Synthetic Aperture Radar Imaging System Research Articles

Advancements in Spaceborne Synthetic Aperture Radar Imaging with System-on-Chip Architecture and System Fault-Tolerant Technology

With the continuous development of satellite payload and system-on-chip (SoC) technology, spaceborne real-time synthetic aperture radar (SAR) imaging systems play a crucial role in various defense and civilian domains, including Earth remote sensing, military reconnaissance, disaster mitigation, and resource exploration. However, designing high-performance and high-reliability SAR imaging systems that operate in harsh environmental conditions while adhering to strict size, weight, and power consumption constraints remains a significant challenge. In this paper, we introduce a spaceborne SAR imaging chip based on a SoC architecture with system fault-tolerant technology. The fault-tolerant SAR SoC architecture has a CPU, interface subsystem, memory subsystem, data transit subsystem, and data processing subsystem. The data processing subsystem, which includes fast Fourier transform (FFT) modules, coordinated rotation digital computer (CORDIC) modules (for phase factor calculation), and complex multiplication modules, is the most critical component and can achieve various modes of SAR imaging. Through analyzing the computational requirements of various modes of SAR, we found that FFT accounted for over 50% of the total computational workload in SAR imaging processing, while the CORDIC modules for phase factor generation accounted for around 30%. Therefore, ensuring the fault tolerance of these two modules is crucial. To address this issue, we propose a word-length optimization redundancy (WLOR) method to make the fixed-point pipelined FFT processors in FFT modules fault tolerant. Additionally, we propose a fault-tolerant pipeline CORDIC architecture utilizing error correction code (ECC) and sum of squares (SOS) check. For other parts of the SoC architecture, we propose a generic partial triple modular redundancy (TMR) hardening method based on the HITS algorithm to improve fault tolerance. Finally, we developed a fully automated FPGA-based fault injection platform to test the design’s effectiveness by injecting errors at arbitrary locations. The simulation results demonstrate that the proposed methods significantly improved the chip’s fault tolerance, making the SAR imaging chip safer and more reliable. We also implemented a prototype measurement system with a chip-included board and demonstrated the proposed design’s performance on the Chinese Gaofen-3 strip-map continuous imaging system. The chip requires 9.2 s, 50.6 s, and 7.4 s for a strip-map with 16,384 × 16,384 granularity, multi-channel strip-map with 65,536 × 8192 granularity, and multi-channel scan mode with 32,768 × 4096 granularity, respectively, and the system hardware consumes 6.9 W of power to process the SAR raw data.

Static Hand Gesture Recognition Based on Millimeter-Wave Near-Field FMCW-SAR Imaging

To address the limitations of wireless sensing in static gesture recognition and the issues of Computer Vision’s dependence on lighting conditions, we propose a method that utilizes millimeter-wave near-field SAR (Synthetic Aperture Radar) imaging for static gesture recognition. First, a millimeter-wave near-field SAR imaging system is used to scan the defined static gestures to obtain data. Then, based on the distance plane, the three-dimensional gesture is divided into multiple two-dimensional planes, constructing an imaging dataset. Finally, an HOG (Histogram of Oriented Gradients) is used to extract features from the imaging results, PCA (Principal Component Analysis) is applied for feature dimensionality reduction, and RF (Random Forest) performs classification. Experimental verification shows that the proposed method achieves an average recognition precision of 97% in unobstructed situations and 93% in obstructed situations, providing an effective means for wireless-sensing-based static gesture recognition.

Proposed SAR range focusing algorithm based on simulation analysis and SDR implementation

Synthetic aperture radar (SAR) imaging systems use pulse focusing techniques for achieving fine range resolution and large range detection. Stretch processing and correlation pulse focusing techniques are the most popular algorithms used in the radar signal processing stage. The architecture of the stretch processing technique makes it commonly used in frequency modulated continuous wave (FMCW) radar systems due to its simple architecture, low sampling rate, and inexpensive hardware. This paper discusses how the stretch range focusing approach can be used instead of the matched filter technique in the processing stage of the SAR Range-Doppler algorithm (RDA) to generate images of finer resolution and higher quality. In addition, validating the proposed algorithm and evaluating its performance according to some evaluation indices such as peak sidelobe ratio (PSLR), integrated sidelobe ratio (ISLR), and the impulse response width (IRW). Also, this paper presents a comprehensive analysis and hardware implementation of the two SAR algorithms, based on software-defined radio (SDR) architecture where it is characterized by less complexity, low cost, and high flexibility due to the usage of both software data processing and software waveform generation. Two Universal Software Radio Peripheral (USRP-2932) and NI-LabVIEW® software development environment are the platforms used for the hardware validation process. The transmitted waveform is a linear frequency modulated (LFM) signal generated by the first USRP (transmitter) which will hit a target located at a certain distance. The second USRP is used as a receiver for target’s echo processing and detection. This processing stage contains two architectures, which are the matched filter pulse compression architecture and the stretch range focusing approach. Finally, the validation process of the hardware results is performed based on the pre-mentioned evaluation indices.

