Doppler Beam Sharpening Research Articles

Signal Processing and Waveform Re-Tracking for SAR Altimeters on High Mobility Platforms with Vertical Movement and Antenna Mis-Pointing.

Synthetic aperture radar (SAR) altimeters can achieve higher spatial resolution and signal-to-noise ratio (SNR) than conventional altimeters by Doppler beam sharpening or focused SAR imaging methods. To improve the estimation accuracy of waveform re-tracking, several average echo power models for SAR altimetry have been proposed in previous works. However, these models were mainly proposed for satellite altimeters and are not applicable to high-mobility platforms such as aircraft, unmanned aerial vehicles (UAVs), and missiles, which may have a large antenna mis-pointing angle and significant vertical movement. In this paper, we propose a novel semi-analytical waveform model and signal processing method for SAR altimeters with vertical movement and large antenna mis-pointing angles. A new semi-analytical expression that can be numerically computed for the flat pulse response (FSIR) is proposed. The 2D delay-Doppler map is then obtained by numerical computation of the convolution between the proposed analytical function, the probability density function, and the time/frequency point target response of the radar. A novel delay compensation method based on sinc interpolation for SAR altimeters with vertical movement is proposed to obtain the multilook echo, which can optimally handle non-integer delays and maintain signal frequency characteristics. In addition, a height estimation method based on least squares (LS) estimation is proposed. The LS estimator does not have an analytical solution, and requires iterative solving through gradient descent. We evaluate the performance of the proposed estimation strategy using simulated data for typical airborne scenarios. When the mis-pointing angles are within 10 degrees, the normalized quadratic error (NQE) of the proposed model is less than 10-10 and the RMSE of τ obtained by the re-tracking method fitted by the proposed model is less than 0.2 m, which indicates the high applicability of the model and accuracy of the re-tracking method.

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

АНАЛИЗ ПОДСТИЛАЮЩЕЙ ПОВЕРХНОСТИ ПРИ ФОРМИРОВАНИИ ИЗОБРАЖЕНИЯ В РЕЖИМЕ ДОПЛЕРОВСКОГО ОБУЖЕНИЯ ЛУЧА

Radar based on real beam scanning is widely used in both civil and military spheres. However,it is difficult to realize high azimuth resolution of a stationary platform or a platform with nonuniformmotion using conventional signal processing algorithms. Doppler beam sharpening (DBS)technology is a combination of high resolution and real-time performance compared to SyntheticAperture Radar (SAR) technology, which uses the Doppler shift between echoes from objects on theunderlying surface along the azimuth direction, caused by the movement of the radar platform. Unfortunately,the traditional DBS imaging algorithm, which constructs a Doppler filter using an FFT,has a low azimuth resolution and a high level of side lobes, which limits further improvement in azimuthresolution. In the article, the algorithm for constructing a map of the underlying surface in thedirection of movement of the radar carrier based on the DBS was studied and the map image wasanalyzed using the Fourier transform. A three-dimensional view of the map of the underlying surfaceis shown with the distribution of values in the images. The subject of the study is the method and algorithmfor constructing a map of the underlying surface in the Doppler beam sharpening mode andidentifying chain structures based on the analysis of the Fourier transform. The object of the study is a set of test images of the terrain map. The result of the study is the development of an algorithm forconstructing a map in order to identify chain structures on the underlying surface. The novelty of thework is an algorithm that allows you to build a map of the underlying surface based on the DBS,taking into account the blind zone in the direction of movement of the radar carrier. The results obtainedalso make it possible to reveal chain structures in the region of interest. The possibility ofestimating the periodicity of image elements using the Fourier transform has been tested. As a resultof solving the tasks set, the following conclusions can be drawn: – an algorithm has been developedfor constructing a map of the underlying surface based on DBS with image correction in the directionof movement of the radar carrier. – analysis of the results of the study showed that the proposed algorithmallows you to identify chain structures on the map.

High resolution automotive imaging using MIMO radar and Doppler beam sharpening

Highly detailed sensing of a vehicle's surrounding environment is a key requirement for the advancement of autonomous driving technology. While conventional automotive radar sensors remain robust under challenging weather conditions, poor cross-range resolution and high sidelobe levels present significant challenges. In this paper we propose an approach which combines MIMO beamforming with Doppler beam sharpening. We demonstrate a significant improvement in terms of cross-range resolution and, importantly, nearly 20 dB sidelobe suppression compared to conventional MIMO processing. This approach is investigated in detail and validated through theoretical analysis, simulation and experiment using data recorded on a moving vehicle. We demonstrate performance which is comparable to a high resolution mechanically scanned radar, using a commercially available MIMO sensor.

