Radar Superresolution Research Articles

Real Aperture Radar Super-Resolution Imaging for Sea Surface Monitoring Based on a Hybrid Model

In recent years, super-resolution imaging techniques have been intensely introduced to enhance the azimuth resolution of real aperture scanning radar (RASR). However, there is a paucity of research on the subject of sea surface imaging with small incident angles for complex scenarios. This research endeavors to explore super-resolution imaging for sea surface monitoring, with a specific emphasis on grounded or shipborne platforms. To tackle the inescapable interference of sea clutter, it was segregated from the imaging objects and was modeled alongside I/Q channel noise within the maximum likelihood framework, thus mitigating clutter’s impact. Simultaneously, for characterizing the non-stationary regions of the monitoring scene, we harnessed the Markov random field (MRF) model for its two-dimensional (2D) spatial representational capacity, augmented by a quadratic term to bolster outlier resilience. Subsequently, the maximum a posteriori (MAP) criterion was employed to unite the ML function with the statistical model regarding imaging scene. This hybrid model forms the core of our super-resolution methodology. Finally, a fast iterative threshold shrinkage method was applied to solve this objective function, yielding stable estimates of the monitored scene. Through the validation of simulation and real data experiments, the superiority of the proposed approach in recovering the monitoring scenes and clutter suppression has been verified.

Super resolution direction finding technique of vortex electromagnetic wave radar in missing mode

Vortex electromagnetic waves have superior performance over electromagnetic waves. In order to improve the radar super-resolution lateralization technique in its missing modes, this study proposes to start from the perspective of mode missing. The missing modes are reconstructed using the Adaptive Step Size Gradient Descent (ASSGD) method by exploiting the features of the missing modes. The linear minimum mean square error (LMMSE) estimation method is also used to solve the problem of poor reconstruction accuracy due to the Missing Modes. The Missing Modes Iterative Adaptive Approach (MMIAA) algorithm and Missing Modes Sparse Learning via Iterative Minimization (MMSLIM) algorithm are then used. Minimization (MMSLIM) algorithm to recover missing modes. The results showed that the RMSEs of the recovery errors of MMSLIM, MMIAA and ASSGD were 0.16, 0.31 and 0.82 respectively at a modal missing ratio of 0.7, while ASSGD fails to recover the missing modal data at a modal missing ratio of 0.9. The overall data quality of the azimuthally estimated RMSE was average when the signal-to-noise ratio was at [–5, 10] dB. And the curve becomes flatter when it reaches 15 dB or more, indicating that MMSLIM, MMIAA has important theoretical and practical value.

Principle and approach to polarization modulation for radar super-resolution

分辨是雷达领域的经典问题, 尽管经过几十年的发展, 但“分不开、辨不清”的矛盾在雷达各类应用中日益凸显. 在雷达分辨研究中, 模糊函数理论占据绝对主流, 同时尝试突破瑞利分辨极限的超分辨研究层出不穷, 这些方法具有一定的超分辨效果, 但因其前提假设而在工程应用中受限. 传统分辨理论并未考虑电磁场的极化特性, 利用极化技术可大幅提升雷达分辨能力, 研究极化与分辨的关系具有重大理论价值. 本文从模糊函数出发, 重点分析目标幅度和相位对分辨效果的影响, 得出“等幅反相”可使雷达突破分辨率极限, 结合雷达收发极化调控技术, 创新性提出极化域变焦理论, 通过全极化域距离像阐释超分辨原理, 并且针对实际雷达系统, 提出极化域变焦多目标检测与分辨方法, 实现多目标的正确判决和距离的有效估计. 仿真结果表明, 极化域变焦处理对交叉极化不敏感, 在高信噪比条件下可将雷达分辨力提升约20倍.

Principles and Methods of Radar Super-Resolution Based on Instantaneous Polarization Response

Principles and Methods of Radar Super-Resolution Based on Instantaneous Polarization Response

Research on Ultra-Wideband NLFM Waveform Synthesis and Grating Lobe Suppression.

Ultra-wideband (UWB) nonlinear frequency modulation (NLFM) waveforms have the advantages of low sidelobes and high resolution. By extending the frequency domain wideband synthesis method to the NLFM waveform, the synthetic bandwidth will be limited, and the grating lobe will grow as the number of subpulses increases at a fixed synthetic bandwidth. Aiming for the highly periodic grating lobes caused by equally spaced splicing and small subpulse time-bandwidth products (TxBW), a multisubpulse UWB NLFM waveform synthesis method is proposed in this paper. Random frequency hopping and spectral correction are utilized to disperse the energy of periodic grating lobes and optimize the matched filter of the subpulse, thereby reducing notches and Fresnel ripples in the synthesized spectrum. The results of the hardware-in-the-loop simulation experiment show that the peak sidelobe ratio (PSLR) and the integral sidelobe ratio (ISLR) of the NLFM synthetic wideband waveform (SWW) obtained by 50 subpulses with a bandwidth of 36 MHz are improved by 4.8 dBs and 4.5 dBs, respectively, when compared to the frequency domain wideband synthesis method, and that the grating lobe is suppressed by an average of 10.6 dBs. It also performs well in terms of point target resolution, and it has potential for 2D radar super-resolution imaging.

