Most downloaded articles
2015, 30(2):289-298.
DOI: 10.16337/j.1004-9037.2015.02.006
Abstract:
Photoacoustic imaging (PAI) is a state of art biomedicine imaging technique in the 21st century, for it inherits the high resolution of ultrasonography in deep tissue and the ability of optical imaging in biochemical information detection simultaneously. The recent progresses of PAI in biomedicine are reviewed. The basic principles and two major implementations of PAI, photoacoustic tomography and photoacoustic microscopy are introduced. Then the capability of multi wavelength PAI in evaluating chemical components in tissues, and the feasibility of PA spectral analyses in evaluating histological microstructures in biological tissue are demonstrated, at the same time, several analysis methods and clinical applications of PAI in biomedical imaging are discussed. Finally, the advantages and potential applications of PAI in biology and medicine are sunmarized.
Photoacoustic imaging (PAI) is a state of art biomedicine imaging technique in the 21st century, for it inherits the high resolution of ultrasonography in deep tissue and the ability of optical imaging in biochemical information detection simultaneously. The recent progresses of PAI in biomedicine are reviewed. The basic principles and two major implementations of PAI, photoacoustic tomography and photoacoustic microscopy are introduced. Then the capability of multi wavelength PAI in evaluating chemical components in tissues, and the feasibility of PA spectral analyses in evaluating histological microstructures in biological tissue are demonstrated, at the same time, several analysis methods and clinical applications of PAI in biomedical imaging are discussed. Finally, the advantages and potential applications of PAI in biology and medicine are sunmarized.
2014, 29(5):677-682.
Abstract:
In compressed sensing, measurement matrix plays an important role in signal acquisition and reconstruction. The traditional random measurement matrices can achieve good performance on the condition that the sampling rate is high enough, whereas the reconstructions are not satisfactory at low sampling rates. Compared with these random measurement matrices, the deterministic measurement matrices possess their own constraints, which lead to worse performance. Based on the generalized rotation (GR) matrix, two kinds of structured random matrices are proposed as the generalized binary rotation (GBR) matrix and the pseudo random generalized binary rotation (PGBR) matrix. Simulation results for two dimensional signals show that the two series of new matrices perform better than the traditional measurement matrices. The amount of time required by the traditional and the new approaches is about the same. Moreover, they can obtain more accurate reconstructions at low sampling rates.
In compressed sensing, measurement matrix plays an important role in signal acquisition and reconstruction. The traditional random measurement matrices can achieve good performance on the condition that the sampling rate is high enough, whereas the reconstructions are not satisfactory at low sampling rates. Compared with these random measurement matrices, the deterministic measurement matrices possess their own constraints, which lead to worse performance. Based on the generalized rotation (GR) matrix, two kinds of structured random matrices are proposed as the generalized binary rotation (GBR) matrix and the pseudo random generalized binary rotation (PGBR) matrix. Simulation results for two dimensional signals show that the two series of new matrices perform better than the traditional measurement matrices. The amount of time required by the traditional and the new approaches is about the same. Moreover, they can obtain more accurate reconstructions at low sampling rates.
2014, 29(5):704-712.
Abstract:
In the traditional variational level set method for image segmentation, the evolving level set function needs periodical re-initialization to keep it close to a signed distance function during the evolution. It remains many serious problems such as when and how to apply the re-initialization. Li presented a new variational formulation that forces the level set function to be close to a signed distance function by adding an internal energy into the energy functional, and therefore completely eliminates the need of the expensive re-initialization procedure. We present a new image segmentation model based on variational level set method. It also completely eliminates the need of the re-initialization by adding a new and simple internal energy into the energy functional. In addition, a new external energy by redefining the edge stopping function is introduced, which makes the proposed model more robust to noisy image segmentation. The experimental results show that, compared with Li model, our model has some superiority in the convergence speed andsegmentation quality for noisy image.
In the traditional variational level set method for image segmentation, the evolving level set function needs periodical re-initialization to keep it close to a signed distance function during the evolution. It remains many serious problems such as when and how to apply the re-initialization. Li presented a new variational formulation that forces the level set function to be close to a signed distance function by adding an internal energy into the energy functional, and therefore completely eliminates the need of the expensive re-initialization procedure. We present a new image segmentation model based on variational level set method. It also completely eliminates the need of the re-initialization by adding a new and simple internal energy into the energy functional. In addition, a new external energy by redefining the edge stopping function is introduced, which makes the proposed model more robust to noisy image segmentation. The experimental results show that, compared with Li model, our model has some superiority in the convergence speed andsegmentation quality for noisy image.
