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Editor Work2021 Vol. 10, No. 3
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2021, 10(3): 327-341.
Abstract
Under the constraints of the point scattering model, traditional Synthetic Aperture Radar (SAR) imaging algorithms can be regarded as a mapping from data space to image space. However, most objects in the real scene are extended targets, which are mismatched with the point scattering model in traditional linear imaging algorithms. The abovementioned reasons lead to the distortion of SAR image representation. A common phenomenon is that the extended targets appear as isolated scattered points, which hinder the application of target recognition on the basis of SAR images. SAR parametric nonlinear imaging techniques are established to solve the abovementioned model mismatch problem. Such methods are characterized by the scattering models that consider point targets and extended targets. Specifically, by using the sensitivity of the phase and amplitude characteristics of the echoes or images to the observation angles, SAR parametric imaging methods can first identify the target type and estimate the scattering parameters, and then reconstruct the target image on the basis of the scattering model. SAR parametric imaging methods can obtain better image quality than traditional linear methods for extended targets. This article mainly introduces the parametric imaging methods of linear extended targets, which correspond to the isolated strong points and continuous edges of objects in the real scene, and discusses the parametric imaging methods on the basis of the echo and image domains and experimental results. Last, the future development trends of SAR parametric imaging methods are discussed.
Under the constraints of the point scattering model, traditional Synthetic Aperture Radar (SAR) imaging algorithms can be regarded as a mapping from data space to image space. However, most objects in the real scene are extended targets, which are mismatched with the point scattering model in traditional linear imaging algorithms. The abovementioned reasons lead to the distortion of SAR image representation. A common phenomenon is that the extended targets appear as isolated scattered points, which hinder the application of target recognition on the basis of SAR images. SAR parametric nonlinear imaging techniques are established to solve the abovementioned model mismatch problem. Such methods are characterized by the scattering models that consider point targets and extended targets. Specifically, by using the sensitivity of the phase and amplitude characteristics of the echoes or images to the observation angles, SAR parametric imaging methods can first identify the target type and estimate the scattering parameters, and then reconstruct the target image on the basis of the scattering model. SAR parametric imaging methods can obtain better image quality than traditional linear methods for extended targets. This article mainly introduces the parametric imaging methods of linear extended targets, which correspond to the isolated strong points and continuous edges of objects in the real scene, and discusses the parametric imaging methods on the basis of the echo and image domains and experimental results. Last, the future development trends of SAR parametric imaging methods are discussed.
2021, 10(3): 342-359.
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Obtaining the internal layout of an unfamiliar building before entering the building has important practical significance and research value, as it can be applied for various services, such as anti-terrorism operations and disaster relief. Low-frequency electromagnetic waves can propagate through common building materials, and then the target information behind the wall is obtained safely and stably. Therefore, using low frequency radio waves to obtain the information behind the wall has become the research focus in the field of building layout reconstruction. To reveal the development context of this field and predict the possible future development trends, this paper summarizes the domestic and foreign public literature in this field since the onset of the 21st century. The results of the relevant literature indicate that the techniques of using low-frequency electromagnetic waves to reconstruct building layout currently include three types: through-the-wall radar imaging technology based on reflected wave measurement, radio-frequency tomography technology based on transmitted wave measurement, and wall position estimation technology based on multipath signals. These three technologies have achieved several practical research results. This article clarifies the development history of the main content covered by these technologies, which mainly includes the principle of through-the-wall radar imaging of stationary targets behind the wall, the observation mode of building internal structure based on through-the-wall radar, the reconstruction technology of building internal structure on the basis of through-the-wall radar imaging, the inversion technology of building internal structure on the basis of radio-frequency tomography, and the wall position estimation technology based on multipath signals. We also discuss the development trend of this field. In the past two decades, the development history of building layout penetrating imaging using low-frequency radio waves shows a change from the traditional airborne and vehicle-mounted building-layout-reconstruction platforms to new platforms such as microrobots and unmanned aerial vehicles. The corresponding reconstruction method has been developed from the traditional radar imaging technology to a variety of new methods, including image enhancement and sparse reconstruction. The results indicate that the building-layout-reconstruction technology is developing in the direction of systematization, diversification, and intelligence.
