In this paper, we propose a novel approach for circular Multiple-Input Multiple-Output (MIMO) array imaging, termed the Partial Equivalent Method (PEM), aimed at sidelobe suppression. In our method, the imaging process of the circular MIMO array is initially decomposed into bistatic circular synthetic aperture radar (BCSAR) components with different bistatic angles. Components with larger bistatic angles produce equivalent channels whose wavenumber spectra are concentrated near zero frequency, leading to significant broadening of the main lobe in the corresponding point spread function (PSF). In traditional MIMO imaging, each transmit–receive antenna pair is considered an equivalent channel, and all these channels are utilized for imaging. However, components with large bistatic angles, when integrated into the MIMO imaging output, result in increased sidelobe levels. To address this issue, we employ the PEM to restrict the range of equivalent channels. This method selectively retains effective channels generated by components with specific bistatic angles, effectively mitigating the adverse effects of BCSAR components with larger bistatic angles. Through point target simulations, electromagnetic simulations, and practical experiments, we demonstrate that the PEM significantly reduces sidelobes and enhances image quality in circular MIMO array imaging.

In satellite synthetic aperture positioning (SAP), the curvature of the Earth’s surface and the curved orbit lead to nonlinear and asymmetric instantaneous Doppler frequencies, especially when dealing with signals of very long durations. This phenomenon significantly affects the accuracy of center frequency estimation and radiating source positioning. This study presents an ultra-high-resolution positioning algorithm designed to process ultra-long-duration data collected by a single satellite. Initially, a method for estimating the zero-Doppler moment based on sub-aperture chirp rates is proposed to obtain an unbiased estimate of the radiation source’s center frequency. Subsequently, a nonlinear instantaneous Doppler compensation method is proposed, utilizing the estimated center frequency and chirp rates to enhance the coherence of the long-duration data. Furthermore, a long coherent positioning is suggested to generate an ultra-high-resolution positioning image. Ultimately, the efficacy of the proposed algorithm is validated through simulations and acquired data.

High-resolution airborne synthetic aperture radar imaging requires long synthetic aperture time (LSAT). However, the LSAT invalidates the Fresnel approximation in the traditional autofocusing method, leaving a residual signal phase in the motion error. To solve the problem, a signal-reconstruction-based phase gradient autofocus (SR-PGA) and a subimage resampling (SIR) methods are proposed. They are developed on the hyperbolic model. First, a subaperture division strategy divides the full-aperture high-order error into multiple low-order subaperture errors (SPEs). Then, the SR-PGA is developed to estimate the SPE, in which the precise deramping is reconstructed to eliminate the residual signal phase in the SPE. Third, the SIR is proposed to eliminate residual Doppler-variant shift of the adjacent subimages, improving the accuracy of the SPE combination. Finally, simulation and actual data processing verify the effectiveness and validity of the algorithm.

In the field of image fusion, spatial detail blurring and color distortion appear in synthetic aperture radar (SAR) images and multispectral (MS) during the traditional fusion process due to the difference in sensor imaging mechanisms. To solve this problem, this paper proposes a fusion method for SAR images and MS images based on a convolutional neural network. In order to make use of the spatial information and different scale feature information of high-resolution SAR image, a dual-channel feature extraction module is constructed to obtain a SAR image feature map. In addition, different from the common direct addition strategy, an attention-based feature fusion module is designed to achieve spectral fidelity of the fused images. In order to obtain better spectral and spatial retention ability of the network, an unsupervised joint loss function is designed to train the network. In this paper, the Sentinel 1 SAR images and Landsat 8 MS images are used as datasets for experiments. The experimental results show that the proposed algorithm has better performance in quantitative and visual representation when compared with traditional fusion methods and deep learning algorithms.

Abstract Accurate frequency offset estimation is the key to correct receiving of low-Earth-orbit (LEO) satellite signals. Under the circumstances of low signal-to-noise ratio (SNR) and large frequency offset, conventional estimation algorithms for frequency offset are limited by their estimation range and/or SNR threshold, for which reason the estimation accuracy cannot achieve the ideal error probability. Based on joint iterative method, this study proposes an improved joint estimation algorithm for frequency offset. Specifically, the coarse estimation stage takes advantage of the wide estimation range of M&M algorithm, thereby enlarging the estimation range of the proposed algorithm while improving the estimation accuracy of the M&M algorithm through conducting iterative estimation. The fine estimation stage integrates the high estimation accuracy of L&R algorithm to capture the residual frequency offset after accomplishing the coarse estimation, reducing the estimation error within low SNR region while lowering the SNR threshold of the proposed algorithm. The simulation results suggest that the proposed algorithm outperforms the commonly used conventional ones in estimating the frequency offset of LEO satellite signals with wide estimation range and low SNR threshold.