Structural displacement monitoring using ground-based synthetic aperture radar

This study develops a ground-based synthetic aperture radar (GBSAR) imaging system and interferometric processing framework for structural health monitoring (SHM). Radar remote sensing for displacement monitoring plays a vital role in SHM. Radar sensors and, more specifically, GBSAR systems have gained more attention in recent studies due to their capability to overcome the limitations of other contact or non-contact SHM sensors. This paper proposes a three-dimensional SAR imaging system and advanced signal processing scheme to extract continuous (fast) displacement components, such as structural vibrations, and discontinuous (slow) displacements, such as subsidence or uplift, using SAR time-series datasets. Using controlled experiments, this study shows that the developed GBSAR can monitor sub-millimeter displacements with high spatial resolution and 0.02 mm precision in the LOS direction. Moreover, several simulations are carried out to evaluate the proposed displacement monitoring algorithms. Accordingly, the proposed continuous monitoring framework can measure fast LOS displacements with better than 0.03 mm precision by reducing the effects of radar movement, clutters, and disturbances. Also, evaluations on a simulated monitoring scene at 20 m from the GBSAR’s LOS show that the proposed discontinuous displacement monitoring algorithm can estimate 3D displacement vectors with sub-millimeter precision. The results show that the developed GBSAR can be used for repeat-pass LOS displacement monitoring and the proposed algorithms demonstrate high potential for measuring continuous sub-second LOS displacements and long-term 3D displacement vectors.

Synthetic aperture radar target recognition via deep attention convolutional network assisted by multiscale residual despeckling network

Owing to the inherent characteristics of synthetic aperture radar (SAR) imaging system based on the coherent imaging modality, SAR images are inevitably corrupted by speckle noise, thereby affecting SAR target recognition accuracy. We explore the impact of speckle noise on SAR target recognition performance. Subsequently, we propose a deep attention convolutional network assisted by a multiscale residual despeckling network to improve SAR target recognition accuracy under speckle corruption. Noise information is first learned via a despeckling subnetwork consisting of dual-branch multiscale feature extraction, feature fusion, and adaptive feature channel selection. Then a classification subnetwork with a cross-dimension interaction attention mechanism is designed to realize feature extraction and identity reasoning of SAR targets. Experimental results on the benchmark moving and stationary target acquisition and recognition dataset demonstrate the effectiveness and superiority of the proposed method.

Optical and SAR Image Dense Registration Using a Robust Deep Optical Flow Framework

The coregistration of optical and synthetic aperture radar (SAR) imageries is the bottleneck in exploring the complementary information from the two multimodal datasets. The difficulties lie in not only the complex radiometric relationship between them, but also the distinct geometrical models of the optical and SAR imaging systems, which cause it nontrivial to explicitly depict the spatial relationship between the corresponding image regions when elevation fluctuations exist. This article aims to investigate the optical flow technique for the pixelwise dense registration of the high-resolution optical and SAR images, so as to get rid of the outlier removal and geometric mapping procedures, which have to be conducted in the classical image registration approaches that are based on sparse feature point matching. Herein, a deep optical flow framework is designed. First, a dilated feature concatenation method is proposed to enhance the discriminability of the pixelwise features for similarity measurement. An effective network training strategy is used, based on a smoothed flow loss, and also a training dataset that contains simulated elevation fluctuations. Second, we propose a self-supervised optical flow fine-tuning strategy. It incorporates the strength of the blockwise matching approach, which produces better matching precision, into the proposed pixelwise matching procedure. In this way, the accuracy of the optical-SAR dense registration is substantially improved. Extensive experiments conducted on the 1-m resolution optical-SAR image pairs of different land-cover types and distinct topographic conditions indicate that the proposed optical-SAR optical flow network -Ft framework is quite robust, and has the potential to perform the optical-SAR image dense registration in practical applications. The Python code of the proposed deep optical flow network will be made available.

