Multiple-output Synthetic Aperture Radar Research Articles

Multiple-Input Multiple-Output Synthetic Aperture Radar Waveform and Filter Design in the Presence of Uncertain Interference Environment

Multiple-input multiple-output synthetic aperture radar (MIMO-SAR) anti-jamming waveform design relies on accurate prior information about the interference. However, it is difficult to obtain accurate prior knowledge about uncertain intermittent sampling repeater jamming (ISRJ), leading to a severe decline in the detection performance of MIMO-SAR systems. Therefore, this article studies the robust joint design problem of MIMO radar transmit waveform and filter against uncertain ISRJ. We characterize two categories of uncertain interference, including sample length uncertainty and sample-time uncertainty, modeled as Gaussian distribution in different range bins. Based on the uncertain interference model, we formulate the maximizing SINR as a figure of merit, which is a non-convex quadratic optimization problem under specific waveform constraints. Based on the alternating direction method of multipliers (ADMM) framework, a novel joint design algorithm of waveform and filter is proposed. In order to improve the convergence performance of ADMM, the difference in convex functions (DC) programming is applied to the ADMM iterations framework to solve the problem of waveform energy inequality constraint. Finally, numerical results demonstrate the effectiveness and robustness of the proposed method, compared to the existing methods that utilize deterministic interference models in the uncertain ISRJ environment. Moreover, the spaceborne SAR real scene imaging simulations are conducted to evaluate the anti-ISRJ performance.

Development of a High-Sensitivity Millimeter-Wave Radar Imaging System for Non-Destructive Testing.

There is an urgent need to develop non-destructive testing (NDT) methods for infrastructure facilities and residences, etc., where human lives are at stake, to prevent collapse due to aging or natural disasters such as earthquakes before they occur. In such inspections, it is desirable to develop a remote, non-contact, non-destructive inspection method that can inspect cracks as small as 0.1 mm on the surface of a structure and damage inside and on the surface of the structure that cannot be seen by the human eye with high sensitivity, while ensuring the safety of the engineers inspecting the structure. Based on this perspective, we developed a radar module (absolute gain of the transmitting antenna: 13.5 dB; absolute gain of the receiving antenna: 14.5 dB) with very high directivity and minimal loss in the signal transmission path between the radar chip and the array antenna, using our previously developed technology. A single-input, multiple-output (SIMO) synthetic aperture radar (SAR) imaging system was developed using this module. As a result of various performance evaluations using this system, we were able to demonstrate that this system has a performance that fully satisfies the abovementioned indices. First, the SNR in millimeter-wave (MM-wave) imaging was improved by 5.4 dB compared to the previously constructed imaging system using the IWR1443BOOST EVM, even though the measured distance was 2.66 times longer. As a specific example of the results of measurements on infrastructure facilities, the system successfully observed cracks as small as 0.1 mm in concrete materials hidden under glass fiber-reinforced tape and black acrylic paint. In this case, measurements were also made from a distance of about 3 m to meet the remote observation requirements, but the radar module with its high-directivity and high-gain antenna proved to be more sensitive in detecting crack structures than measurements made from a distance of 780 mm. In order to estimate the penetration length of MM waves into concrete, an experiment was conducted to measure the penetration of MM waves through a thin concrete slab with a thickness of 3.7 mm. As a result, Λexp = 6.0 mm was obtained as the attenuation distance of MM waves in the concrete slab used. In addition, transmission measurement experiments using a composite material consisting of ceramic tiles and fireproof board, which is a component of a house, and experiments using composite plywood, which is used as a general housing construction material in Japan, succeeded in making perspective observations of defects in the internal structure, etc., which are invisible to the human eye.