The discrete fractional Fourier transform is an excellent tool in non-stationary signal processing. And an efficient and accurate computation is important for the two-dimensional discrete fractional Fourier trans-form (2D DFRFT). Inspired by the sparse Fourier transform algorithm, we propose a two-dimensional sparse fractional Fourier transform (2D SFRFT) algorithm to estimate the fractional Fourier spectrum effi-ciently. Compared with existing methods, we have achieved the lowest runtime and sample complexity. Moreover, by analyzing the errors due to noises, the 2D SFRFT algorithm is improved to be robust. The applications in image fusion, parameter estimation of multicomponent 2D chirp signal and complex ma-neuvering targets in SAR radar demonstrate the effectiveness of the proposed algorithms.(c) 2022 Published by Elsevier B.V.

Multiple azimuth channels (MACs) synthetic aperture radar (SAR) can theoretically achieve high azimuth resolution and wide swath (HRWS). Nevertheless, in practice, channel mismatch will lead to ghost or azimuth ambiguities, which will degrade the imaging quality. This article proposes a novel idea for estimating the channel mismatch of MACs SAR in the image domain. First, we found that the degree of freedom (DOF) of MACs signals doubles after signal reconstruction and imaging. As a result, when the channel number is not great enough, the subspace method for error estimation is unable to be implemented. To deal with this problem, we introduce a DOF compression method based on spectral filtering. This method can decrease the image-domain DOF. Finally, an image-domain subspace method is proposed to estimate the channel phase error, using the focused data and selecting the high SNR region of SAR images. The proposed method has advantages for the channel phase error estimation. Simulated space-borne MACs SAR data and real measured airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

High resolution and wide-swath imaging always suffer channel errors of the multiple azimuth channels (MACs) synthesis aperture radar (SAR). This article presents an image-domain channel error estimation algorithm based on image subspace least square (ISP-LS) method and a postimaging reconstruction algorithm for MACs SAR. The proposed method mainly consists of three parts: first, preprocessing and SAR imaging; second, the ISP-LS-based channel error estimation and calibration algorithm; third, postimaging reconstruction and ambiguity suppression. The channel phase and baseline errors are joint-estimated based on image subspace after SAR imaging, providing advantages that the higher signal-to-noise ratio (SNR) regions SAR images and the subspace method can be used to achieve a more accurate estimate with a relatively low computational load. We also propose a postimaging reconstruction method for ambiguity suppression, which can realize imaging each channel data and then combining the multichannel SAR images. Simulated and acquired airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

Spaceborne synthetic aperture radar (SAR) can operate at various modes, including stripmap mode, spotlight mode, sliding spotlight mode, and Terrain observation by progressive scans (TOPS) mode. These four imaging modes can be regarded as unified, differing in rotation-center ranges. To uniformly focus the data of these four imaging modes in real-time, this article proposes a real-time unified focusing algorithm (RT-UFA) for the multi-mode SAR via azimuth sub-aperture complex-valued image combining and scaling. The imaging processing can be performed while the data are being recorded. In the first stage of imaging, sub-aperture complex-valued images with relative low-resolution can be obtained by the cascade of the extended chirp scaling (ECS) and azimuth dechirp. Then, these complex-valued images are coherently combined by shifting the integer number of pixels, and thus the full-resolution image of all the recorded data can be obtained. The azimuth scaling and the pixels shifting in the RT-UFA are analyzed in detail. Simulation and SAR data results are presented to validate the analysis and RT-UFA.

The synchrosqueezing transform is a time-frequency (TF) analysis tool to process non-stationary signals. Unfortunately, it does not produce accurate TF results when faced with LFM signals. In this paper, we propose synchrosqueezing-Hough transform (SS-HT) to address this problem. First, we introduce Hough transform to obtain more accurate instantaneous frequency (IF) estimation. Then, in order to improve the TF concentration, a new TF rearrangement operator is constructed. Furthermore, we demonstrated that SS-HT has the ability to reconstruct the signal. The experimental results prove that SSHT can not only obtain high TF concentration, but also improve the accuracy of IF estimation and parameters estimation of LFM signals.