A Fast Sparse Azimuth Super-Resolution Imaging Method of Real Aperture Radar Based on Iterative Reweighted Least Squares With Linear Sketching

It is greatly significant to achieve radar forward-looking region imaging. Due to the limitation of phase ambiguity and small Doppler gradient in forward-looking region, synthetic aperture radar and Doppler beam sharpening cannot work for forward-looking imaging, while real aperture radar (RAR) has arbitrary imaging geometry. Nevertheless, restricted by the antenna aperture, azimuth resolution of RAR is coarse, super-resolution technology is required to improve its azimuth resolution. Exploiting the sparse prior information of the target, the super-resolution problem can be transformed into an $L_1$ norm minimization problem mathematically. Iterative reweighted algorithm can effectively solve the $L_1$ norm minimization problem by replacing $L_1$ norm with reweighted $L_2$ norm and computing the weight in each iteration. However, it suffers from a large computational load due to the repeated multiplications and inversions of large matrices. In this article, a fast azimuth super-resolution imaging method of RAR based on iterative reweighted least squares (IRLS) with linear sketching (LS) was proposed to achieve fast super-resolution imaging of RAR. The LS theory is employed to compress echo matrix and antenna measurement matrix into much smaller matrices via multiplying them by an embedded matrix. Then, the IRLS solver was utilized to address the reconstructed objective function. Much of the expensive computation can then be performed on the smaller matrices, thereby accelerating the algorithm. Simulations and experimental data prove that the proposed algorithm can offer a time complexity reduction without loss of imaging performance.

Guidance Method on a Target within a Dense Group

The guidance method on a separate target within a dense group for the implementation of trajectory control is proposed. This control technique solves the problem of transition from the typical angle resolution determined by the width of the directional pattern to the Doppler frequency resolution based on the Doppler beam sharpening effect with subsequent minimization of the current miss. The obtained law has been analyzed and the results of simulation are presented.

Simultaneous Super-Resolution and Target Detection of Forward-Looking Scanning Radar via Low-Rank and Sparsity Constrained Method

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, there is a blind zone of forward-looking imaging for conventional Doppler beam sharpening and synthetic aperture radar. Scanning radar can be utilized to obtain a real beam image of a forward-looking area and implement target detection, while its azimuth resolution is poor due to the limitation of antenna size. Besides, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, an integrated algorithm of super-resolution imaging and target detection for forward-looking scanning radar is proposed. In this algorithm, first of all, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging and the objective function is established. Subsequently, the convex theory is utilized to solve the objective function and transform the problem of simultaneous super-resolution imaging and target detection into an optimization problem. Lastly, the super-resolution imaging and the target detection results are obtained simultaneously by solving the optimization problem using the alternating direction method of multipliers. In addition, simulation and experiment results are given to verify the effectiveness of the proposed algorithm.

High-Resolution Forward-Looking Multichannel SAR Imagery With Array Deviation Angle Calibration

Traditional synthetic aperture radar (SAR) imaging is limited to achieve the high-resolution image of the side-looking areas. Nevertheless, equipped with a small size linear array across the trajectory, forward-looking multichannel SAR (FLMC-SAR) is capable of reconstructing the high-resolution image of the front area. In FLMC-SAR imaging framework, the left-right Doppler ambiguity is expected to resolve with beamforming approaches using the multichannel system diversity. However, beamforming-based Doppler ambiguity resolving is sensitive to the array deviation angle, which causes a mismatch between the azimuth angle and Doppler frequency. In this article, we propose an array deviation angle calibration and imagery algorithm for FLMC-SAR. The space–time characteristic of FLMC-SAR is explored and the range-dependent array deviation angle model is established. Following the Doppler beam sharpening imaging, strong targets are selected to derive the mismatch of the space–time characteristic. A maximum likelihood estimation of the array deviation angle is developed to modify the matching between the azimuth angle and Doppler frequency. Therefore, the left-right Doppler ambiguity can be solved correctly, yielding high-resolution FLMC-SAR imagery. Extensive simulation and real data experiments are performed to demonstrate the effectiveness of the proposed method.

Deception Jamming Against Doppler Beam Sharpening Radars

Missile seekers are becoming increasingly more capable of using Doppler Beam Sharpening (DBS) modes as part of the homing cycle, which requires new countermeasures against this mode. One type of countermeasure, is to create false targets within the seeker DBS image. This paper proposes a jamming technique to generate false targets at a precise location within a seeker DBS image, by both delaying and adding a Doppler shift to received waveforms. The effects of tracking errors on the position of the false target are analysed, both analytically and with simulations and used to assess the practical implementation of the jamming scheme. An experimental DBS system was built to test the effectiveness of the jamming scheme against a platform moving in steps and assess errors caused by incorrectly estimating the seeker trajectory.