Simulations of Spotlight Synthetic Aperture Radar Super-resolution Algorithm

Simulations of Spotlight Synthetic Aperture Radar Super-resolution Algorithm

Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing

Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super-resolution imaging, multi-target resolution, and cognitive reconfiguration. This paper proposes a fast, super-resolution imaging method employing continuous compressive sensing for sparse-aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low-rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super-resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super-resolution image and shows better performance than state-of-the-art methods.

Superresolution Radar Imaging via Peak Search and Compressed Sensing

Compressed sensing (CS) based imaging technique is considered to be an effective solution for high-resolution radar imaging due to the sparse distribution of the scatterers. Nevertheless, the unoptimized CS based synthetic aperture radar (SAR) or inverse SAR (ISAR) imaging approach may suffer from the computationally intensive problem when apply it to wideband radar signatures of electrical large-scale targets. In this paper, a two-dimensional (2-D) superresolution imaging technique based on peak search and compressed sensing (PS-CS) is presented. A peak search strategy is first developed to solve the problem of high computational complexity of CS based method in scattering parameter estimation. Superresolution imaging result is then achieved by extrapolating the estimated parameters of scattering along with the observing angle dimension and frequency dimension. The numerical and measurement data acquired from different man-made targets are presented to demonstrate the feasibility and usefulness of the proposed technique for supperresolution radar imaging.

Deep Mutual GAN for Life-Detection Radar Super Resolution

To improve the life-detection radar resolution under certain hardware conditions, in this letter, a deep mutual learning generative adversarial network model (Deep Mutual GAN) is proposed. In the proposed model, the generator can improve the angular resolution of the input low-resolution radar image by five times, which is enough to meet our requirements for the resolution of life detection. We innovatively use two generators in GAN with the same network structure and make the two generators learn from each other. In this way, the learning process of a generator is not only achieved by its confrontation with the discriminator but also guided by another generator. As a result, the knowledge of the generator is no longer only obtained through its own learning; each generator learns knowledge from another generator while learning knowledge by itself. The proposed model can effectively make the convergence of GAN more stable and improves the super resolution effect. We also introduce the details of the network structure of generator and discriminator, in which residual learning and a symmetrical network structure are applied. The experimental results show that the proposed method can achieve state-of-the-art imaging effect, which is meaningful for subsequent target detection and recognition.

MIMO Radar Super-Resolution Imaging Based on Reconstruction of the Measurement Matrix of Compressed Sensing

This letter proposes a novel sparse recovery method of multiple-input and multiple-output (MIMO) radar compressed sensing (CS) imaging algorithms. This method leverages the prior structure of the measurement matrix to judge targets’ locations and to estimate the sparsity level in the grid roughly and finally inhabits the emergence of false targets in the imaging figure. Explicitly, the algorithm we propose is inspired by the orthogonal matching pursuit (OMP) algorithm. First, the measurement matrix can be divided into some submatrices by column. Then, we estimate which submatrices do not contain signal components by the algorithms we propose in this literature to achieve the reconstruction of the measurement matrix. Finally, we use the sparse Bayesian learning algorithm and the sparsity adaptive matching pursuit algorithm to recover the target location and scattering intensity. Experiments validate that the reconstruction error of the algorithm we propose is much lower than other sparse recovery algorithms, and targets in the imaging are more obvious than other algorithms.

Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method

Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform.

Separating Radar Signals From Impulsive Noise Using Atomic Norm Minimization

We consider the problem of corrupted radar super-resolution, a generalization of compressed radar super-resolution in which one aims to recover the continuous-valued delay-Doppler pairs of moving objects from a collection of corrupted and noisy measurements. The received signal in this type consists of contributions from objects, outlier and noise. While this problem is ill-posed in general, tractable recovery is possible when both the number of objects and corrupted measurements are limited. In this brief, we propose an atomic norm optimization in order to find the delay-Doppler pairs and the outlier signal. The objective function of our optimization problem encourages both sparsity in the continuous delay-Doppler domain and the sparsity of perturbations in the outlier signal. We show that the associated problem can be solved via semidefinite programming (SDP). Moreover, we provide a reasonably fast and efficient algorithm for solving this SDP based on the alternating method of multipliers (ADMM). Simulations results verify the superior performance of our proposed problem and the high accuracy and low computational complexity of our algorithm.