On the Effects of the Incidence Angle on the L-Band Multi-Polarisation Scattering of a Small Ship

The monitoring of ships is of paramount importance for ocean and coastal area surveillance. The synthetic aperture radar is shown to be a key sensor to provide effective and continuous observation of ships due to its unique imaging capabilities. When advanced synthetic aperture radar imaging systems are considered, the full scattering information is available that was demonstrated to be beneficial in developing improved ship detection and classification algorithms. Nonetheless, the capability of polarimetric synthetic aperture radar to observe marine vessels is significantly affected by several imaging and environmental parameters, including the incidence angle. Nonetheless, how changes in the incidence angle affect the scattering of ships still needs to be further investigated since only a sparse analysis, i.e., on different kinds of ships of different sizes observed at multiple incidence angles, has been performed. Hence, in this study, for the first time, the polarimetric scattering of the same ship, i.e., a small fishing trawler, which is imaged multiple times under the same sea state conditions but in a wide range of incidence angles, is analysed. This unique opportunity is provided by a premium L-band UAVSAR airborne dataset that consists of five full-polarimetric synthetic aperture radar scenes collected in the Gulf of Mexico. Experimental results highlight the key role played by the incidence angle on both coherent, i.e., co-polarisation signature and pedestal height, and incoherent, i.e., multi-polarisation and total backscattering power, polarimetric scattering descriptors. Experimental results show that: (1) the polarised scattering component is more sensitive to the incidence angle with respect to the unpolarised one; (2) the co-polarised channel under horizontal polarisation dominated the polarimetric backscattering from the fishing trawler at lower angles of incidence, while both co-polarised channels contribute to the polarimetric backscattering at higher incidence angles; (3) the HV polarisation provides the largest target-to-clutter ratio at lower incidence angles, while the HH polarisation should be preferred at higher angles of incidence.

A Study on Millimeter Wave SAR Imaging for Non-Destructive Testing of Rebar in Reinforced Concrete.

In this study, we investigate a millimeter wave (mmWave) synthetic aperture radar (SAR) imaging scheme utilizing a low-cost frequency modulated continuous wave (FMCW) radar to take part in non-destructive testing which could be a useful tool for both civilian and military demands. The FMCW radar working in the frequency range from 76 GHz to 81 GHz is equipped with a 2-D moving platform aiming to reconstruct the 2-D image of the shape of the target object. Due to the lab environment containing several devices and furniture, various noise and interference signals from the floor are not avoidable. Therefore, the digital signal processing algorithms are joined to remove the undesired signals as well as improve the target recognition. This study adopts the range migration algorithms (RMAs) on the processed reflected signal data to form the image of the target because of its verified ability in this type of mission. On the other hand, the integration of compressed sensing (CS) algorithms into the SAR imaging system is also researched which helps to improve the performance of the system by reducing the measurement duration while still maintaining the image quality. Three minimization algorithms are used involving the imaging system as the CS solvers reconstruct the radar data before being processed by RMA to form the image. The proposed imaging scheme demonstrates its good ability with high azimuth resolution in the mission of detecting tiny cracks in the rebar of reinforced concrete. In addition, the participation of CS algorithms improves the performance of the scheme as the cracks on the rebar can be located on the images, which are reconstructed from only 30% of the dataset. The comparison of CS solvers shows that ADMM outperforms the other candidates in the reconstruction task.

Joint radar communication system design based on filter bank multicarrier modulation scheme

Since orthogonal frequency division multiplexing (OFDM) signals are sensitive to Doppler shift, OFDM signals with cyclic prefix (CP) not only reduce the communication rate but also result in symmetric pseudo peaks in the ambiguity function of the radar, which eventually lead to the degradation of the performance of the joint radar communication system (JRCS). In this study, a novel JRCS is proposed based on the filter bank multicarrier (FBMC) waveform for the inverse synthetic aperture radar (ISAR) imaging and communication system. On the one hand, FBMC waveforms are capable of low orthogonality between subcarriers, which can counter Doppler and multipath effects. On the other hand, FBMC waveforms do not require CP, so the false targets can be avoided and spectral efficiency can be improved. The influence of multipath effect and Doppler shift on FBMC waveform in an integrated system is analysed in detail, and a Doppler compensation algorithm suitable for FBMC-integrated waveform is proposed. The phase error caused by target motion can be iteratively compensated to obtain a well-focussed ISAR image. Finally, considering the ISAR image entropy after motion compensation as the evaluation metric, extensive simulation experiments verify the noise robustness and effectiveness of the proposed algorithm. Compared with the conventional OFDM and CP-OFDM schemes, the simulation verifies that the proposed waveform design and processing method has better performance in the ISAR imaging and communication integrated system.

Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval.

Near-field high-resolution synthetic aperture radar (SAR) imaging is mostly accompanied by a large number of data acquisition processes, which increases the system complexity and device cost. According to extensive reports, reducing the number of sampling points of a radar in space can greatly reduce the amount of data. However, when spatial sparse sampling is carried out, a ghost normally appears in the imaging results due to the high side lobes generated in the azimuth. To address this issue, a technique is introduced in this paper to recover the blank data through amplitude and phase compensation based on the correlation between sparse array sampling through adjacent points. Firstly, the data sampled by the sparse array is compressed in the range direction to obtain the expected data slices in the same range direction. Then, the blank element of the slice is compensated for with amplitude and phase to obtain full aperture data. Finally, the matched filter method is used to aid in the image reconstruction. The simulation results verified that the method proposed in this paper can effectively reconstruct the image under two kinds of sparse sampling conditions. Thus, a simple single-input single-output (SISO) synthetic aperture radar imaging test bench is established. Compared with the results of a 1 mm (1/4 ) sampling interval, the quality of the reconstructed image under the condition of a 4 mm (1 ) sampling interval still stands using our proposed method. Demonstrated by the experiment, the normalized root-mean-square error(NMSE) is 5.75%. Additionally, when the spatial sampling points are sampled randomly with 30% of the full sampling condition, this method can also restore and reconstruct the image with high quality. Due to the decrease of sampling points, the data volume can be reduced, which is beneficial for improving the scanning speed and alleviating the pressure of data transmission for near-field high resolution SAR imaging systems.

Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data.

Time-domain backprojection algorithms are widely used in state-of-the-art synthetic aperture radar (SAR) imaging systems that are designed for applications where motion error compensation is required. These algorithms include an interpolation procedure, under which an unknown SAR range-compressed data parameter is estimated based on complex-valued SAR data samples and backprojected into a defined image plane. However, the phase of complex-valued SAR parameters estimated based on existing interpolators does not contain correct information about the range distance between the SAR imaging system and the given point of space in a defined image plane, which affects the quality of reconstructed SAR scenes. Thus, a phase-control procedure is required. This paper introduces extensions of existing linear, cubic, and sinc interpolation algorithms to interpolate complex-valued SAR data, where the phase of the interpolated SAR data value is controlled through the assigned a priori known range time that is needed for a signal to reach the given point of the defined image plane and return back. The efficiency of the extended algorithms is tested at the Nyquist rate on simulated and real data at THz frequencies and compared with existing algorithms. In comparison to the widely used nearest-neighbor interpolation algorithm, the proposed extended algorithms are beneficial from the lower computational complexity perspective, which is directly related to the offering of smaller memory requirements for SAR image reconstruction at THz frequencies.

A Low Sidelobe Deceptive Jamming Suppression Beamforming Method With a Frequency Diverse Array

The deceptive jamming technique can threaten a synthetic aperture radar (SAR) imaging system by repeatedly transmitting copied radar signals back to the radar. A conventional phased array can generate only an angle-dependent beam and cannot distinguish the jammer from other targets. Instead, a frequency diverse array (FDA) can offer a range-angle-dependent beampattern by introducing small frequency offsets to array elements, which can be adopted to distinguish signals from different range cells. Thus, considering that the position of the jammer is unknown, it is necessary to suppress the range peak sidelobe as much as possible. To lower this value, this communication proposes a weighted FDA to achieve the optimal solution at the expense of raising other range sidelobes.

A Novel Space-Borne High-Resolution SAR System with the Non-Uniform Hybrid Sampling Technology for Space Targets Imaging

In order to reduce the complexity of the receiving system for wideband signals, a novel space-borne high-resolution synthetic aperture radar (SAR) system with the non-uniform hybrid sampling technology is proposed in this paper. The non-uniform hybrid sampling technology is firstly applied in the SAR imaging system for the detection of space targets. The non-uniform hybrid sampling technology is able to optimize the transmitted and receiving timing of SAR signals, reducing the requirement of the sampling rate of the analog-to-digital converter (ADC) for the reception of the wideband echoes from space targets. Meanwhile, according to the oversampling requirement of SAR imaging in the azimuth direction, a theoretical model of non-uniform hybrid sampling parameters and relative velocity between the SAR system and the space targets is established. A series of simulation experiments with different targets and different non-uniform hybrid parameters are performed, and an X-band SAR imaging experimental system is constructed to verify the effectiveness of the proposed non-uniform hybrid sampling technology. The experimental results show that the imaging resolution is better than 8 cm. When the non-uniform hybrid sampling interval is 15 us, the imaging quality is consistent with imaging results of the Nyquist real-time sampling, and it is easier to implement in the high-resolution imaging for space targets.

