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Editor Work2016 Vol. 5, No. 4
Previous Issue
2016, 5(4): 333-349.
Abstract
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This paper provides a brief review of the development in Unmanned Aerial Vehicle (UAV) borne SAR technology, and gives a summary on the important areas of UAV SAR, including the operation mode, key facilitating technology, performance and specifications, typical systems and applications. According to the characteristics and attributes of UAV platform, the paper focuses on the current development of high resolution, motion compensation and innovative operation mode of the UAV SAR payload. On the demonstration of high resolution, full polarization and interferometric UAV SAR systems, the technologies of top level design on modular reconfiguration, real-time image formation and multi-dimentional motion compensation involved are introduced in detail. Also, the future development trends of UAV SAR technology is discussed as well.
This paper provides a brief review of the development in Unmanned Aerial Vehicle (UAV) borne SAR technology, and gives a summary on the important areas of UAV SAR, including the operation mode, key facilitating technology, performance and specifications, typical systems and applications. According to the characteristics and attributes of UAV platform, the paper focuses on the current development of high resolution, motion compensation and innovative operation mode of the UAV SAR payload. On the demonstration of high resolution, full polarization and interferometric UAV SAR systems, the technologies of top level design on modular reconfiguration, real-time image formation and multi-dimentional motion compensation involved are introduced in detail. Also, the future development trends of UAV SAR technology is discussed as well.
2016, 5(4): 350-360.
Vacuum Electronic Devices (VEDs) which are considered as the heart of a radar system, play an important role in their development. VEDs and radar systems supplement and promote each other. Some new trends in VEDs have been observed with advancements in the simulation tools for designing VEDs, new materials, new fabrication techniques. Recently, the performance of VEDs has greatly improved. In addition, new devices have been invented, which have laid the foundation for the developments of radar detection technology. This study introduces the recent development trends and research results of VEDs from microwave and millimeter wave devices and power modules, integrated VEDs, terahertz VEDs, and high power VEDs.
Vacuum Electronic Devices (VEDs) which are considered as the heart of a radar system, play an important role in their development. VEDs and radar systems supplement and promote each other. Some new trends in VEDs have been observed with advancements in the simulation tools for designing VEDs, new materials, new fabrication techniques. Recently, the performance of VEDs has greatly improved. In addition, new devices have been invented, which have laid the foundation for the developments of radar detection technology. This study introduces the recent development trends and research results of VEDs from microwave and millimeter wave devices and power modules, integrated VEDs, terahertz VEDs, and high power VEDs.
2016, 5(4): 361-372.
Orthogonal Frequency-Division Multiplexing (OFDM) radar is receiving increasing attention in the radar field in recent years and is showing excellent performance. However, for practical applications, there are several problems with phase-coded OFDM radar, such as the existence of few good codes, limited length capability, and a high Peak-to-Mean-Envelope Power Ratio (PMEPR). To address those problems, in this paper, we propose a design method for a four-phase-coded OFDM radar signal based on Bernoulli chaos, which can construct codes of arbitrary amounts and lengths and demonstrate more agility and flexibility. By adopting original phase weighting, this method can obtain a chaotic four-phase-coded OFDM signal with a PMEPR less than two. This signal has excellent performance with respect to high resolution and Doppler radar application.
Orthogonal Frequency-Division Multiplexing (OFDM) radar is receiving increasing attention in the radar field in recent years and is showing excellent performance. However, for practical applications, there are several problems with phase-coded OFDM radar, such as the existence of few good codes, limited length capability, and a high Peak-to-Mean-Envelope Power Ratio (PMEPR). To address those problems, in this paper, we propose a design method for a four-phase-coded OFDM radar signal based on Bernoulli chaos, which can construct codes of arbitrary amounts and lengths and demonstrate more agility and flexibility. By adopting original phase weighting, this method can obtain a chaotic four-phase-coded OFDM signal with a PMEPR less than two. This signal has excellent performance with respect to high resolution and Doppler radar application.
2016, 5(4): 373-377.