2015, 30(5):915-932.
Abstract:
Biomedical signal processing plays an important role in life science research, p revention and treatment of diseases and medical instrument industry. Since biome dical signal is detected from human beings, it can be diverse and complicated du e to physiological and psychological reasons. This paper summarizes and classifi es the commonly used biomedical signals, features and the corresponding processi ng approaches. The applications of biomedical signal processing on electrocardio gram (ECG) and electroencephalogram (EEG) signal are introduced. New advances in biomedical signal processing in recent years are also deliberated. Finally, s ome thoughts are provided with respect to the future researches on biomedical si gnal processing.
Biomedical signal processing plays an important role in life science research, p revention and treatment of diseases and medical instrument industry. Since biome dical signal is detected from human beings, it can be diverse and complicated du e to physiological and psychological reasons. This paper summarizes and classifi es the commonly used biomedical signals, features and the corresponding processi ng approaches. The applications of biomedical signal processing on electrocardio gram (ECG) and electroencephalogram (EEG) signal are introduced. New advances in biomedical signal processing in recent years are also deliberated. Finally, s ome thoughts are provided with respect to the future researches on biomedical si gnal processing.
2022, 37(3):512-541.
DOI: 10.16337/j.1004-9037.2022.03.004
Abstract:
Domain adaptation mainly deals with similar task decision across different data distributions. As an emerging branch of machine learning， domain adaptation has received much attention. With the rise of deep learning in recent years， the deep domain adaptation paradigm， as a combination of deep learning and traditional domain adaptation， has attracted more and more research. Although a variety of deep domain adaptation methods have been proposed， few systematic reviews have been published. To this end， this paper definitely reviews and analyzes the existing deep domain adaptation work and summarizes them to provide reference for relevant researchers. In conclusion， the main contributions of this work include the following aspects. Firstly， the background， concepts and application fields of domain adaptation are summarized. Secondly， according to whether the model training involves adversarial mechanism， we group the existing deep domain adaptation methods into two categories， such as deep adversarial domain adaptation and deep non-adversarial domain adaptation， and review and analyze them， respectively. Then， the benchmark datasets commonly used in the domain adaptation research are tabulated with profiles. Finally， the issues suffered in the existing deep domain adaptation work are summarized and analyzed， and future research directions are given.
Domain adaptation mainly deals with similar task decision across different data distributions. As an emerging branch of machine learning， domain adaptation has received much attention. With the rise of deep learning in recent years， the deep domain adaptation paradigm， as a combination of deep learning and traditional domain adaptation， has attracted more and more research. Although a variety of deep domain adaptation methods have been proposed， few systematic reviews have been published. To this end， this paper definitely reviews and analyzes the existing deep domain adaptation work and summarizes them to provide reference for relevant researchers. In conclusion， the main contributions of this work include the following aspects. Firstly， the background， concepts and application fields of domain adaptation are summarized. Secondly， according to whether the model training involves adversarial mechanism， we group the existing deep domain adaptation methods into two categories， such as deep adversarial domain adaptation and deep non-adversarial domain adaptation， and review and analyze them， respectively. Then， the benchmark datasets commonly used in the domain adaptation research are tabulated with profiles. Finally， the issues suffered in the existing deep domain adaptation work are summarized and analyzed， and future research directions are given.
2022, 37(3):471-487.
DOI: 10.16337/j.1004-9037.2022.03.001
Abstract:
With the development of big data， data has become a major strategic resource for countries and its social impact is increasingly obvious. Thus， data science is proposed to explore and study basic scientific problems contained in big data. In this paper， the development of big data， the rise and connotation of data science are first introduced. Second， the research status of big data and data science is analyzed， and the application of data in various industries is discussed. Third， the big data proving ground that is constructed to explore laws and problems of data science is briefly described. Finally， in order to promote the development of data science， accelerate the transformation of the real world to the digital world， and realize the intelligent life， the key issues of data science and the new thinking in digital world are discussed.
