MIMO Radar Imaging Research Articles

Fully Polarimetric Calibration of a Near-Field MIMO Imaging Radar Using the QCCLD Reflector

Fully Polarimetric Calibration of a Near-Field MIMO Imaging Radar Using the QCCLD Reflector

The Impact of Antenna Array Calibration Errors on MIMO and Multi-Channel Synthetic Aperture Radar Imaging

In radar imaging, any lateral resolution is either attained using antenna arrays or by synthetic aperture radar (SAR) principles. Recently, the combination of both approaches to a multi-channel SAR (MCSAR) has gained increased attention. However, antenna arrays are impaired by unknown calibration errors, particularly phase errors, amplification deviations, mutual coupling, and phase center position deviation of the antenna element. Since, first, established calibration procedures are often non-ideal and, second, the calibration parameters usually vary over time due to aging and changes in temperature, calibration errors distort the measurements during the radar operation. Therefore, current research focuses on finding new practical calibration procedures. However, hardly any research has been conducted to analyze the basic effects which erroneous calibration parameters have on the reconstructed image in MCSAR or MIMO radar. In this work, we analyze the effects of multiple calibration error types on MCSAR and MIMO radar imaging. Both measurement principles lead to periodic error patterns, resulting in deterministic distortions. The derived relations can be used to precisely predict the position and number of ghost targets caused by calibration errors for known measurement setups and targets. For this, no information about the actual calibration errors is required. Furthermore, worst-case distortion levels are provided for different bounded errors allowing rough estimates of the impact of different error types on the image's distortion level. The findings are validated via measurements using a 76 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\,$</tex-math></inline-formula> GHz FMCW MIMO radar.

TVmmC: Robust Calibration Method for Signal Distortion in Near-Field MIMO Imaging Radar

TVmmC: Robust Calibration Method for Signal Distortion in Near-Field MIMO Imaging Radar

MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning

Transmitting orthogonal waveforms are the basis for giving full play to the advantages of MIMO radar imaging technology, but the commonly used waveforms with the same frequency cannot meet the orthogonality requirement, resulting in serious coupling noise in traditional imaging methods and affecting the imaging effect. In order to effectively suppress the mutual coupling interference caused by non-orthogonal waveforms, a new non-orthogonal waveform MIMO radar imaging method based on deep learning is proposed in this paper: with the powerful nonlinear fitting ability of deep learning, the mapping relationship between the non-orthogonal waveform MIMO radar echo and ideal target image is automatically learned by constructing a deep imaging network and training on a large number of simulated training data. The learned imaging network can effectively suppress the coupling interference between non-ideal orthogonal waveforms and improve the imaging quality of MIMO radar. Finally, the effectiveness of the proposed method is verified by experiments with point scattering model data and electromagnetic scattering calculation data.

MIMO Radar Imaging Based on Two-dimensional Compressive Sensing Recovery Algorithm

Conventional sparse signal recovery-based MIMO radar imaging method rearranges the received two-dimensional (2D) signals into a vector, However, transforming the two dimensional equation to one dimensional equation then solving linear equation based sparse signal recovery problem requires huge memory and computational budget. A two-dimensional compressed sensing MIMO radar imaging algorithm based on improved smooth L0 norm is proposed in this paper. Firstly, a two-dimensional MIMO radar imaging model is established to transform the imaging problem into a sparse optimization problem. Then, the two-dimensional negative exponential function sequence is used as the smoothed function sequence to approximate the L0 norm. The two-dimensional imaging of MIMO radar is realized by cyclic iteration and gradient projection algorithm. Simulation results show that the proposed algorithm has advantages over the traditional compressed sensing algorithms.

Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

Calibration to Mitigate Near-Field Antennas Effects for a MIMO Radar Imaging System.

A calibration method for a high-resolution hybrid MIMO turntable radar imaging system is presented. A line of small metal spheres is employed as a test pattern in the calibration process to measure the position shift caused by undesired antenna effects. The unwanted effects in the antenna near-field responses are analysed, modelled and significantly mitigated based on the symmetry and differences in the responses of the MIMO configuration.

Bayesian Matching Pursuit-Based Distributed FMCW MIMO Radar Imaging

In this article, to get a high-resolution radar image with distributed frequency modulated continuous waveform multiple-input-multiple-output (FMCW MIMO) radar, Bayesian matching pursuit (BMP)-based imaging methods are proposed, in which the received signals at the distributed FMCW MIMO radars are reformulated in terms of the (azimuth, range) patches in the image region of interest and the maximum a posterior (MAP) estimator that can estimate the azimuth angles and ranges of multiple targets is then derived. For a single FMCW MIMO radar, we first propose the BMP-based imaging, in which the support vector (indicating the presence of targets in the radar image patches) and the associated coefficients (the reflection coefficients of the associated targets) are iteratively updated. Because MAP estimation needs the combinatorial search over all possible candidates to find the nonzero elements in the support vector, an approximated MAP estimation method is newly proposed. In addition, by extending the BMP-based imaging for a single FMCW MIMO radar, we develop a new distributed BMP-based imaging method when multiple FMCW MIMO radars are spatially distributed. Through the simulation, we confirm that the proposed BMP-based radar imaging methods outperform the conventional backprojection method or the orthogonal matching pursuit-based method with slightly increased computational complexity.