The analytical expression of shortwave interference in the range-Doppler domain is derived and is found to exhibit a constant-amplitude spectrum ridge parallel to the range axis. The spatial nonstationarity of the shortwave interference induced by ionosphere perturbation is then analyzed and is found to be equivalent to the amplitude-phase error between the same frequency points of shortwave interference on different antenna elements; hence, the above mentioned spatial nonstationarity only a has a slight effect on the performance of Adaptive Digital BeamForming (ADBF). On the basis of the above analyses, this paper presents a post-Doppler ADBF approach for skywave radar. This approach involves transforming the received signal in each antenna element into a range-Doppler domain and then performing adaptive processing at each Doppler frequency point. The real radar data processing conducted in this study shows that the ADBF approach has a good interference suppression performance and strong robustness.
The analytical expression of shortwave interference in the range-Doppler domain is derived and is found to exhibit a constant-amplitude spectrum ridge parallel to the range axis. The spatial nonstationarity of the shortwave interference induced by ionosphere perturbation is then analyzed and is found to be equivalent to the amplitude-phase error between the same frequency points of shortwave interference on different antenna elements; hence, the above mentioned spatial nonstationarity only a has a slight effect on the performance of Adaptive Digital BeamForming (ADBF). On the basis of the above analyses, this paper presents a post-Doppler ADBF approach for skywave radar. This approach involves transforming the received signal in each antenna element into a range-Doppler domain and then performing adaptive processing at each Doppler frequency point. The real radar data processing conducted in this study shows that the ADBF approach has a good interference suppression performance and strong robustness.
2016, 5(4): 378-388.
In stepped-frequency radar, the contrast cost function can be used to estimate the target radial velocity and acceleration, and therefore reduce range profile distortions. However, the contrast cost surface fluctuates sharply in the velocity and acceleration space, which greatly limits the efficiency of the algorithm. In this study, the cause of this fluctuation is analyzed and its elimination is formulated analytically through strict formula derivation. Based on intensive study of the inherent properties of the contrast cost surface, a novel and highly efficient target motion compensation algorithm is presented. Theoretical analysis and simulations confirm the effectiveness and feasibility of this new algorithm.
In stepped-frequency radar, the contrast cost function can be used to estimate the target radial velocity and acceleration, and therefore reduce range profile distortions. However, the contrast cost surface fluctuates sharply in the velocity and acceleration space, which greatly limits the efficiency of the algorithm. In this study, the cause of this fluctuation is analyzed and its elimination is formulated analytically through strict formula derivation. Based on intensive study of the inherent properties of the contrast cost surface, a novel and highly efficient target motion compensation algorithm is presented. Theoretical analysis and simulations confirm the effectiveness and feasibility of this new algorithm.
Multiple Measurement Vectors ISAR Imaging Algorithm Based on a Class of Linearized Bregman Iteration
2016, 5(4): 389-401.
This study aims to enable steady and speedy acquisition of Inverse Synthetic Aperture Radar (ISAR) images using sparse echo data. To this end, a Multiple Measurement Vectors (MMV) ISAR echo model is studied. This model is then combined with the Compressive Sensing (CS) theory to realize a class of MMV fast ISAR imaging algorithms based on the Linearized Bregman Iteration (LBI). The algorithms involve four methods, and the iterative framework, application conditions, and relationship between the four methods are given. The reconstructed performance of the methods, convergence, anti-noise, and selection of regularization parameters are then compared and analyzed comprehensively. Finally, the experimental results are compared with the traditional Single Measurement Vector (SMV) ISAR imaging algorithm; this comparison shows that the proposed algorithm delivers an improved imaging quality with a low Signal-to-Noise Ratio (SNR).
This study aims to enable steady and speedy acquisition of Inverse Synthetic Aperture Radar (ISAR) images using sparse echo data. To this end, a Multiple Measurement Vectors (MMV) ISAR echo model is studied. This model is then combined with the Compressive Sensing (CS) theory to realize a class of MMV fast ISAR imaging algorithms based on the Linearized Bregman Iteration (LBI). The algorithms involve four methods, and the iterative framework, application conditions, and relationship between the four methods are given. The reconstructed performance of the methods, convergence, anti-noise, and selection of regularization parameters are then compared and analyzed comprehensively. Finally, the experimental results are compared with the traditional Single Measurement Vector (SMV) ISAR imaging algorithm; this comparison shows that the proposed algorithm delivers an improved imaging quality with a low Signal-to-Noise Ratio (SNR).
2016, 5(4): 402-409.