Obtaining the internal layout of an unfamiliar building before entering the building has important practical significance and research value, as it can be applied for various services, such as anti-terrorism operations and disaster relief. Low-frequency electromagnetic waves can propagate through common building materials, and then the target information behind the wall is obtained safely and stably. Therefore, using low frequency radio waves to obtain the information behind the wall has become the research focus in the field of building layout reconstruction. To reveal the development context of this field and predict the possible future development trends, this paper summarizes the domestic and foreign public literature in this field since the onset of the 21st century. The results of the relevant literature indicate that the techniques of using low-frequency electromagnetic waves to reconstruct building layout currently include three types: through-the-wall radar imaging technology based on reflected wave measurement, radio-frequency tomography technology based on transmitted wave measurement, and wall position estimation technology based on multipath signals. These three technologies have achieved several practical research results. This article clarifies the development history of the main content covered by these technologies, which mainly includes the principle of through-the-wall radar imaging of stationary targets behind the wall, the observation mode of building internal structure based on through-the-wall radar, the reconstruction technology of building internal structure on the basis of through-the-wall radar imaging, the inversion technology of building internal structure on the basis of radio-frequency tomography, and the wall position estimation technology based on multipath signals. We also discuss the development trend of this field. In the past two decades, the development history of building layout penetrating imaging using low-frequency radio waves shows a change from the traditional airborne and vehicle-mounted building-layout-reconstruction platforms to new platforms such as microrobots and unmanned aerial vehicles. The corresponding reconstruction method has been developed from the traditional radar imaging technology to a variety of new methods, including image enhancement and sparse reconstruction. The results indicate that the building-layout-reconstruction technology is developing in the direction of systematization, diversification, and intelligence.
2021, 10(3): 360-369.
In the midcourse trajectory of ballistic targets, warhead releasing and decoy throwing are two of the several types of target separation events. In the early stages, multiple targets are close to each other, and coupling scattering usually leads to variations in the radar cross section and polarization. If these variation features are investigated clearly, the tracking and recognition ability of an early warning radar will improve for ballistic targets. In this study, dynamic scattering of ballistic targets with three types of separation movements is analyzed; and several separation features that promote the action recognition development of midcourse ballistic targets are proposed.
In the midcourse trajectory of ballistic targets, warhead releasing and decoy throwing are two of the several types of target separation events. In the early stages, multiple targets are close to each other, and coupling scattering usually leads to variations in the radar cross section and polarization. If these variation features are investigated clearly, the tracking and recognition ability of an early warning radar will improve for ballistic targets. In this study, dynamic scattering of ballistic targets with three types of separation movements is analyzed; and several separation features that promote the action recognition development of midcourse ballistic targets are proposed.
2021, 10(3): 370-381.
This paper presents a derivation of a formula with a concise and uniform analytic form by the Stationary Phase Method (SPM) plus Geometrical Optics (GO), the Physical Theory of Diffraction (PTD), and Geometrical Theory of Diffraction (GTD) to calculate the frequency-dependent factor for the arbitrary multiple scattering mechanism, validated by the simulated and measured data of a series of canonical ensembles, validated by the simulated and measured data of a series of canonical ensembles. Although the GTD model, a scattering center model, can accurately describe the frequency-dependent characteristic of several main scattering mechanisms of the radar target, no explicit and general expression relates the frequency-dependent factor to the type of scattering mechanism. The derived formula relates the scattering center’s frequency-dependent factor with bounce times, dimensions of all the encountered geometrical elements, and a caustic type of ray contributing to the scattering center and can be applied to determine the parameter value of frequency-dependent factor of the GTD model and its derived versions in the forward parametric modeling.
This paper presents a derivation of a formula with a concise and uniform analytic form by the Stationary Phase Method (SPM) plus Geometrical Optics (GO), the Physical Theory of Diffraction (PTD), and Geometrical Theory of Diffraction (GTD) to calculate the frequency-dependent factor for the arbitrary multiple scattering mechanism, validated by the simulated and measured data of a series of canonical ensembles, validated by the simulated and measured data of a series of canonical ensembles. Although the GTD model, a scattering center model, can accurately describe the frequency-dependent characteristic of several main scattering mechanisms of the radar target, no explicit and general expression relates the frequency-dependent factor to the type of scattering mechanism. The derived formula relates the scattering center’s frequency-dependent factor with bounce times, dimensions of all the encountered geometrical elements, and a caustic type of ray contributing to the scattering center and can be applied to determine the parameter value of frequency-dependent factor of the GTD model and its derived versions in the forward parametric modeling.