Three-Dimensional High-Resolution MIMO Radar Imaging via OFDM Modulation and Unitary ESPRIT

Imaging and recognition of targets with complex maneuvers bring a new challenge to conventional radar applications. In this paper, the three-dimensional (3D) high-resolution image is attained in real-time by a Multiple-Input-Multiple-Output (MIMO) radar system with single Orthogonal-Frequency-Division-Multiplexing (OFDM) pulse. First, to build the orthogonal transmit waveform set for MIMO transmission, we utilize complex orthogonal designs (CODs) for OFDM subcarrier modulation. Based on the OFDM modulation, a preprocessing method is developed for transmit waveform separation without conventional matched filtering. The result array manifold is the Kronecker product of the steering vectors of subcarrier/transmit antenna/receive antenna uniform linear arrays (ULAs). Then, the high-resolution image of target is attained by the Multidimensional Unitary Estimation of Signal Parameters via Rotational Invariant Techniques (MD-UESPRIT) algorithm. The proposed imaging procedures include the multidimensional spatial smoothing, the unitary transform via backward-forward averaging, and the joint eigenvalue decomposition (JEVD) algorithm for automatically paired coordinates estimation. Simulation tests compare the reconstruction results with the conventional methods and analyze the estimation precision relative to signal-to-noise ratio (SNR), system parameters, and errors.

Sparsity-Based Robust Bistatic MIMO Radar Imaging in the Presence of Array Errors

A sparse recovery method for robust transmit-receive angle imaging in a bistatic MIMO radar is proposed to deal with the effect of array gain-phase errors. The impact of multiplicative array gain-phase errors is changed to be additive through model reformulation, and transmit-receive angle imaging is formulated to a sparse total least square signal problem. Then, an iterative algorithm is proposed to solve the optimization problem. Compared with existing methods, the proposed method can achieve a significant performance gain in the case that the number of snapshots is small. Simulation results verify the effectiveness of the proposed method.

3-D MIMO Radar Imaging of Ship Target with Rotational Motions

3-D MIMO Radar Imaging of Ship Target with Rotational Motions

An Imaging Plane Calibration Method for MIMO Radar Imaging.

In two dimensional cross-range multiple-input multiple-output radar imaging for aerial targets, due to the non-cooperative movement of the targets, the estimated imaging plane parameters, namely the center and the posture angles of the imaging plane, may have deviations from true values, which defocus the final image. This problem is called imaging plane mismatch in this paper. Focusing on this problem, firstly the deviations of spatial spectrum fulfilling region caused by imaging plane mismatch is analyzed, as well as the errors of the corresponding spatial spectral values. Thereupon, the calibration operation is deduced when the imaging plane parameters are accurately obtained. Afterwards, an imaging plane calibration algorithm is proposed to utilize particle swarm optimization to search out the imaging plane parameters. Finally, it is demonstrated through simulations that the proposed algorithm can accurately estimate the imaging plane parameters and achieve good image focusing performance.

Calibration of MIMO Fully Polarimetric Imaging Radar Systems with Passive Targets

Abstract The performance of polarimetric imaging radar systems depends greatly on how well they are calibrated. The complexity of the calibration procedure scales with the number of send and receive antennas, i. e. the size of the antenna array. This article proposes a practical and effective approach for calibrating a polarimetric near field MIMO imaging radar using only two simple passive targets. The calibration requires four measurement steps: An empty space measurement for suppressing direct mutual coupling between the channels. An offset short calibration with the specular reflection of a planar metal plate for the co-polarized channels. Two measurements with a dihedral reflector inclined at different angles for the cross-polarized calibration.

Measurement matrix design for colocated MIMO radar imaging based on Compressive Sensing

Compressive Sensing (CS) is recently used in the colocated MIMO radar to reconstruct the high resolution range profiles (HRRPs) of the target. By exploiting the joint-block sparsity of the HRRPs, the reconstruction result is improved significantly. In this paper, a compressive measurement matrix is designed to reduce the amount of data. An objective for measurement matrix optimization is presented by considering the block sparsity of the HRRPs, and the Weighted Coherence Minimization algorithm is used to optimize the measurement matrix. Simulation results show that the optimized measurement matrix is superior to the Gaussians randomly measurement matrix which doesn’t utilize the block sparsity of the HRRPs.