Fast Total Variation Method Based on Iterative Reweighted Norm for Airborne Scanning Radar Super-Resolution Imaging

The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp norm problem by solving a sequence of minimum weighted L2 norm problems, and has been applied to solving the TV norm. However, during the solving process, the IRN method is required to update the weight term and result term in each iteration, involving multiplications and the inversion of large matrices. Consequently, it suffers from a huge calculation load, which seriously restricts the application of the TV imaging method. In this work, by analyzing the structural characteristics of the matrix involved in iteration, an efficient method based on suitable matrix blocking is proposed. It transforms multiplications and the inversion of large matrices into the computation of multiple small matrices, thereby accelerating the algorithm. The proposed method, called IRN-FTV method, is more time economical than the IRN-TV method, especially for high dimensional observation scenarios. Numerical results illustrate that the proposed IRN-FTV method enjoys preferable computational efficiency without performance degradation.

Forward-Looking Scanning Radar Superresolution Imaging Based on Second-Order Accelerated Iterative Shrinkage-Thresholding Algorithm

Scanning radar can be used to obtain images of targets in forward-looking area, and has attracted much attention in many fields, such as ocean monitoring, air-to-ground attack, navigation, and so on. However, its azimuth resolution is extremely poor due to the limitation of the antenna size. In order to break through the limitation, many superresolution algorithms have been proposed, and iterative shrinkage-thresholding algorithm (ISTA) is one of the most famous methods because of its antinoise ability and simplicity. In the meantime, the slow convergence of iterative shrinkage-thresholding algorithm is also known to all. In this article, a second-order accelerated ISTA for scanning radar forward-looking superresolution imaging is proposed. In this algorithm, a prediction vector is constructed before each iteration by using the first and the second-order difference information of iteration vectors to reduce the number of iterations and get a faster convergence speed. In the end, simulations and experimental results are given to illustrate the effectiveness of the accelerated imaging algorithm.

Vector extrapolation accelerated iterative shrinkage/thresholding regularization method for forward-looking scanning radar super-resolution imaging

The iterative shrinkage/thresholding regularization method is an efficient and robust approach to implement super-resolution imaging for forward-looking scanning radar (FLSR). Nevertheless, numerous iterations are normally required to obtain a high super-resolution ratio and thus it can be time consuming. A vector extrapolation accelerated iterative shrinkage/thresholding regularization method is proposed for FLSR. In order to achieve effective acceleration, a predicted point is particularly formulated by linearly extrapolating the historical iterates before conducting the iterative shrinkage/thresholding operation. To reduce the prediction error and stabilize the acceleration process, an adaptively adjusted acceleration step size is derived according to the similarity of the adjacent iterative vectors. Last, a fixed-threshold constraining operator is conducted on the step size to guarantee the convergence of the acceleration method. The results of simulation experiment and real data processing verify the advantage of the proposed method in both the convergence speed and super-resolution performance when compared with the traditional acceleration methods.

Superresolution radar imaging based on fast inverse-free sparse Bayesian learning for multiple measurement vectors

Compressive sensing has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. By exploiting the block sparse structure of the target image, sparse solution for multiple measurement vectors (MMV) can be applied in ISAR imaging and a substantial performance improvement can be achieved. As an effective sparse recovery method, sparse Bayesian learning (SBL) for MMV involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size. To address this problem, we develop a fast inverse-free (IF) SBL method for MMV. A relaxed evidence lower bound (ELBO), which is computationally more amiable than the traditional ELBO used by SBL, is obtained by invoking fundamental property for smooth functions. A variational expectation–maximization scheme is then employed to maximize the relaxed ELBO, and a computationally efficient IF-MSBL algorithm is proposed. Numerical results based on simulated and real data show that the proposed method can reconstruct row sparse signal accurately and obtain clear superresolution ISAR images. Moreover, the running time and computational complexity are reduced to a great extent compared with traditional SBL methods.

Finite-Rate-of-Innovation-Sampling-Based Super-Resolution Radar Imaging

We address the problem of estimating target locations that are sparse in a pulse-Doppler radar's unambiguous region by sampling the received signal at sub-Nyquist rates. The received signal is modeled as a finite-rate-of-innovation (FRI) signal, and the problem of estimating the delays in a single transceiver radar is formulated as one of recovery of sparse common-support (SCS) FRI signals, which arises in the context of channel estimation in multiple input, multiple output communication systems. The delays are estimated by the SCS-FRI reconstruction method. We present a new method termed delay focusing to estimate the Doppler shifts. To obtain overall performance gains, we also present an extended method called dual focusing , which combines both delay and Doppler focusing schemes, and has the capability to superresolve targets in the delay-Doppler plane. The performance of the recovery methods in the presence of noise is also analyzed. We demonstrate that the proposed estimation methods are robust to noise. Monte Carlo performance analysis in the presence of noise shows that the dual focusing method accurately resolves closely spaced targets and yields a significant decrease in normalized mean-square error of up to 10–20 dB for the estimated Doppler shifts. We also simulate the scenario where multiple targets are in a formation , that is, when they are closely spaced along both delay and Doppler axes, and show that the dual focusing method achieves a hit rate of nearly 100% at a much lower signal-to-noise ratios than that required for Doppler focusing.