With the development of big data， data has become a major strategic resource for countries and its social impact is increasingly obvious. Thus， data science is proposed to explore and study basic scientific problems contained in big data. In this paper， the development of big data， the rise and connotation of data science are first introduced. Second， the research status of big data and data science is analyzed， and the application of data in various industries is discussed. Third， the big data proving ground that is constructed to explore laws and problems of data science is briefly described. Finally， in order to promote the development of data science， accelerate the transformation of the real world to the digital world， and realize the intelligent life， the key issues of data science and the new thinking in digital world are discussed.
2022, 37(3):502-511.
DOI: 10.16337/j.1004-9037.2022.03.003
Abstract:
The evolution of massive multiple-input multiple-output （MIMO） techniques is an important support for further improving the performance of six-generation （6G） wireless communication systems. However， with the continuous expansion of large-scale antenna arrays， frequency division duplex （FDD） massive MIMO systems are facing severe challenges in acquiring downlink channel state information （CSI）. Deep learning has a powerful ability to learn and process high-dimensional data， which provides a potential solution to this challenge. In this paper， we survey FDD massive MIMO downlink CSI acquisition techniques based on deep learning， including CSI feedback and prediction techniques. Firstly， the theoretical frameworks of CSI feedback and prediction based on deep learning are presented. Then， the superior performance of relevant research results at home and abroad is analyzed， providing a reference scheme for solving the problem of acquiring downlink CSI in FDD massive MIMO systems towards 6G. Finally， unsolved open problems of FDD massive MIMO downlink CSI acquisition are discussed， followed by potential solutions correspondingly.
The evolution of massive multiple-input multiple-output （MIMO） techniques is an important support for further improving the performance of six-generation （6G） wireless communication systems. However， with the continuous expansion of large-scale antenna arrays， frequency division duplex （FDD） massive MIMO systems are facing severe challenges in acquiring downlink channel state information （CSI）. Deep learning has a powerful ability to learn and process high-dimensional data， which provides a potential solution to this challenge. In this paper， we survey FDD massive MIMO downlink CSI acquisition techniques based on deep learning， including CSI feedback and prediction techniques. Firstly， the theoretical frameworks of CSI feedback and prediction based on deep learning are presented. Then， the superior performance of relevant research results at home and abroad is analyzed， providing a reference scheme for solving the problem of acquiring downlink CSI in FDD massive MIMO systems towards 6G. Finally， unsolved open problems of FDD massive MIMO downlink CSI acquisition are discussed， followed by potential solutions correspondingly.
2022, 37(3):597-608.
DOI: 10.16337/j.1004-9037.2022.03.010
Abstract:
Traditional visual simultaneous localization and mapping （SLAM） algorithms rely on the scene rigidity assumption. However， when dynamic objects exist in the scene， the stability of the SLAM system will be affected and the accuracy of pose estimation will be reduced. Currently， most of the existing methods apply probability strategies and geometric constraints to reduce the impact caused by a small number of dynamic objects. But when the number of dynamic objects in the scene is high， these methods will fail. In order to deal with this problem， a novel algorithm is proposed in this paper. It combines the dynamic visual SLAM algorithm with the multi-target tracking algorithm. Firstly， a semantic instance segmentation network together with geometric constraints is introduced to assist the visual SLAM module to effectively separate the static feature points from the dynamic ones， and at the same time， it can also achieve the better multi-target tracking performance. Furthermore， the trajectory and velocity information of the moving objects can also be estimated， which can provide decision information for autonomous robots navigation. The experimental results on KITTI dataset show that the localization accuracy of the proposed algorithm is improved by about 28% compared with ORB-SLAM2 algorithm in dynamic environments.
Traditional visual simultaneous localization and mapping （SLAM） algorithms rely on the scene rigidity assumption. However， when dynamic objects exist in the scene， the stability of the SLAM system will be affected and the accuracy of pose estimation will be reduced. Currently， most of the existing methods apply probability strategies and geometric constraints to reduce the impact caused by a small number of dynamic objects. But when the number of dynamic objects in the scene is high， these methods will fail. In order to deal with this problem， a novel algorithm is proposed in this paper. It combines the dynamic visual SLAM algorithm with the multi-target tracking algorithm. Firstly， a semantic instance segmentation network together with geometric constraints is introduced to assist the visual SLAM module to effectively separate the static feature points from the dynamic ones， and at the same time， it can also achieve the better multi-target tracking performance. Furthermore， the trajectory and velocity information of the moving objects can also be estimated， which can provide decision information for autonomous robots navigation. The experimental results on KITTI dataset show that the localization accuracy of the proposed algorithm is improved by about 28% compared with ORB-SLAM2 algorithm in dynamic environments.