Vibration Measurement Method for Artificial Structure Based on MIMO Imaging Radar

Measuring the vibrating states of an artificial structure is an important approach to monitor the stability of the structure. However, the existing radar vibration measurement methods do not have enough azimuth resolution for measuring the vibrations in a relatively large scene, which contains multiple buildings or a large building. For such application scene, this paper proposes a vibration measurement method based on multiple-input multiple-output imaging radar system, which can achieve high azimuth resolution. Features of the proposed method are threefold: first of all, by utilizing the ability of quickly acquiring imaging data, the proposed method can achieve vibration measurement for the entire area simultaneously; second, in order to detect the positions of vibrating objects and decrease the time of vibration parameter estimation part, this paper proposes vibration similarity index to quantify the similarity between the detected signal and ideal vibration signal; at last, due to the limitations of the hardware performance, this paper adopts multiple signal classification least-squares estimation method to estimate the vibrating frequency and amplitude. To evaluate the performance of the proposed method, ideal point target simulation and vibrating calibrator experiment have been conducted, and the results show that the positions and the vibrating parameters of the vibrating objects fit well with the reference values. In addition, car experiment and bridge experiment have been carried out to verify the ability of the proposed method to measure the vibration of real artificial structures, which cannot be seen as point targets.

A Compressive Sensing-Based Bistatic MIMO Radar Imaging Method in the Presence of Array Errors

A robust transmit-receive angle imaging method for bistatic MIMO radar based on compressed sensing is proposed. A new imaging model with array gain and phase error is established. The array gain error and phase error were modeled as a random interference for observation matrix by mathematical derivation. A constraint of observation matrix error is constructed in optimization problem of sparse recovery to reduce the effect of the interference of observation matrix. Then, the iterative algorithm of the optimization problems is derived. The proposed recovery method is more robust than the existed method in small samples, especially in the case of one snapshot. It is applicable in the case of relatively small array gain and phase errors. Simulation results confirm the effectiveness of the proposed method.

Tensor-based Match Pursuit Algorithm for MIMO Radar Imaging

Tensor-based Match Pursuit Algorithm for MIMO Radar Imaging

Multi-Target Radar Imaging Based on Phased-MIMO Technique—Part II: Adaptive Resource Allocation

The advantages of phased-MIMO technique have been utilized for multi-target imaging in the first part of this paper, and the better multi-target imaging performance has been shown compared with the traditional phased-array radar imaging and traditional MIMO radar imaging. For multi-target imaging, the limited radar resources should be allocated for different targets to achieve the maximal performance of radar. By establishing and solving a radar resource allocation optimization model, an adaptive resource allocation strategy for multi-target imaging based on phased-MIMO technique is proposed. With the method, the multi-target imaging efficiency can be improved significantly. The effectiveness of the proposed method is demonstrated by simulations.

Multi-Target Radar Imaging Based on Phased-MIMO Technique—Part I: Imaging Algorithm

Radar imaging provides the shape structure information for target recognition. In this paper, a multi-target radar imaging method based on the emerging phased-MIMO (multiple-input multiple-output) technique is proposed. Each single-element transmit antenna in traditional MIMO radar imaging system is replaced by a transmit array (TA), which operates in phased-MIMO mode. In the phased-MIMO mode, each TA is divided into several sub-arrays operating in phased-array radar mode, and the transmitted waveform by each sub-array is orthogonal to each other. The sub-arrays steer different transmit beams to different targets’ directions with optimized waveform for the corresponding target. The virtual aperture produced by the waveform diversity is used for coherent processing to improve the signal-to-noise ratio and the signal-to-interference ratio, and then the single-snapshot imaging for multi-target with high azimuth resolution can be achieved based on dictionary optimization and orthogonal matching pursuit. Simulation results indicate the effectiveness of the proposed method.

Reliable Orientation Estimation of Vehicles in High-Resolution Radar Images

With new generations of high-resolution imaging radars, the orientation of vehicles can be estimated without temporal filtering. This enables time-critical systems to respond even faster. Based on a large data set, this paper compares three generic algorithms for the orientation estimation of a vehicle. An experimental MIMO imaging radar is used to highlight the requirements of a robust algorithm. The well-known orientated bounding box and the so-called L-fit are adapted for radar measurements and compared with a brute-force approach. A quality function selects the best fitted model and is a key factor to minimize alignment errors. Moreover, the reliability of the estimation is evaluated with respect to the aspect angle, the distance to the target, and the number of sensors. An approach to estimate the reliability of the current orientation estimation is introduced. It is shown that the root mean square error of the orientation estimation is 9.77° and 38% smaller compared with the common algorithm. In 50% of the evaluated measurements the orientation estimation error is smaller than 3.73°.