A Novel Intrapulse Beamsteering SAR Imaging Mode Based on OFDM-Chirp Signals

The multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has developed rapidly since its discovery. At the same time, the low-disturbance and high-gain requirements of the MIMO system are continuing to increase. Through the application of digital beamforming (DBF) techniques, the multidimensional waveform encoding (MWE) technique can play a key role in MIMO systems, which can greatly improve the system’s performance, especially the multi-mission capability of radar. Intrapulse beamsteering in elevation is a typical form of multi-dimensional waveform encoding which can greatly improve the transmitting efficiency and multi-mission performance of radar. However, because of the high sensitivity of the DBF technique to height, there is significant deterioration in performance in the presence of terrain undulations. The OFDM (Orthogonal Frequency Division Multiplexing) technique is widely used in communication. Due to the similarity of radar and communication systems and the great waveform diversity of OFDM signals, the OFDM radar has recently begun to emerge as a new radar system, simultaneously, the orthogonality of OFDM signals is in the time and frequency domains, and is not affected by terrain undulation. So, this paper proposes a novel radar mode combining intrapulse beamsteering in elevation and OFDM-Chirp signals, that is, the combination of “beam orthogonality” and “waveform orthogonality”, which can greatly improve the performance and fault tolerance to interference signals. In this manuscript, the system working mode and signal processing flow are introduced in detail, and simulations for both point targets and distributed targets are carried out to verify the feasibility of the proposed mode. Simultaneously, a comparison experiment is carried out, which shows the high level of fault tolerance to terrain undulation and the high potential of the proposed radar mode in Earth observation.

Object Recognition for Millimeter Wave MIMO-SAR Images Based on High-Resolution Feature Recursive Alignment Fusion Network

There are several complex situations in recognizing concealed objects from millimeter wave multiple-input multiple-output synthetic aperture radar (MIMO-SAR) security images, such as incomplete imaging of objects, partially occluded objects, and overlapping objects, which are detrimental to the accurate recognition of concealed objects. To solve these problems, a concealed object detection method based on a high-resolution feature recursive alignment fusion network (HR-FRAFnet) is proposed. The HR-FRAFnet can segment the object area from the grayscale image with complex human background and complete the recognition. The overall architecture of the HR-FRAFnet follows the encoder-decoder framework. Specifically, in the encoder stage, a deep parallel feature extraction network (DPFEN) connects the multi-resolution feature maps in parallel and repeats multi-scale feature fusion. This approach suppresses the background noise flowing and retains more recognizable target characteristics. Then, in the decoder stage, a feature recursive alignment fusion module (FRAFM) is designed to enhance the perception of object edges. The FRAFM effectively improves the segmentation accuracy of objects whereas decreasing the computational complexity of the network. Besides, we employ a combined loss function to alleviate the foreground-background imbalance problem in MIMO-SAR images. Homemade human security screening image datasets are used for evaluation. The experimental results show that the proposed method outperforms existing semantic segmentation methods in Mean Intersection over Union (mIoU) and reduces the incidence of missed and error detection of targets.

Ground-Based MIMO-SAR Fast Imaging Algorithm Based on Geometric Transformation

Ground-based multiple-input multiple-output synthetic aperture radar (MIMO-SAR) is a new type of deformation monitoring sensor that has the advantages of no mechanical motion and fast echo acquisition. Ground-based MIMO-SAR can significantly improve the data rate of deformation monitoring. In this paper, a fast imaging algorithm tailored for ground-based MIMO-SAR data is proposed, which can be applied in both far-field and near-field scenarios. First, the phase center approximation error of the non-collinear array in the near field is analyzed. Then, a fast imaging algorithm based on geometric transformation for the coherent synthesis of subimages is put forward. The algorithm uses the geometric transformation to convert the subaperture imaging results into the full aperture coordinate system, which avoids the point-by-point interpolation calculation and further reduces the computational cost of the subimage coherent synthesis algorithm. Simulations and experiments show that the algorithm can achieve high-precision focusing imaging, and its operation efficiency is significantly improved compared with the algorithm based on interpolation.