Analysis on the compressive sensing based narrow-band radar super resolution imaging mechanism of rapidly spinning targets

According to the characteristics of spinning targets, the narrow-band radar echoes can be directly used for imaging spinning targets. However, spurious peaks appear due to azimuth down sampling with a low pulse repetition frequency (PRF). By exploiting the sparsity of the targets, the compressed sensing (CS) theory can be adopted to obtain super resolution image under sub-sampling condition. This paper mainly focuses on analyzing the physical mechanism of the CS-based narrow-band imaging method. Firstly, the narrow-band radar's under-sampling echoes' model from rapidly spinning targets is established. The relationship between CS and the model is analyzed. Then the reasons why the CS-based narrow-band imaging method can guarantee the exact recovery of the spinning target are given from physical view. The theoretical lower limit of sub-sampling pulse numbers is provided. Finally, the simulation results verify the effectiveness of the theoretical analysis. The main results obtained in the paper are listed as follows. One is that the mechanism of the CS-based narrow-band imaging method differs from those of the conventional range Doppler imaging methods. The spurious peaks appear due to calculating the Doppler frequency directly under a low PRF. To avoid this phenomenon, the CS-based method searches the positions of the scatterers instead. The variation from calculating the Doppler frequency directly to searching the positions of the scatterers is the physical mechanism of the CS-based super resolution imaging method. The other is that the resolution and the allowable grid mismatch of the CS-based imaging method are related to the wavelength, which is 0.4 and unrelated to the bandwidth. So the performance of the CS-based imaging method is related to the sub-sampling rate, the number of the scatters and the wavelength, and unrelated to the bandwidth of the wave. However, this paper only considers the ideal point scattering model and the grid is perfectly matched with the model. In the following, three aspects can be further studied. First, due to the spinning target distribution on a continuous scene, the off-grid problem would severely affect the performance of the CS-based imaging method. The continuous compressive sensing theory can be used for solving the off-grid problem and explaining the related physical mechanism. Second, the illumination of the radar cannot reach some scatterers on the target in some observation intervals, which results in the occlusion effect and the time-varying scattering amplitude. The dynamic CS theory can be used for reference in solving this problem. Finally, if the estimated spinning frequency has error, how to correct and compensate for the error adaptively needs to be further studied.

Multiple-input-multiple-output radar superresolution three-dimensional imaging based on multidimensional smoothed L0

By exploiting the sparsity of radar target image, it is hopeful to obtain a high-resolution target image in multiple-input-multiple-output (MIMO) radar via a sparse representation (SR) method. However, for the three-dimensional (3-D) imaging, the conventional SR method has to convert the 3-D problem into the one-dimensional (1-D) problem. Thus, it will inevitably impose a heavy burden on the storage and computation. A multidimensional smoothed L0 (MD-SL0) algorithm is proposed based on the conventional smoothed L0 algorithm. The proposed MD-SL0 can directly apply to the multidimensional SR problem without transforming to the 1-D case. As a result, a MIMO radar 3-D imaging method via MD-SL0 is achieved with high computation efficiency and low storage burden. Finally, the effectiveness of the method is validated by the results of comparative experiments.

Super-resolution radar

In this paper, we study the identification of a time-varying linear system from its response to a known input signal. More specifically, we consider systems whose response to the input signal is given by a weighted superposition of delayed and Doppler-shifted versions of the input. This problem arises in a multitude of applications such as wireless communications and radar imaging. Due to practical constraints, the input signal has finite bandwidth $B$ , and the received signal is observed over a finite time interval of length $T$ only. This gives rise to a delay and Doppler resolution of $1/B$ and $1/T$ . We show that this resolution limit can be overcome, i.e., we can exactly recover the continuous delay–Doppler pairs and the corresponding attenuation factors, by solving a convex optimization problem. This result holds provided that the distance between the delay–Doppler pairs is at least $2.37/B$ in time or $2.37/T$ in frequency. Furthermore, this result allows the total number of delay–Doppler pairs to be linear up to a log-factor in $BT$ , the dimensionality of the response of the system, and thereby the limit for identifiability. Stated differently, we show that we can estimate the time–frequency components of a signal that is $S$ -sparse in the continuous dictionary of time–frequency shifts of a random window function, from a number of measurements that is linear up to a log-factor in $S$ .