Differences between the spatial pattern (pixel intensity and distribution) of targets and clutter allow target segmentation to be achieved by analyzing spatial patterns in Synthetic Aperture Radar (SAR) images. This paper thus proposes a target segmentation method for SAR images based on the appearance conversion machine theory. The proposed method analyses the spatial patterns in SAR images and calculates the degree of similarity between the SAR image and the reference clutter images. Subsequently, regions that show high similarity to reference clutter images are erased so that segmentation can be achieved. To evaluate the degree of similarity, we also use an automatic threshold selection method based on the cumulative histogram of the similarity imge. Experimental results using simulation and real data verify the effectiveness of the proposed method.
Differences between the spatial pattern (pixel intensity and distribution) of targets and clutter allow target segmentation to be achieved by analyzing spatial patterns in Synthetic Aperture Radar (SAR) images. This paper thus proposes a target segmentation method for SAR images based on the appearance conversion machine theory. The proposed method analyses the spatial patterns in SAR images and calculates the degree of similarity between the SAR image and the reference clutter images. Subsequently, regions that show high similarity to reference clutter images are erased so that segmentation can be achieved. To evaluate the degree of similarity, we also use an automatic threshold selection method based on the cumulative histogram of the similarity imge. Experimental results using simulation and real data verify the effectiveness of the proposed method.
2016, 5(4): 410-418.
To improve the accuracy and stability of built-up area extraction from Synthetic Aperture Radar (SAR) image time series, in this paper, we propose a multilayer neural-network-based built-up area extraction method that combines the characters of time-series images. The proposed method coarsely tags single images and obtains a large number of samples from time-series images that have been processed by a histogram specification procedure. To generate a training sample dataset, we use samples generated from one image to determine network depth and select samples with higher accuracy from the sample set taken from the time-series images. The final model is trained by the selected large and high quality training dataset. We perform two comparison experiments with 38 25-m resolution ENVISAT ASAR images. Using the proposed method, we achieved 90.2% minima accuracy and a 0.725 minima Kappa coefficient, which are much higher than those of the three conventional methods. Thus, the accuracy and stability of built-up area extraction are significantly improved. In addition, the method proposed in this paper has the advantages of requiring minimal manual operation, well generalization, and training efficiency.
To improve the accuracy and stability of built-up area extraction from Synthetic Aperture Radar (SAR) image time series, in this paper, we propose a multilayer neural-network-based built-up area extraction method that combines the characters of time-series images. The proposed method coarsely tags single images and obtains a large number of samples from time-series images that have been processed by a histogram specification procedure. To generate a training sample dataset, we use samples generated from one image to determine network depth and select samples with higher accuracy from the sample set taken from the time-series images. The final model is trained by the selected large and high quality training dataset. We perform two comparison experiments with 38 25-m resolution ENVISAT ASAR images. Using the proposed method, we achieved 90.2% minima accuracy and a 0.725 minima Kappa coefficient, which are much higher than those of the three conventional methods. Thus, the accuracy and stability of built-up area extraction are significantly improved. In addition, the method proposed in this paper has the advantages of requiring minimal manual operation, well generalization, and training efficiency.
2016, 5(4): 419-424.
Geometric positioning accuracy is an important technical indicator for Synthetic Aperture Radar (SAR) in the geomatics domain. Airborne SAR is an important trend in SAR technology due to its advantages of high flexibility, high resolution, and low cost. Motion error and undulating terrain are the major sources of airborne SAR geometric positioning. In this paper, we consider the SAR imaging principle and imaging geometry to carefully examine the mechanism of geometric positioning error caused by the coupling of motion error and terrain fluctuation. Therefore, we propose a fast geometric precision correction method and verify the validity and effectiveness of the proposed method based on simulation results and experimental data.
Geometric positioning accuracy is an important technical indicator for Synthetic Aperture Radar (SAR) in the geomatics domain. Airborne SAR is an important trend in SAR technology due to its advantages of high flexibility, high resolution, and low cost. Motion error and undulating terrain are the major sources of airborne SAR geometric positioning. In this paper, we consider the SAR imaging principle and imaging geometry to carefully examine the mechanism of geometric positioning error caused by the coupling of motion error and terrain fluctuation. Therefore, we propose a fast geometric precision correction method and verify the validity and effectiveness of the proposed method based on simulation results and experimental data.