MIMO-SAR Interferometric Measurements for Wind Turbine Tower Deformation Monitoring

Deformations affect the structural integrity of wind turbine towers. The health of such structures is thus assessed by monitoring. The majority of sensors used for this purpose are costly and require in situ installations. We investigated whether Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO-SAR) sensors can be used to monitor wind turbine towers. We used an automotive-grade, low-cost, off-the-shelf MIMO-SAR sensor operating in the W-band with an acquisition frequency of 100 Hz to derive Line-Of-Sight (LOS) deformation measurements in ranges up to about 175 m. Time series of displacement measurements for areas at different heights of the tower were analyzed and compared to reference measurements acquired by processing video camera recordings and total station measurements. The results showed movements in the range of up to 1 m at the top of the tower. We were able to detect the deformations also with the W-band MIMO-SAR sensor; for areas with sufficient radar backscattering, the results suggest a sub-mm noise level of the radar measurements and agreement with the reference measurements at the mm- to sub-mm level. We further applied Fourier transformation to detect the dominant vibration frequencies and identified values ranging from 0.17 to 24 Hz. The outcomes confirmed the potential of MIMO-SAR sensors for highly precise, cost-efficient, and time-efficient structural monitoring of wind turbine towers. The sensors are likely also applicable for monitoring other high-rise structures such as skyscrapers or chimneys.

Efficient Millimeter-Wave Imaging Algorithm for Layered Dielectrics Based on MIMO-SAR With Nonuniform Transmitting Array

In this letter, an efficient near-field millimeter-wave (MMW) imaging algorithm is presented for layered dielectrics based on multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) with nonuniform transmitting array. First, the model of target echo under MIMO-SAR configuration is built by dyadic Green's function, and the precise spectral echo expression is obtained through fast Fourier transform (FFT) and spherical-wave decomposition. Then, the image reconstruction of sparse MIMO-SAR is divided into separate single-input–multiple-output (SIMO)-SAR imaging problems, and the target's subimage corresponding to different SIMO-SAR setup can be quickly solved by employing inverse FFT, multistep phase compensation and wavenumber integration. Finally, the fully focused MIMO-SAR reconstructed image will be obtained by coherently accumulating all the SIMO-SAR results. Simulation analysis and experimental results validate the effectiveness the proposed algorithm.

A Novel MIMO-SAR Echo Separation Solution for Reducing the System Complexity: Spectrum Preprocessing and Segment Synthesis

The problem of echo separation using digital beamforming (DBF) on receive for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is of notable importance to allow for practical systems. Regrettably, current DBF-MIMO-SAR schemes, such as the short-term shift-orthogonal (STSO) scheme, are computationally cumbersome, increasing the required hardware complexity. To alleviate this problem, we here propose an improved echo separation solution for realizing a low-cost MIMO-SAR system. We detail a generic waveform design scheme as well as optimized monostatic radar waveforms (e.g., nonlinear frequency modulation (NLFM) signal) showing how these can be directly adopted in the proposed scheme to improve the imaging performance. The proposed scheme enables the number of the interference segments generated by unmatched waveforms to be halved by the use of the fast time spectrum preprocessing and segment synthesis, dramatically simplifying the array configuration and reduces the system complexity. By exploiting inter-pulse phase coding techniques, the proposed method can provide a reconfigurable waveform transmitting scheme, allowing the system resources in range frequency, elevation space, and Doppler domains to be jointly exploited for the separation of aliased signal returns. The proposed scheme is evaluated using extensive numerical and measured data sets, demonstrating the feasibility and potential of the proposed method for resource-limited spaceborne/airborne MIMO-SAR systems.

An Efficient MMW 3-D Imaging Algorithm for Near-Field MIMO-SAR With Nonuniform Transmitting Array

In this letter, an efficient millimeter-wave (MMW) frequency-domain imaging algorithm is presented for 3-D object reconstruction of the near-field multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) with nonuniform transmitting array. For each single-input–multiple-output (SIMO) case of the MIMO-SAR setup, the precise spectral echo expression is derived by spherical-wave decomposition and fast Fourier transform (FFT) operations. To obtain the linear phase in range direction, the multiorder approximation theory is employed to expand the coupling phases, and thus the target subimage can be reconstructed by utilizing phase compensation, central wavenumber approximation, and inverse FFT. Finally, the fully focused 3-D image for MIMO setup will be realized through accumulating all the SIMO subimages. Compared with the existing representative algorithms, our method avoids complicated interpolation and achieves high-quality fast imaging with only matrix multiplication and a small amount of iteration operations. The experimental results validate the effectiveness of the proposed algorithm.