2022, 37(3):586-596.
DOI: 10.16337/j.1004-9037.2022.03.009
Abstract:
Aiming at the problem that the training samples for few-shot learning are too few， which leads to the weak expression of features， a novel dynamic routing prototypical network based on SLDA（DRP-SLDA） is proposed based on the supervised topic model（Supervised LDA， SLDA） and dynamic routing algorithm. The SLDA topic model is used to establish the semantic mapping between words and categories， enhance the category distribution characteristics of words， and obtain the semantic representation of samples from the perspective of word granularity. The dynamic routing prototypical network（DR-Proto） is presented. The network makes full use of the semantic relationship between samples by extracting cross features， and uses the dynamic routing algorithm to iteratively generate dynamic prototype with category representation， so as to solve the problem of feature expression. The experimental results show that the DRP-SLDA model can effectively extract the category distribution characteristics of words and dynamically obtain the dynamic prototype to increase the category identification， which can obviously improve the generalization ability of few-shot text classification.
Aiming at the problem that the training samples for few-shot learning are too few， which leads to the weak expression of features， a novel dynamic routing prototypical network based on SLDA（DRP-SLDA） is proposed based on the supervised topic model（Supervised LDA， SLDA） and dynamic routing algorithm. The SLDA topic model is used to establish the semantic mapping between words and categories， enhance the category distribution characteristics of words， and obtain the semantic representation of samples from the perspective of word granularity. The dynamic routing prototypical network（DR-Proto） is presented. The network makes full use of the semantic relationship between samples by extracting cross features， and uses the dynamic routing algorithm to iteratively generate dynamic prototype with category representation， so as to solve the problem of feature expression. The experimental results show that the DRP-SLDA model can effectively extract the category distribution characteristics of words and dynamically obtain the dynamic prototype to increase the category identification， which can obviously improve the generalization ability of few-shot text classification.
2020, 35(1):1-20.
DOI: 10.16337/j.1004-9037.2020.01.003
Abstract:
Image super-resolution reconstruction is an image processing technology, which recovers high-resolution images from low-resolution images. While, the super-resolution problem is under-determined. In recent years, researchers have proposed learning-based methods to learn image prior information from a large amount of data, in order to constrain the super-resolution solution space. This paper introduces the mainstream image super-resolution reconstruction algorithms in the past two decades, which are divided into two categories: traditional features based methods and deep learning based methods. For the traditional super-resolution reconstruction algorithms, this paper mainly presents the methods based on neighborhood embedding, the methods based on sparse representation, and the methods based on local linear regression. For the deep learning based methods, the super-resolution model design, the up-sampling method and the loss function form are provided. In addition, this paper introduces the application of super-resolution reconstruction technology in video super-resolution, remote-sensing image super-resolution, and high-level vision tasks. Finally, the future development directions of image super-resolution reconstruction technology are provided.
Image super-resolution reconstruction is an image processing technology, which recovers high-resolution images from low-resolution images. While, the super-resolution problem is under-determined. In recent years, researchers have proposed learning-based methods to learn image prior information from a large amount of data, in order to constrain the super-resolution solution space. This paper introduces the mainstream image super-resolution reconstruction algorithms in the past two decades, which are divided into two categories: traditional features based methods and deep learning based methods. For the traditional super-resolution reconstruction algorithms, this paper mainly presents the methods based on neighborhood embedding, the methods based on sparse representation, and the methods based on local linear regression. For the deep learning based methods, the super-resolution model design, the up-sampling method and the loss function form are provided. In addition, this paper introduces the application of super-resolution reconstruction technology in video super-resolution, remote-sensing image super-resolution, and high-level vision tasks. Finally, the future development directions of image super-resolution reconstruction technology are provided.
2022, 37(3):633-642.
DOI: 10.16337/j.1004-9037.2022.03.013
Abstract:
The effect of classification learning is closely related to the distribution of limited training samples. Support vector data description （SVDD）， as a single boundary solution model， cannot well describe the actual distribution characteristics of the data， resulting in some target objects falling outside the hypersphere. To improve its classification ability， this paper proposes a granular computing-driven SVDD （GrC-SVDD） classification method to construct a multi-granularity levels attribute sets and the corresponding multi-granular hyperspheres. Firstly，the importance of the attribute within the current granularity level is calculated through the neighborhood self-information. Secondly， the best attribute set is then chosen to retrain the hyperspheres that did not achieve the purity criterion at the previous granularity level， and so on until all hyperspheres meet the conditions or the attributes are exhausted. The experimental section discusses the effect of parameters on classification performance and learns hyperparameters. The experimental results show that GrC-SVDD has better classification performance compared with SVDD and popular classification methods.
