loss and improves the performance of group target tracking. The effectiveness of the proposed method is validated by the simulation results.
The random finite set-based extended target tracking methods generally partition measurements by spatial information. It is possible to place clutter measurements into target cells in a dense clutter environment resulting in degradation of tracking performance. To solve this issue, in this paper, the amplitude information of the target and clutter was introduced into the Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter, and thus, the optimal partition was found by calculating the amplitude likelihood of the measurement cells. Additionally, when calculating the centroid of a measurement cell, amplitude was used as a weighting factor to find the mass center instead of the widely used geometric center. This further reduced clutter interference. The tracking results of Swerling 1 fluctuating targets in a Rayleigh clutter when the signal-to-clutter ratios were 13 dB and 6 dB showed that the performance of the proposed algorithm in cardinality estimation and state estimation was better than that of the GIW-PHD filter. The random finite set-based extended target tracking methods generally partition measurements by spatial information. It is possible to place clutter measurements into target cells in a dense clutter environment resulting in degradation of tracking performance. To solve this issue, in this paper, the amplitude information of the target and clutter was introduced into the Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter, and thus, the optimal partition was found by calculating the amplitude likelihood of the measurement cells. Additionally, when calculating the centroid of a measurement cell, amplitude was used as a weighting factor to find the mass center instead of the widely used geometric center. This further reduced clutter interference. The tracking results of Swerling 1 fluctuating targets in a Rayleigh clutter when the signal-to-clutter ratios were 13 dB and 6 dB showed that the performance of the proposed algorithm in cardinality estimation and state estimation was better than that of the GIW-PHD filter.
To improve the cross-range resolution of three-dimensional (3-D) images obtained along the direction of movement by Multiple-Input Mmultiple-Output (MIMO) radar, a novel MIMO-ISAR 3-D imaging method that combines multi-snapshot images is proposed. This method integrates multiple single-snapshot 3-D images acquired by a planar antenna array during a specific period of observation and extracts the peak slice along the linear fitting direction of the scatterers to construct a new 3-D image. The simulation results demonstrated that the proposed method significantly improves the cross-range resolution of the imaging results along the direction of movement compared with other methods based on single-snapshot 3-D images. Additionally, compared with the classical MIMO-ISAR method based on rearrangement and interpolation, this method is suitable for both fast-moving and slow-moving targets. Moreover, the imaging results are well focused and the side lobes along the direction of movement are effectively suppressed. To improve the cross-range resolution of three-dimensional (3-D) images obtained along the direction of movement by Multiple-Input Mmultiple-Output (MIMO) radar, a novel MIMO-ISAR 3-D imaging method that combines multi-snapshot images is proposed. This method integrates multiple single-snapshot 3-D images acquired by a planar antenna array during a specific period of observation and extracts the peak slice along the linear fitting direction of the scatterers to construct a new 3-D image. The simulation results demonstrated that the proposed method significantly improves the cross-range resolution of the imaging results along the direction of movement compared with other methods based on single-snapshot 3-D images. Additionally, compared with the classical MIMO-ISAR method based on rearrangement and interpolation, this method is suitable for both fast-moving and slow-moving targets. Moreover, the imaging results are well focused and the side lobes along the direction of movement are effectively suppressed.

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