A Two-Stage Echo Separation Scheme for Spaceborne MIMO-HWRS SAR System

Multiple-input and multiple-output synthetic aperture radar (MIMO SAR) is a very potential technique for high-resolution and wide-width (HRWS) imaging due to the fact that it can provide more degrees of freedom. In this paper, an innovative MIMO-HRWS SAR imaging system is introduced with segmented phase coding (SPC) waveforms. However, it is well known that the echo separation issue is the most technical challenge for MIMO SAR. It has shown that the separation of orthogonal echoes from cross-correlation interference can be achieved by scan-on-receive (SCORE). However, this method often requires large computing resources on board, especially for null-steering process. For this, we propose a two-stage spaceborne-ground echo separation scheme. This scheme is mainly divided into two-steps: the first step is a real-time SCORE-beamforming process on board to reduce the downlink data volume, and the second step is a bandpass-null steering process to suppress the interfering echoes on the ground. Thus, this scheme allows for the low computing resources on board and sufficient suppression of the interference simultaneously. Following this scheme, the MIMO-HRWS SAR system enables its number of equivalent phase centers nearly double that of the azimuth multichannel SAR system, thus higher-resolution and wider-swath imaging. Finally, detailed simulation experiments for the MIMO-HRWS SAR imaging system are performed to verify the practicability and feasibility of the proposed scheme.

First Demonstration of Echo Separation for Orthogonal Waveform Encoding MIMO-SAR Based on Airborne Experiments

Multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) has extensive application prospects, mainly including the acquisition of multidimensional scattering information, high-resolution and wide-width (HRWS) imaging, and moving target indication (MTI). Its echo separation is the most technical challenge, and so far, the confirmation for orthogonal waveform encoding MIMO-SAR by airborne experiments has not been reported in any literature. Here, an echo separation experiment based on the segmented phase code (SPC) waveforms and an airborne digital beamforming SAR (DBF-SAR) system is demonstrated for the first time. In the experiment, the SPC waveforms are cyclically transmitted within the adjacent pulse repetition intervals (PRIs) to simulate multiple transmitters, and the scattered echoes are received by the 16-channel antennas in elevation at the same time. In the postprocessing, the echo signals of continuous PRIs are added to obtain the mixed echoes, and a detailed echo separation method is adopted. In the method, the mixed echo signals from close arrival angles and far arrival angles are separated by the time shift and weighting, and by the bandpass filtering and DBF technique, respectively. Through the presented method, the mixed echoes of dual-transmit and 16-receive (2T16R) SAR imaging mode are separated and imaged successfully. The experimental results not only validate the echo separation scheme but also indicate that it is very promising in future MIMO-SAR missions.

Spatially Variant Sidelobe Suppression for Linear Array MIMO SAR 3-D Imaging

Linear array (LA) multiple-input–multiple-output (MIMO) synthetic aperture radar (SAR) has the capacity of achieving 3-D images in a single pass. If processed by matched filtering-based linear 3-D imaging, sidelobe suppression is often required for image quality enhancement. However, in the case of imaging a large target in a short range, sidelobes of the target will become spatially variant and curved, and traditional sidelobe suppression methods will fail. This article proposes a new spatially variant curved sidelobe suppression method for LA MIMO SAR short-range 3-D imaging. The key technique is the employment of a new pseudopolar coordinate system where both the spatial variance and the curvature of 3-D sidelobes can be removed. Specifically, a new 3-D spatially variant apodization (SVA) kernel is applied for sidelobe suppression to maintain the resolution performance. The validity of the presented approach has been demonstrated via computer simulations, the tower crane experiment, and the unmanned ground vehicle experiment.