The fast approximation algorithm of non-uniform discrete Fourier transform (NUDFT) is an important issue in signal processing. In this paper, a novel estimation algorithm is constructed for NUDFT-II, which is the general form of the sparse Fourier transform (SFT). Firstly, we propose the cyclic convolution in the non-uniform frequency domain and derive the product and convolution theorem. Secondly, the relationship is deduced between the inverse NUDFT of a uniformly sampled sequence in the frequency domain and that of a non-uniformly sampled sequence. Then, based on the relationship and cyclic convolution, we establish a random permutation operation, a non-uniform flat window filtering operation, and a frequency subsampling operation for NUDFT-II to compress sparse signal length. Meanwhile, a method to estimate the significant frequencies' locations and values is introduced. Finally, we propose the non-uniform sparse Fourier transform (NUSFT). Simulation results demonstrate that the proposed method has low complexity in sample and runtime, and has high robustness. Furthermore, the NUSFT algorithm has been applied to signal detection and reconstruction, which shows that the required signals are obtained accurately. And the NUSFT can detect frequencies that cannot be detected by the SFT.

The time difference of arrival (TDOA) and frequency difference of arrival (FDOA) between two receivers are widely used to locate an emitter. Algorithms based on cross ambiguity functions can simultaneously estimate the TDOA and FDOA accurately. However, the algorithms, including the joint processing of received data, require transferring a large volume of data to a central computing unit. It can be a heavy load for the data link, especially for a wideband signal obtained at a high sampling rate. Thus, we proposed a multi-pulse cross ambiguity function (MPCAF) to compress the data before transmitting and then estimate the TDOA and FDOA with the compressed data. The MPCAF consists of two components. First, the raw data are compressed with a proposed two-dimensional compression function. Two methods to construct a reference pulse used in the two-dimensional compression function are considered: a raw data-based method constructs the pulse directly from the received signal, and a signal parameter-based method constructs it through the parameters of the received signal. Second, a wideband cross-correlation function is studied to refine the TDOA and FDOA estimates with the compressed data. The simulation and Cramer–Rao lower bound (CRLB) analyses show that the proposed method dramatically reduces the data transmission load but estimate the TDOA and FDOA well. The hardware-in-the-loop simulation confirms the method’s effectiveness.

Small satellite synthetic aperture radar (SAR) has become a new development direction of spaceborne SAR due to its advantages of flexible launch, short development cycle, and low cost. However, there are fewer researches on distributed small satellite multiple input multiple output (MIMO) SAR. This paper proposes an ultra-high resolution imaging method for the distributed small satellite spotlight MIMO-SAR, which applies the sub-aperture division technique and the sub-aperture image coherent fusion algorithm to MIMO-SAR. After deblurring the sub-aperture signal, the large bandwidth signal is obtained by using an improved time domain bandwidth synthesis (TBS) method, and then the ultra-high resolution image is obtained by using a sub-aperture image coherent fusion algorithm. Simulation results validate the feasibility and effectiveness of the proposed approach.

This article presents a fast back-projection (BP) algorithm based on subaperture (SA) image coherent combination in a downsampled Cartesian coordinate grid for high squint diving terrain observation by progressive scans (HSD-TOPS) synthetic aperture radar (SAR) ground plane imaging. A two-step spectrum compression (SC) method is proposed to coherently combine the aliasing SA images by exploiting the relationship between the wavenumber and the image frequency. The first-step SC is introduced to align the spectrum support region centers. The second-step SC effectively corrects the space-variant spectrum inclination. The proposed algorithm does not need interpolation in the process of image combination, which ensures the accuracy and the efficiency of the algorithm. Furthermore, the SC method is well-modified to suppress the sidelobes of the focused image. Simulation and measured data processing verify the effectiveness of the proposed method.

合成孔径雷达(SAR)卫星的探测范围有限,全轨利用率不高,并且单个卫星的计算和存储资源有限,难以完成大量的星载合成孔径雷达数据处理.本文拟采用多处理板联合的数据处理方法,针对星上分布式计算构建一种模拟系统,以提高星上数据处理效率,验证多处理板联合的数据处理方法在高效利用卫星计算与存储资源、提升单个卫星全轨利用率等方面的优点.本系统基于距离多普勒(range Doppler,RD)成像算法的多卫星分布式SAR实时处理方法,并以现场可编程门阵列(FPGA)芯片为核心构建了分布式计算模拟系统.不同于传统的单个卫星RD算法处理过程,该系统将处理过程分为三个阶段.每个阶段内运算任务被合理地分配给不同的数据处理单元.利用高分三号卫星(GF-3)SAR原始数据进行成像处理,以检验方法和系统的性能.