2021, 10(3): 382-390.
In this paper, a novel four-leaf clover-shaped coding metasurface is proposed and applied to achieve an ultra-wideband diffusion-like scattering. The proposed metasurface element has rotational symmetry; hence, it produces similar reflection characteristics for both x- and y-polarized waves. To realize a 1-bit coding metasurface, two elements are chosen that have a phase difference of 180°±37° from 15.5 to 40.5 GHz. An optimization algorithm is applied to get the best arrangement of unit cells in the array to attain the wideband RCS reduction. The four-leaf clover-shaped metasurface can attain more than 10 dB RCS reduction from 15.5 to 26.5 GHz and 30.5 to 40.5 GHz. A prototype of the proposed design is fabricated, and an experiment is carried out to validate the performance of the metasurface. The proposed concept of four-leaf clover-shaped coding metasurface is an effective solution for wideband RCS reduction applications.
In this paper, a novel four-leaf clover-shaped coding metasurface is proposed and applied to achieve an ultra-wideband diffusion-like scattering. The proposed metasurface element has rotational symmetry; hence, it produces similar reflection characteristics for both x- and y-polarized waves. To realize a 1-bit coding metasurface, two elements are chosen that have a phase difference of 180°±37° from 15.5 to 40.5 GHz. An optimization algorithm is applied to get the best arrangement of unit cells in the array to attain the wideband RCS reduction. The four-leaf clover-shaped metasurface can attain more than 10 dB RCS reduction from 15.5 to 26.5 GHz and 30.5 to 40.5 GHz. A prototype of the proposed design is fabricated, and an experiment is carried out to validate the performance of the metasurface. The proposed concept of four-leaf clover-shaped coding metasurface is an effective solution for wideband RCS reduction applications.
2021, 10(3): 391-401.
When Interferometric Synthetic Aperture Radar (InSAR) is used to obtain the Digital Elevation Model (DEM), highly sloped terrains will make interferometric fringes dense and increase the difficulty of phase unwrapping, which will affect the accuracy of phase unwrapping and elevation inversion. To solve this problem, an InSAR elevation inversion method based on BackProjection (BP) model with an external DEM is proposed. This model achieves imaging and InSAR DEM inversion in a uniform BP geographic space and introduces an external DEM as auxiliary information. These processes, in turn, can remove most phases of the terrain and reduce the density of interferometric fringes and phase wrapping. Additionally, the proposed method can avoid the procedures of image registration and phase unwrapping in most cases, which simplifies traditional InSAR processing and achieves high processing accuracy. A simulation experiment and X-band InSAR data processing were performed to verify the effectiveness of the proposed method.
When Interferometric Synthetic Aperture Radar (InSAR) is used to obtain the Digital Elevation Model (DEM), highly sloped terrains will make interferometric fringes dense and increase the difficulty of phase unwrapping, which will affect the accuracy of phase unwrapping and elevation inversion. To solve this problem, an InSAR elevation inversion method based on BackProjection (BP) model with an external DEM is proposed. This model achieves imaging and InSAR DEM inversion in a uniform BP geographic space and introduces an external DEM as auxiliary information. These processes, in turn, can remove most phases of the terrain and reduce the density of interferometric fringes and phase wrapping. Additionally, the proposed method can avoid the procedures of image registration and phase unwrapping in most cases, which simplifies traditional InSAR processing and achieves high processing accuracy. A simulation experiment and X-band InSAR data processing were performed to verify the effectiveness of the proposed method.
2021, 10(3): 402-415.