Improved DBF-MIMO-SAR Waveform Transmission Scheme for Reducing the Cost of DOF in the Elevation

The echo separation solution based on digital beamforming (DBF) in the elevation is quite applicable to multiple-input multiple-output (MIMO) synthetic aperture radar (SAR). For the current DBF-MIMO-SAR schemes, however, at least <inline-formula><tex-math notation="LaTeX">$\bm {2M-1}$</tex-math></inline-formula> subapertures in the elevation must be employed to successfully separate the aliased signal returns induced by the <inline-formula><tex-math notation="LaTeX">$\bm M$</tex-math></inline-formula> simultaneously transmitted waveforms, which essentially increases the system complexity. To this end, the paper proposes an improved waveform transmission scheme to exploit the potential benefits that MIMO concept can provide for a practical remote sensing system with limited available channels. In this scheme, the basic signal segments are coded by the improved phase coding matrix which is associated with the slow time. Afterwards, time-shift weighting, using the decoding matrix, is implemented to remove the interference from close arrival angles. In this process, the interference segments in near or far range can also be cancelled out. Consequently, the number of interferences has been halved and thereby, compared with the already-proposed DBF-MIMO-SAR schemes, e.g., short-term shift-orthogonal (STSO), the designed scheme only needs <inline-formula><tex-math notation="LaTeX">$\bm {M}$</tex-math></inline-formula> elevation channels for the realization of DBF-MIMO-SAR. Finally. numerous simulations have been carried out to demonstrate that the proposed scheme shows great potential in terms of echo separation for channel-limited MIMO SAR systems.

A Robust Digital Beamforming on Receive in Elevation for Airborne MIMO SAR System

The echo separation issue for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is usually regarded as a more technical challenge. Spotlighted as a promising solution to the echo separation, the well-known short-term shift-orthogonal (STSO) beamforming scheme has become increasingly popular. However, for airborne MIMO SAR systems, the digital beamforming (DBF) involved in the STSO scheme usually encounters more issues, e.g., the direction of arrival (DOA) mismatch induced by topography variation. Up to now, relatively less research on robust DBF processing has been conducted for airborne MIMO SAR systems. In this respect, an adaptive DBF technique, based on interference plus noise covariance matrix (IPNCM) reconstruction and desired signal steering vector estimation, has been proposed in this paper. IPNCM reconstruction and steering vector estimation can not only cope with the DOA mismatch problem, but also remove the desired signal component in the training data cells to increase the beamformer convergence rates. Consequently, the proposed approach really improves the array output signal-to-interference-plus-noise ratio (SINR). Moreover, numerous discussions and simulations are carried out to prove the effectiveness of proposed DBF technique under various disturbance environments. Compared with the current DBF techniques, the proposed method provides a bright application prospect for the STSO scheme.

A Reconfigurable MIMO-SAR Transmission Scheme Based on Inter-Pulse and Intra-Pulse Joint Phase Modulation

The echo separation issue associated with different transmit antennas is the most technical challenge in realizing the Multiple-Input Multiple-Output (MIMO) Synthetic Aperture Radar (SAR) system. In this paper, a reconfigurable MIMO-SAR transmission scheme based on inter-pulse and intra-pulse joint phase modulation is proposed. The proposed scheme has many advantages. First, lots of monostatic waveforms can be directly employed in a MIMO-SAR system, which offers flexibility in waveform selection. Second, according to different MIMO-SAR applications, it is reconfigurable and can flexibly adjust the system Degree of Freedoms (DoFs) to accomplish echo separation, achieving the best system performance, which is the key for the multimodal operation of MIMO-SARs. Finally, due to the limited system resources in the real MIMO-SAR system, it is hard to separate echo in a single domain, and the proposed scheme jointly capitalizes DoFs of Doppler, time, and space domains to ensure the successful echo separation. In addition, detailed simulation experiments are carried out to verify the practicability and effectiveness of the newly proposed transceiver schemes.

Linear-Array-MIMO SAR Tomography: An Autofocus Approach for Time-Variant and 3-D Space-Variant Motion Errors