The effect of classification learning is closely related to the distribution of limited training samples. Support vector data description （SVDD）， as a single boundary solution model， cannot well describe the actual distribution characteristics of the data， resulting in some target objects falling outside the hypersphere. To improve its classification ability， this paper proposes a granular computing-driven SVDD （GrC-SVDD） classification method to construct a multi-granularity levels attribute sets and the corresponding multi-granular hyperspheres. Firstly，the importance of the attribute within the current granularity level is calculated through the neighborhood self-information. Secondly， the best attribute set is then chosen to retrain the hyperspheres that did not achieve the purity criterion at the previous granularity level， and so on until all hyperspheres meet the conditions or the attributes are exhausted. The experimental section discusses the effect of parameters on classification performance and learns hyperparameters. The experimental results show that GrC-SVDD has better classification performance compared with SVDD and popular classification methods.
2012, 27(1).
Abstract:
Fingerprint segmentation has been considered as one of the critical processes of the automatic fingerprint identification system. Following the analysis of the relationship between the second order statistical characteristics and the grey-scale level, the offset value and the relative direction, an innovative fingerprint segmentation algorithm based on the gray level co-occurrence matrix (GLCM) is thus presented. Firstly, the fingerprint is split into a number of rectangular blocks to get the contrasts of GLCM for each in different directions. And then, to judge for whether a rectangular block is the prospect region or not, the proposed algorithm compares its variance of the contrast with the predefined threshold. The theoretical analysis and experiment results on the FVC2004 show that the proposed algorithm performs well and is robust in handling the varied qualities of fingerprint images collected in any circumstance.
Fingerprint segmentation has been considered as one of the critical processes of the automatic fingerprint identification system. Following the analysis of the relationship between the second order statistical characteristics and the grey-scale level, the offset value and the relative direction, an innovative fingerprint segmentation algorithm based on the gray level co-occurrence matrix (GLCM) is thus presented. Firstly, the fingerprint is split into a number of rectangular blocks to get the contrasts of GLCM for each in different directions. And then, to judge for whether a rectangular block is the prospect region or not, the proposed algorithm compares its variance of the contrast with the predefined threshold. The theoretical analysis and experiment results on the FVC2004 show that the proposed algorithm performs well and is robust in handling the varied qualities of fingerprint images collected in any circumstance.
2014, 29(3):341-350.
Abstract:
With the rapid increase of wireless devices and the openness of wireless communication, the security problem becomes more and more serious and challenging. Different from traditional key-based cryptography schemes, physical layer security has been proposed to realize unconditional security from the information theory perspective. This paper provides a review of the model of physical layer security built by Shannon and tracks the evolution of security schemes without key leaded by Wyner and secret key-based secrecy schemes leaded by Maurer. Among them, the former aims at widening the channel quality gap between the authorized user and the eavesdropper; while the latter using the wireless channel as a nature random source exploiting the channel characteristics. In the coming fifth generation mobile communication scheme, physical layer security can match it with a lightweight security technology which avoids the long delay in the traditional cryptography. Although the theoretical studies of physical layer security have become maturity, they still face many practical problems to be solved in practical.
With the rapid increase of wireless devices and the openness of wireless communication, the security problem becomes more and more serious and challenging. Different from traditional key-based cryptography schemes, physical layer security has been proposed to realize unconditional security from the information theory perspective. This paper provides a review of the model of physical layer security built by Shannon and tracks the evolution of security schemes without key leaded by Wyner and secret key-based secrecy schemes leaded by Maurer. Among them, the former aims at widening the channel quality gap between the authorized user and the eavesdropper; while the latter using the wireless channel as a nature random source exploiting the channel characteristics. In the coming fifth generation mobile communication scheme, physical layer security can match it with a lightweight security technology which avoids the long delay in the traditional cryptography. Although the theoretical studies of physical layer security have become maturity, they still face many practical problems to be solved in practical.
2013, 28(1).