2016, 5(4): 425-433.
Arc Synthetic Aperture Radar (Arc-SAR) is a new operation-mode radar that is suitable for wide observation in certain areas. With its high resolution, wide observation area, and short repetition visit period, Arc-SAR is widely used in monitoring deformation measurements, landslides, and natural hazards. Because of the special movement of Arc-SAR, the BackproPagation Algorithm (BPA) is widely used in its image processing. Here, we propose a novel two-dimensional frequency domain algorithm to process an Arc-SAR signal. First, we derive a signal model of Arc-SAR based on that of frequency-modulated continuous-wave radar. Next, because the length of the arm is far shorter than the distance of the antenna to the target, the signal can be processed in a 2D frequency domain. Finally, we analyze the error of the frequency method used. This algorithm has the advantages of high computation speed and accuracy. The simulation and experimental data results confirm the effectiveness and validity of the proposed method.
Arc Synthetic Aperture Radar (Arc-SAR) is a new operation-mode radar that is suitable for wide observation in certain areas. With its high resolution, wide observation area, and short repetition visit period, Arc-SAR is widely used in monitoring deformation measurements, landslides, and natural hazards. Because of the special movement of Arc-SAR, the BackproPagation Algorithm (BPA) is widely used in its image processing. Here, we propose a novel two-dimensional frequency domain algorithm to process an Arc-SAR signal. First, we derive a signal model of Arc-SAR based on that of frequency-modulated continuous-wave radar. Next, because the length of the arm is far shorter than the distance of the antenna to the target, the signal can be processed in a 2D frequency domain. Finally, we analyze the error of the frequency method used. This algorithm has the advantages of high computation speed and accuracy. The simulation and experimental data results confirm the effectiveness and validity of the proposed method.
2016, 5(4): 434-443.
As a special working mode, the circular scanning Synthetic Aperture Radar (SAR) is widely used in the earth observation. With the increase of resolution and swath width, the simulation data has a massive increase, which boosts the new requirements of efficiency. Through analyzing the redundancy in the raw data simulation based on Graphics Processing Unit (GPU), a fast simulation method considering reduction of redundant computation is realized by the multi-GPUs and Message Passing Interface (MPI). The results show that the efficiency of 4-GPUs increases 2 times through the redundant reduction, and the hardware cost decreases by 50%, thus the overall speedup achieves 350 times than the traditional CPU simulation.
As a special working mode, the circular scanning Synthetic Aperture Radar (SAR) is widely used in the earth observation. With the increase of resolution and swath width, the simulation data has a massive increase, which boosts the new requirements of efficiency. Through analyzing the redundancy in the raw data simulation based on Graphics Processing Unit (GPU), a fast simulation method considering reduction of redundant computation is realized by the multi-GPUs and Message Passing Interface (MPI). The results show that the efficiency of 4-GPUs increases 2 times through the redundant reduction, and the hardware cost decreases by 50%, thus the overall speedup achieves 350 times than the traditional CPU simulation.
2016, 5(4): 444-452.
For real-time autofocus of defocused images produced by Synthetic Aperture Radar (SAR), the twodimensional autofocus approach proposed in this study is used to correct the residual range cell migration and compensate for the phase error. Next, a block-wise Phase Gradient Autofocus (PGA) is used to correct the space-variant phase error. The Field-Programmable Gate Array (FPGA) design procedures, resource utilization, processing speed, accuracy, and autofocus are discussed in detail. The system is able to autofocus an 8K 8K complex image with single precision within 5.7 s when the FPGA works at 200 MHz. The processing of the measured data verifies the effectiveness and real-time capability of the proposed method.
For real-time autofocus of defocused images produced by Synthetic Aperture Radar (SAR), the twodimensional autofocus approach proposed in this study is used to correct the residual range cell migration and compensate for the phase error. Next, a block-wise Phase Gradient Autofocus (PGA) is used to correct the space-variant phase error. The Field-Programmable Gate Array (FPGA) design procedures, resource utilization, processing speed, accuracy, and autofocus are discussed in detail. The system is able to autofocus an 8K 8K complex image with single precision within 5.7 s when the FPGA works at 200 MHz. The processing of the measured data verifies the effectiveness and real-time capability of the proposed method.
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