For high speed maneuvering platforms, multichannel synthetic aperture radar (SAR) can realize wide swath imaging more flexibly at a high squint. In this mode, the signal reconstruction and imaging is a challenging task because the direction of the channel array vector is time-variant and inconsistent with the radar velocity vector. In this paper, the properties of space time spectrum are analyzed in detail at first. It is found that the space time spectrum of the signal is irregular, in which the space time spectral lines are nonlinear and there is a massive Doppler spectrum shift. Therefore, a range-dependent signal reconstruction method based on space time spectrum correction is proposed to obtain the unambiguous Doppler spectrum. For wide-swath data processing, an improved Omega-K approach based on time domain spectrum compression is further proposed to obtain a well-focused image. A modified Stolt mapping is used to address the range variations of range cell migration (RCM). Subsequently, a time domain spectrum compression function is used to eliminate the time domain aliasing of small-aperture data without zero-padding. Simulation results and real data processing are presented to validate the proposed algorithm. ? 2021 Elsevier B.V. All rights reserved.

针对分布式小卫星多发多收合成孔径雷达的聚束工作模式,提出了一种超高分辨成像方法,降低了多通道高分辨率模式下卫星的存储压力以及成像负荷.该方法首先将各通道的全孔径信号划分为子孔径信号;然后对子孔径信号进行解模糊处理,并利用改进的时域带宽合成方法获得大带宽信号;再使用子孔径图像相干融合算法获得超高分辨率图像.仿真实验表明,改进的时域带宽合成方法能够有效地合成带宽,并且所提方法的成像效果良好.

DInSAR技术可快速获取高空间分辨率的形变信息,融合精密水准测量数据获得更为精确的形变,有助于其在城市地面沉降和滑坡地质灾害监测的应用.以天津市蓟州区五名山为例,研究融合DInSAR与精密水准测量的滑坡体形变监测.利用二轨DInSAR技术,获取了天津市蓟州区五名山近两年的DInSAR形变,通过与精密水准测量相比较,构建基于精密水准测量的InSAR形变校正模型,对校正模型进行了验证,获得了蓟州区北部山区校正的DInSAR形变.研究结果表明:与精密水准测量相比较,校正前后的DInSAR形变,均方根误差分别为3.69 mm和2.64 mm,校正模型可改善DInSAR形变.由于矿山开采,蓟州区北部山区2017-2018年的垂直形变达到38 mm,形变较大地区集中在采矿区的周边及山区.

The special imaging mechanism of the Synthetic Aperture Radar (SAR) causes the sidelobe effect on SAR images. In target detection, the sidelobe effect changes the shapes of strong reflective targets, which results in the problems of localization difficulty and localization error. To solve this problem, this paper proposes a ship detection algorithm based on Spatially Variant Apodization (SVA) and Order Statistic-Constant False Alarm Rate (OS-CFAR). First, the global-CFAR algorithm is used to prescreen the potential target points, which reduces the computational burden of the following steps. Second, the SVA algorithm is modified to improve the speed of sidelobe suppression and applied to the raw complex image data. Then, the nonlinear method OS-CFAR is used to detect the targets on the processed image, and the morphological dilation processing is used to make up for the wrong suppressed points caused by the SVA algorithm. Finally, the GF-3 SAR images are used to test the algorithm and the comparison of the image contrast and detected numbers in the results with SVA and without SVA verifies the effectiveness of the proposed algorithm.

The small satellite SAR has received increasing attention due to its flexibility and low cost. But limited by the data transmission technology, real-time transmission of a large amount of raw data generated by the spaceborne spotlight SAR can hardly be achieved. Meanwhile, the azimuth bandwidth of the spotlight mode is larger than the PRF, resulting in aliasing of the azimuth spectrum. Based on these, this paper proposes a real-time scheme for small satellite SAR with spotlight mode. The method can solve the problem of data transmission and eliminate spectrum overlap in Doppler domain by means of sub-aperture processing. The modified range migration algorithm (RMA) is used to perform range compression and range cell migration compensation (RCMC) on sub-aperture data. Then dechirp in the azimuth time domain is applied to obtain the low-resolution complex image focused in the range time-azimuth frequency domain. Finally, all theected onto a grid image with azimuth interval matching the azimuth full-resolution to complete image fusion.