Abstract:
These years, modeling the process of Internet public opinions' evolution and trend forecasting based on that has become a hot topic. The existing short-term trend forecasting method ignores the variability of statistical properties of Internet public opinions’ evolution, which leads to a blind model selection, and the forecasting performance is poor. Therefore, this paper presents an adaptive evolution modeling method of Internet public opinions (AEMIPO). Firstly, this method tracks the statistical characteristics of the process of Internet public opinions' evolution dynamically, such as smoothness, periodicity and self-similarity. Then, by selecting ARMA, ARIMA, SARIMA and FARIMA, an alternative model bank is constructed. Finally, by making model selection rules, an appropriate model is selected to model and forecast the process of evolution adaptively. The experimental results show that compared with the existing methods, AEMIPO has higher forecasting accuracy and stability, and this method is more suitable for short-term modeling and trend forecasting of Internet public opinions’ evolution.
These years, modeling the process of Internet public opinions' evolution and trend forecasting based on that has become a hot topic. The existing short-term trend forecasting method ignores the variability of statistical properties of Internet public opinions’ evolution, which leads to a blind model selection, and the forecasting performance is poor. Therefore, this paper presents an adaptive evolution modeling method of Internet public opinions (AEMIPO). Firstly, this method tracks the statistical characteristics of the process of Internet public opinions' evolution dynamically, such as smoothness, periodicity and self-similarity. Then, by selecting ARMA, ARIMA, SARIMA and FARIMA, an alternative model bank is constructed. Finally, by making model selection rules, an appropriate model is selected to model and forecast the process of evolution adaptively. The experimental results show that compared with the existing methods, AEMIPO has higher forecasting accuracy and stability, and this method is more suitable for short-term modeling and trend forecasting of Internet public opinions’ evolution.
2012, 27(3).
Abstract:
The Bayer GWR algorithm is proposed to obtain the high-quality color image for embedded machine vision system. The method changes the original GWR input parameters according to the structure of Bayer CFA. Using tricolor’s each mean value to replace their sum value in the GW assumes of GWR, and the each mean value of the near white point to replace their maximum in the Retinex assumes of GWR. Test shows: the method can effectively reduce the color temperature with a low data computation and process the 640?480 original image data in the smart camera with 600MHz within 34 ms. It can satisfy the practical application of practical and real-time requirements.
The Bayer GWR algorithm is proposed to obtain the high-quality color image for embedded machine vision system. The method changes the original GWR input parameters according to the structure of Bayer CFA. Using tricolor’s each mean value to replace their sum value in the GW assumes of GWR, and the each mean value of the near white point to replace their maximum in the Retinex assumes of GWR. Test shows: the method can effectively reduce the color temperature with a low data computation and process the 640?480 original image data in the smart camera with 600MHz within 34 ms. It can satisfy the practical application of practical and real-time requirements.
16 Advances in Theory and Application of Compressed Sensing in Radar Target Detection and Recognition
2012, 27(1).
Abstract:
Compressed sensing is a new paradigm in signal processing that trades sampling frequency for computing power and allows accurate reconstruction of signals sampled at rates many times less than the conventional Nyquist frequency. Today, modern radar systems operate with high bandwidths and high resolution. Compared to complex radar system and mass data, often only a small amount of target parameters is the final output. Compressed sensing is one of good means to effectively reduce data size. This paper reviews the latest developments of compressed sensing in radar target detection and recognition and introduces the key technical problems of design of measurement matrix and reconstruction algorithm for sparse signal. Several possible applications are considered: PD radar, through wall radar, MIMO radar, radar target parameter estimation, radar imaging and radar target detection and recognition system. Then this paper also discusses the existing difficult problems in the study and looks into the future research directions on compressive sensing applied to radar.
Compressed sensing is a new paradigm in signal processing that trades sampling frequency for computing power and allows accurate reconstruction of signals sampled at rates many times less than the conventional Nyquist frequency. Today, modern radar systems operate with high bandwidths and high resolution. Compared to complex radar system and mass data, often only a small amount of target parameters is the final output. Compressed sensing is one of good means to effectively reduce data size. This paper reviews the latest developments of compressed sensing in radar target detection and recognition and introduces the key technical problems of design of measurement matrix and reconstruction algorithm for sparse signal. Several possible applications are considered: PD radar, through wall radar, MIMO radar, radar target parameter estimation, radar imaging and radar target detection and recognition system. Then this paper also discusses the existing difficult problems in the study and looks into the future research directions on compressive sensing applied to radar.
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