As important man-made targets, bridges have been a major focus of Synthetic Aperture Radar (SAR) image interpretation, and many researchers have developed methods for bridge detection. The core frameworks of these methods are analogical, a river is first extracted, and a water bridge is detected based on the positional relationship between the river and bridge. However, existing bridge detection methods relying on river extraction; cannot be utilized detect land bridges. This is because the background environment under a bridge is land, not river, which has different scattering characteristics and shape layouts. As such, the traditional method for extracting rivers is not suitable for extracting land background, and it is impossible to locate a bridge based on prior knowledge of its location of. To resolve this problem, in this study, we propose a land bridge detection method based on polarized Circular SAR (CSAR) images. In our proposed method, the Circular Polarization Entropy (CPE) of an observed scene is introduced to separate possible bridge targets from a land background (In our experiment, the average CPE of the bridge is 0.4018, and that of the land background is 0.7819; thus there is a clear difference between the bridge and background). False targets are removed based on the difference in the polarization entropy variance features of the bridges and other ground objects; and the size characteristics of the bridges. Finally, accurate extractions of land bridges are obtained based on the geometric characteristics of the bridges. Experimental results based on real airborne L-band polarized CSAR data verify the correctness of the theoretical analysis and effectiveness of the proposed method.
As important man-made targets, bridges have been a major focus of Synthetic Aperture Radar (SAR) image interpretation, and many researchers have developed methods for bridge detection. The core frameworks of these methods are analogical, a river is first extracted, and a water bridge is detected based on the positional relationship between the river and bridge. However, existing bridge detection methods relying on river extraction; cannot be utilized detect land bridges. This is because the background environment under a bridge is land, not river, which has different scattering characteristics and shape layouts. As such, the traditional method for extracting rivers is not suitable for extracting land background, and it is impossible to locate a bridge based on prior knowledge of its location of. To resolve this problem, in this study, we propose a land bridge detection method based on polarized Circular SAR (CSAR) images. In our proposed method, the Circular Polarization Entropy (CPE) of an observed scene is introduced to separate possible bridge targets from a land background (In our experiment, the average CPE of the bridge is 0.4018, and that of the land background is 0.7819; thus there is a clear difference between the bridge and background). False targets are removed based on the difference in the polarization entropy variance features of the bridges and other ground objects; and the size characteristics of the bridges. Finally, accurate extractions of land bridges are obtained based on the geometric characteristics of the bridges. Experimental results based on real airborne L-band polarized CSAR data verify the correctness of the theoretical analysis and effectiveness of the proposed method.
2021, 10(3): 416-431.
The disadvantages of the traditional Inverse Synthetic Aperture Radar (ISAR) imaging method based on Fourier transform include large data storage and long collection time. The Compressive Sensing (CS) theory can use limited data to restore an image with the sparsity of the image, reducing the cost of data collection. However for multidimensional data, the traditional compressive sensing methods need to convert three-dimensional data into a one-dimensional vector, causing the storage and calculation burden. Therefore, this study proposes a fast MultiDimensional Alternating Direction Method of Multipliers ((MD-ADMM)) sparse reconstruction method for Multiple-Input Multiple-Output ISAR (MIMO-ISAR) imaging. The CS model based on the tensor signal was established, and the model with the ADMM algorithm was optimized. The measured matrix is decomposed into a tensor modal product, and matrix inversion is replaced by tensor element division, significantly reducing memory consumption and computational burden. Fast ISAR imaging can be achieved by a small amount of data sampling by the proposed method. Compared with other tensor compressed sensing methods, this method has the advantages of stronger robustness, higher image quality, and computational efficiency. The effectiveness of the proposed method can be invalidated by simulated and measured data.
The disadvantages of the traditional Inverse Synthetic Aperture Radar (ISAR) imaging method based on Fourier transform include large data storage and long collection time. The Compressive Sensing (CS) theory can use limited data to restore an image with the sparsity of the image, reducing the cost of data collection. However for multidimensional data, the traditional compressive sensing methods need to convert three-dimensional data into a one-dimensional vector, causing the storage and calculation burden. Therefore, this study proposes a fast MultiDimensional Alternating Direction Method of Multipliers ((MD-ADMM)) sparse reconstruction method for Multiple-Input Multiple-Output ISAR (MIMO-ISAR) imaging. The CS model based on the tensor signal was established, and the model with the ADMM algorithm was optimized. The measured matrix is decomposed into a tensor modal product, and matrix inversion is replaced by tensor element division, significantly reducing memory consumption and computational burden. Fast ISAR imaging can be achieved by a small amount of data sampling by the proposed method. Compared with other tensor compressed sensing methods, this method has the advantages of stronger robustness, higher image quality, and computational efficiency. The effectiveness of the proposed method can be invalidated by simulated and measured data.