Linear-array multiple-input–multiple-output (LA-MIMO) synthetic aperture radar (SAR) can obtain 3-D radar images by only one pass. However, it is sensitive to time-variant measurement errors of curved track and time-variant attitude angles, meaning that autofocus processing for the LA-MIMO SAR tomography is necessary. The existing autofocus methods cannot be used to estimate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-variant</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3-D space-variant</i> motion errors (3-D SVME) of the LA-MIMO SAR. To solve this problem, a new autofocus approach based on multiple local autofocusing and the LA-MIMO SAR time-variant motion error estimation is proposed. First, the local motion error estimation based on the fast local spectral analysis (SPECAN) 3-D imaging and the maximum contrast optimization 2-D local autofocusing is performed to estimate the local time-variant motion errors. Then, based on the linear-array motion error model, the time-variant 3-D trajectory deviations of the array center and attitude angles are estimated by the weighted least square estimation (WLSE) to solve the 3-D SVMEs. Last, the 3-D fast factorized backprojection (FFBP) is performed to obtain the well-focused 3-D image of the whole beam. The proposed approach has been applied for the tomography of a new crawler-type unmanned-ground-vehicle (UGV) LA-MIMO SAR. Both the simulation and real data experiments verify the effectiveness of the proposed approach.

MIMO-SAR Interferometric Measurements for Structural Monitoring: Accuracy and Limitations

Terrestrial Radar Interferometry (TRI) is a measurement technique capable of measuring displacements with high temporal resolution at high accuracy. Current implementations of TRI use large and/or movable antennas for generating two-dimensional displacement maps. Multiple Input Multiple Output Synthetic Aperture Radar (MIMO-SAR) systems are an emerging alternative. As they have no moving parts, they are more easily deployable and cost-effective. These features suggest the potential usage of MIMO-SAR interferometry for structural health monitoring (SHM) supplementing classical geodetic and mechanical measurement systems. The effects impacting the performance of MIMO-SAR systems are, however, not yet sufficiently well understood for practical applications. In this paper, we present an experimental investigation of a MIMO-SAR system originally devised for automotive sensing, and assess its capabilities for deformation monitoring. The acquisitions generated for these investigations feature a 180∘ Field-of-View (FOV), distances of up to 60 m and a temporal sampling rate of up to 400 Hz. Experiments include static and dynamic setups carried out in a lab-environment and under more challenging meteorological conditions featuring sunshine, fog, and cloud-cover. The experiments highlight the capabilities and limitations of the radar, while allowing quantification of the measurement uncertainties, whose sources and impacts we discuss. We demonstrate that, under sufficiently stable meteorological conditions with humidity variations smaller than 1%, displacements as low as 25 μm can be detected reliably. Detecting displacements occurring over longer time frames is limited by the uncertainty induced by changes in the refractive index.

MIMO-SAR: A Hierarchical High-Resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform.

IRCI‐free multiple subband MIMO SAR

An inter-range cell interference (IRCI)-free multiple subband multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) algorithm is proposed to obtain a high-resolution wide swath (HRWS) imaging. The received signals of the proposed configuration are formulated as multiple-input single-output (MISO) system identification problems using the principle of the displaced phase centre in a way that all the subband waveforms are processed simultaneously without the need to separate them at the receiver. This would maximise the bandwidth utilisation efficiency. The channel impulse response in the range dimension is identified using a frequency domain system identification--based estimation algorithm, which is free from IRCI, instead of using conventional matched filters. A pulse repetition frequency lower than the Doppler bandwidth is used to obtain the HRWS imaging, and the resulted azimuth ambiguity is removed using a set of spatial filters applied on the received signals formulated as separate MISO system identification problems. Finally, both simulated and constructed raw data are used to validate the efficiency of the proposed algorithm.

A FFT-Based Millimeter-Wave Imaging Algorithm with Range Compensation for Near-Field MIMO-SAR

In this article, an improved fast Fourier transform (FFT)–based millimeter-wave imaging algorithm with range compensation is presented, which can be used to reconstruct 3D images for near-field multiple-input multiple-output synthetic aperture radar (MIMO-SAR). The frequency-domain interpolation is avoided and only one-step spherical-wave decomposition is employed in this algorithm. During the image reconstruction process, the amplitude factor is considered for the compensation of signal propagation loss, and the final target image can be obtained by FFT/IFFT and coherent accumulation steps. As demonstrated with numerical theoretical analysis and experimental results, the proposed method greatly reduces the computational load but ensures the quality of image reconstruction compared to the back-projection (BP) algorithm. Moreover, it is superior to the MIMO-range migration algorithm (MIMO-RMA) in compensating propagation loss and other performance indexes in the image reconstruction results.