A Gaussian Process Classification and Target Recognition Algorithm for SAR Images

Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR data, Gaussian process classification algorithm for SAR images is studied in this paper. In this paper, we introduce the basic principle of Gaussian process, briefly analyze the basic theory of classification and the characteristics of SAR images, provide the evaluation index system of image classification, and give the SAR classification model of Gaussian process. Taking Laplace approximation as an example, several classification algorithms are introduced directly. Based on the two classifications, we propose an indirect multipurpose classification method and a multifunction classification method for two-pair two-Gaussian processes. The SAR image algorithm based on the two categories is relatively simple and achieves certain results.

A Novel Vortex Synthetic Aperture Radar Imaging System: Decreasing the Pulse Repetition Frequency Without Increasing the Antenna Aperture

Synthetic aperture radar (SAR) is an advanced ground-observing remote sensing technology, and high-resolution wide-swath (HRWS) imaging has always been the goal of SAR. In general, an improved azimuth resolution requires a large pulse repetition frequency (PRF), resulting in high system requirements. To overcome this difficulty, azimuth multichannel technology has gradually developed, achieving HRWS imaging and decreasing the PRF by increasing the spatial sampling, i.e., increasing the number of antenna subapertures in the azimuth. This article proposes a theoretical architecture that generates multiple virtual receiving apertures in the azimuth rather than real apertures. The virtual receiving apertures are formed by multiplying the azimuth signals by linear phase histories provided by vortex beams carrying different orbital angular momentum (OAM) modes. Vortex beams with different OAM modes have different oblique phase wavefronts, so virtual receiving positions are generated in the along-track direction. This approach aims to reduce the PRF without increasing the azimuth real receiving aperture. Simulation results demonstrate the effectiveness and limitations of the method. Finally, to overcome the inherent limitations of the method, two possible implementation schemes are proposed.

Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries

In this article, we introduce a novel algorithm for efficient near-field synthetic aperture radar (SAR) imaging for irregular scanning geometries. With the emergence of fifth-generation (5G) millimeter-wave (mmWave) devices, near-field SAR imaging is no longer confined to laboratory environments. Recent advances in positioning technology have attracted significant interest for a diverse set of new applications in mmWave imaging. However, many use cases, such as automotive-mounted SAR imaging, unmanned aerial vehicle (UAV) imaging, and freehand imaging with smartphones, are constrained to irregular scanning geometries. Whereas traditional near-field SAR imaging systems and quick personnel security (QPS) scanners employ highly precise motion controllers to create ideal synthetic arrays, emerging applications, mentioned previously, inherently cannot achieve such ideal positioning. In addition, many Internet of Things (IoT) and 5G applications impose strict size and computational complexity limitations that must be considered for edge mmWave imaging technology. In this study, we propose a novel algorithm to leverage the advantages of non-cooperative SAR scanning patterns, small form-factor multiple-input multiple-output (MIMO) radars, and efficient monostatic planar image reconstruction algorithms. We propose a framework to mathematically decompose arbitrary and irregular sampling geometries and a joint solution to mitigate multistatic array imaging artifacts. The proposed algorithm is validated through simulations and an empirical study of arbitrary scanning scenarios. Our algorithm achieves high-resolution and high-efficiency near-field MIMO-SAR imaging, and is an elegant solution to computationally constrained irregularly sampled imaging problems.

Analysis of Hypersonic Platform-Borne SAR Imaging: A Physical Perspective

The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization of air. The ionized gas forms the plasma sheath wrapped around the hypersonic platform. The plasma sheath will severely affect the propagation of SAR signal and further affect the SAR imaging. Therefore, hypersonic platform-borne SAR imaging should be studied from a physical perspective. In this paper, hypersonic platform-borne SAR imaging under plasma sheath is analyzed. The SAR signal propagation in plasma sheath is computed using scatter matrix method. The proposed SAR signal model is verified by using a ground experiment system. Moreover, the effect of attenuation caused by plasma sheath on SAR imaging is studied under different SAR parameters and plasma sheath. The result shows that attenuation caused by plasma sheath will degrade the SAR imaging quality and even cause the point and area targets to be submerged into the noise. The real SAR images under plasma sheath also illustrate this phenomenon. Furthermore, by studying imaging results under different SAR and plasma parameters, it can be concluded that the severe degradation of SAR imaging quality appears at condition of high plasma sheath electron density and low SAR carrier frequency. The work in this paper will be beneficial for the study of hypersonic platform-borne SAR imaging and design of hypersonic SAR imaging systems in the future.