2021, 10(3): 432-442.
Mixed source localization plays an important role in passive radars. Aiming at the problem of low accuracy via phase difference method under a uniform circular array, this paper proposes a matrix differencing method for mixed far-field and near-field source localization. First, a two-dimensional MUltiple SIgnal Classification (MUSIC) method was utilized to estimate the azimuth and elevation angles of far-field sources. Thereafter, the covariance matrix difference method was exploited to extract the difference matrix of near-field sources. The azimuth and elevation angles of the far-field sources were estimated using the Estimation of Signal Parameters via Rotational Invariance Techniques-like (ESPRIT-like) method. Furthermore, the distance of the near-field sources was obtained by the one-dimensional MUSIC method. Finally, simulations were performed to verify the performance of the proposed algorithm. The proposed algorithm could effectively identify the mixed source when the two-dimensional Direction-Of-Arrival (DOA) of the far-field and near-field sources were the same. Moreover, the proposed algorithm could improve the accuracy of the mixed source parameter estimation. Results show that when the signal-to-noise ratio was set to 20 dB, the 2-D DOA estimation error of the near-field source was approximately 0.01°, and the distance error of the near-field source was approximately 0.1 m.
Mixed source localization plays an important role in passive radars. Aiming at the problem of low accuracy via phase difference method under a uniform circular array, this paper proposes a matrix differencing method for mixed far-field and near-field source localization. First, a two-dimensional MUltiple SIgnal Classification (MUSIC) method was utilized to estimate the azimuth and elevation angles of far-field sources. Thereafter, the covariance matrix difference method was exploited to extract the difference matrix of near-field sources. The azimuth and elevation angles of the far-field sources were estimated using the Estimation of Signal Parameters via Rotational Invariance Techniques-like (ESPRIT-like) method. Furthermore, the distance of the near-field sources was obtained by the one-dimensional MUSIC method. Finally, simulations were performed to verify the performance of the proposed algorithm. The proposed algorithm could effectively identify the mixed source when the two-dimensional Direction-Of-Arrival (DOA) of the far-field and near-field sources were the same. Moreover, the proposed algorithm could improve the accuracy of the mixed source parameter estimation. Results show that when the signal-to-noise ratio was set to 20 dB, the 2-D DOA estimation error of the near-field source was approximately 0.01°, and the distance error of the near-field source was approximately 0.1 m.
2021, 10(3): 443-452.
To address the problem of detecting point-like targets in a partially homogeneous Gaussian cluttered environment, we developed a modified Generalized Likelihood Ratio Test (GLRT) detection method based on a symmetrically spaced linear array that relies on a GLRT design criterion. Considering the target energy spillover during sampling, we use a spillover model of the target energy to decrease spillover loss. To establish the discrete-time signal mode, we use a persymmetric structure of the disturbance covariance matrix to reduce the requirement for auxiliary signals. Lastly, we estimate all of the unknown parameters based on a consideration of both primary and secondary data to derive the persymmetric modified GLRT detector, which has good target detection and range estimation performance. The performance assessment shows that the proposed method not only performs as a constant false-alarm-rate receiver in partially homogeneous environments but also guarantees superior detection performance relative to that of its competitors. In sample-starved environments, compared with other detection methods of the same type, it realizes a detection performance advantage greater than 1 dB.
To address the problem of detecting point-like targets in a partially homogeneous Gaussian cluttered environment, we developed a modified Generalized Likelihood Ratio Test (GLRT) detection method based on a symmetrically spaced linear array that relies on a GLRT design criterion. Considering the target energy spillover during sampling, we use a spillover model of the target energy to decrease spillover loss. To establish the discrete-time signal mode, we use a persymmetric structure of the disturbance covariance matrix to reduce the requirement for auxiliary signals. Lastly, we estimate all of the unknown parameters based on a consideration of both primary and secondary data to derive the persymmetric modified GLRT detector, which has good target detection and range estimation performance. The performance assessment shows that the proposed method not only performs as a constant false-alarm-rate receiver in partially homogeneous environments but also guarantees superior detection performance relative to that of its competitors. In sample-starved environments, compared with other detection methods of the same type, it realizes a detection performance advantage greater than 1 dB.
2021, 10(3): 453-466.
Waveform design of joint radar and communication has become a focus of intense research in recent years. Some scholars have proposed to use the odd and even carrier of Orthogonal Frequency Division Multiplexing (OFDM) signal to modulate the radar and communication functions, respectively, to realize the integration. However, OFDM systems generally use cyclic prefix to avoid Inter-Carrier Interference (ICI) and Inter-Symbol Interference (ISI) caused by multipath effects, reducing energy utilization and creating false targets, which affect radar performance. In addition, the traditional OFDM integrated signal is more sensitive to Doppler shift. A small Doppler frequency offset will also cause a considerable drop in orthogonal performance. On this basis, this paper proposes a new waveform design and processing method. This method uses blank guard intervals to replace cyclic prefixes, which can resist multipath effects while avoiding false targets introduced by cyclic prefixes, effectively preventing ICI and ISI. In terms of signal processing methods, this paper proposes a method for channel estimation and Doppler compensation using the priori information of the radar signal. Compared with the traditional method, this new method reduces the system’s resource overhead, such as pilot frequency and training sequence. It improves energy utilization and spectrum efficiency. The peak side lobe ratio, integration side lobe rate, and bit error ratio are also improved. Simulation experiments verify the effectiveness of this method.
Waveform design of joint radar and communication has become a focus of intense research in recent years. Some scholars have proposed to use the odd and even carrier of Orthogonal Frequency Division Multiplexing (OFDM) signal to modulate the radar and communication functions, respectively, to realize the integration. However, OFDM systems generally use cyclic prefix to avoid Inter-Carrier Interference (ICI) and Inter-Symbol Interference (ISI) caused by multipath effects, reducing energy utilization and creating false targets, which affect radar performance. In addition, the traditional OFDM integrated signal is more sensitive to Doppler shift. A small Doppler frequency offset will also cause a considerable drop in orthogonal performance. On this basis, this paper proposes a new waveform design and processing method. This method uses blank guard intervals to replace cyclic prefixes, which can resist multipath effects while avoiding false targets introduced by cyclic prefixes, effectively preventing ICI and ISI. In terms of signal processing methods, this paper proposes a method for channel estimation and Doppler compensation using the priori information of the radar signal. Compared with the traditional method, this new method reduces the system’s resource overhead, such as pilot frequency and training sequence. It improves energy utilization and spectrum efficiency. The peak side lobe ratio, integration side lobe rate, and bit error ratio are also improved. Simulation experiments verify the effectiveness of this method.
2021, 10(3): 467-484.
The need of extra wireless spectrum is on the rise, given the rapid development of global wireless communication industry. To this end, Radar and Communication Spectrum Sharing (RCSS) has gained considerable attentions recently from both industry and academia. In particular, RCSS aims not only at enabling the spectral cohabitation of radar and communication systems, but also at designing a novel joint system that is capable of both functionalities. In this paper, a systematic overview of RCSS by focusing on the two main research directions are provided, i.e., Radar-Communication Coexistence (RCC) and Dual-Functional Radar-Communication (DFRC). We commence by discussing the coexistence examples of radar and communication at various frequency bands, and then elaborate on the practical application scenarios of the DFRC techniques. As a further step, the state-of-the-art approaches of both RCC and DFRC are reviewed. Finally we conclude the paper by identifying a number of open problems in the research area of RCSS.
The need of extra wireless spectrum is on the rise, given the rapid development of global wireless communication industry. To this end, Radar and Communication Spectrum Sharing (RCSS) has gained considerable attentions recently from both industry and academia. In particular, RCSS aims not only at enabling the spectral cohabitation of radar and communication systems, but also at designing a novel joint system that is capable of both functionalities. In this paper, a systematic overview of RCSS by focusing on the two main research directions are provided, i.e., Radar-Communication Coexistence (RCC) and Dual-Functional Radar-Communication (DFRC). We commence by discussing the coexistence examples of radar and communication at various frequency bands, and then elaborate on the practical application scenarios of the DFRC techniques. As a further step, the state-of-the-art approaches of both RCC and DFRC are reviewed. Finally we conclude the paper by identifying a number of open problems in the research area of RCSS.
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