Integrated Denoised Synthetic Aperture Radar Images for Enhanced Digital Elevation Model Generation

Synthetic aperture radar (SAR) enables imaging of topographic surfaces day and night in different atmospheric conditions. SAR imaging systems record both intensity and phase information of the backscattered signals. Acquired intensity information is often exposed to speckle noise, and gathered phase information is usually corrupted by thermal and other types of noise. Thus, these types of noises have negative effects on interpretation of SAR images. Digital elevation model (DEM) can be generated by interferometric SAR using two SAR images, of the same area, with slightly different look angles. The generated DEM is affected by the corruption of both intensity and phase information. In this paper, a proposed framework of convolutional neural network (CNN) and modified Wiener filter (MWF) is suggested in DEM generation process. The main purpose of the proposed framework is minimizing not only speckle noise of input SAR images but also phase noise of the interferogram. Thus, an enhanced DEM can be generated. Extensive experiments are carried out and different DEMs are generated from original SAR and from both despeckled SAR images and filtered interferogram. Results and comparative analyses show significant improvements in both quality and vertical accuracy of the DEM generated by the proposed hybrid (CNN-MWF) framework.

Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band

Terahertz (THz) imaging technology has received increased attention in recent years and has been widely applied, whereas the three-dimensional (3D) imaging for moving targets remains to be solved. In this paper, an adaptive 3D imaging scheme is proposed based on a single input and multi-output (SIMO) interferometric inverse synthetic aperture radar (InISAR) imaging system to achieve 3D images of moving targets in THz band. With a specially designed SIMO antenna array, the angular information of the targets can be determined using the phase response difference in different receiving channels, which then enables accurate tracking by adaptively adjusting the antenna beam direction. On the basis of stable tracking, the high-resolution imaging can be achieved. A combined motion compensation method is proposed to produce well-focused and coherent inverse synthetic aperture radar (ISAR) images from different channels, based on which the interferometric imaging is performed, thus forming the 3D imaging results. Lastly, proof-of-principle experiments were performed with a 0.2 THz SIMO imaging system, verifying the effectiveness of the proposed scheme. Non-cooperative moving targets were accurately tracked and the 3D images obtained clearly identify the targets. Moreover, the dynamic imaging results of the moving targets were achieved. The promising results demonstrate the superiority of the proposed scheme over the existing THz imaging systems in realizing 3D imaging for moving targets. The proposed scheme shows great potential in detecting and monitoring moving targets with non-cooperative movement, including unmanned military vehicles and space debris.

On-chip data organization and access strategy for spaceborne SAR real-time imaging processor

Spaceborne SAR(synthetic aperture radar) imaging requires real-time processing of enormous amount of input data with limited power consumption. Designing advanced heterogeneous array processors is an effective way to meet the requirements of power constraints and real-time processing of application systems. To design an efficient SAR imaging processor, the on-chip data organization structure and access strategy are of critical importance. Taking the typical SAR imaging algorithm-chirp scaling algorithm-as the targeted algorithm, this paper analyzes the characteristics of each calculation stage engaged in the SAR imaging process, and extracts the data flow model of SAR imaging, and proposes a storage strategy of cross-region cross-placement and data sorting synchronization execution to ensure FFT/IFFT calculation pipelining parallel operation. The memory wall problem can be alleviated through on-chip multi-level data buffer structure, ensuring the sufficient data providing of the imaging calculation pipeline. Based on this memory organization and access strategy, the SAR imaging pipeline process that effectively supports FFT/IFFT and phase compensation operations is therefore optimized. The processor based on this storage strategy can realize the throughput of up to 115.2 GOPS, and the energy efficiency of up to 254 GOPS/W can be achieved by implementing 65 nm technology. Compared with conventional CPU+GPU acceleration solutions, the performance to power consumption ratio is increased by 63.4 times. The proposed architecture can not only improve the real-time performance, but also reduces the design complexity of the SAR imaging system, which facilitates excellent performance in tailoring and scalability, satisfying the practical needs of different SAR imaging platforms.