항공기 탑재 AESA 레이다의 비행 시험을 통한 DBS 영상 형성

This study presents the results of the acquisitions of Doppler beam sharpening(DBS) images, obtained through flight trials of the airborne active electronically scanned array(AESA) radar. To use the AESA radar for DBS image acquisition, we designed methods for beam operation and image formation considering the beam operation peculiarities of the AESA radar. Selection methods of pulse repetition frequency(PRF) and waveforms to achieve the required azimuth resolution of the images are described, and the steps for implementation of our image formation method are presented and explained. Under the condition of mounting the AESA radar on the ramp door of cargo plane, methods for obtaining DBS images through flight trials are also explained, and the obtained images are compared with aerial ground photographs.

Sea-Surface Target Angular Superresolution in Forward-Looking Radar Imaging Based on Maximum A Posteriori Algorithm

Forward-looking imaging for sea-surface target has raised many concerns in the fields of vessel monitoring and sea rescue in all-day and all-weather time. Constrained by the imaging principle, conventional high-resolution radar imaging techniques, such as synthetic aperture radar and Doppler beam sharpening, are incapable of forward-looking imaging. Currently, scanning radar is the main tool to obtain forward-looking image, but it suffers from low angular resolution. Recently, much attention has been paid to superresolution methods, which can provide improved angular resolution over that of real-beam scanning radar. However, these methods are mainly used for ground targets, which mainly consider the ground clutter. For sea surface imaging, the distribution of sea clutter is more complex than the ground clutter. Besides, the performance of sea surface imaging is limited by the moving speed of targets. If we directly use these existing methods to sea-surface target imaging, it will lead to amplification of clutter and even elimination of the real target. In this paper, we consider the sea clutter distribution and present a Rayleigh-based sparse maximum a posteriori (RSMAP) deconvolution algorithm to improve the angular resolution of the sea-surface low-speed and static targets. First, the Rayleigh distribution is considered as the likelihood function because it can describe the amplitude characteristic of the sea clutter. Second, since the target concerned is often sparse compared with the imaging region, the sparse constraint is considered as the prior information of the sea-surface targets in objective function. Finally, the targets distribution is obtained by MAP estimation under the Bayesian framework. The simulations and measured data processing results have shown that the proposed algorithm provides better angular resolution and target location accuracy than traditional methods.

Airborne Forward-Looking Radar Super-Resolution Imaging Using Iterative Adaptive Approach

Airborne forward-looking radar (AFLR) imaging has raised many concerns in fields of Earth observation, independent of weather and daytime. Constrained by imaging principles, conventional high-resolution radar imaging techniques such as synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) are incapable of AFLR imaging. The real aperture radar (RAR) can obtain AFLR images using a scanning antenna, but suffers from coarse cross-range resolution. Recently, there has been much attention paid to the iterative adaptive approach (IAA), which draws from the benefits of RAR imaging and provides improved cross-range resolution. However, earlier work on the IAA imposed a convolution model on the received azimuth echo, neglecting the effect of the Doppler phase. This model mismatch degrades the imaging performance for moving platforms. To settle this problem, this paper first establishes a Doppler-convolution model of AFLR imaging, where both Doppler phase and antenna convolution effects are considered, allowing more accurate reconstruction when applying the IAA to formulate a super-resolution image. Then, a data-depended approach for Doppler centroid estimation is proposed to circumvent the problem of low estimation precision using platform motion parameters delivered by navigational devices mounted on the radar platform. Simulation results demonstrate that the proposed implementation of the IAA based on the Doppler-convolution model and Doppler centroid estimation can overcome the deficiencies of the SAR and DBS techniques in the forward-looking imaging direction, and present a noticeably superior performance as compared with conventional AFLR imaging methods.

Knowledge-Aided Doppler Beam Sharpening Super-Resolution Imaging by Exploiting the Spatial Continuity Information.

This paper deals with the problem of high cross-range resolution Doppler beam sharpening (DBS) imaging for airborne wide-area surveillance (WAS) radar under short dwell time situations. A knowledge-aided DBS (KA-DBS) imaging algorithm is proposed. In the proposed KA-DBS framework, the DBS imaging model for WAS radar is constructed and the cross-range resolution is analyzed. Since the radar illuminates the imaging scene continuously through the scanning movement of the antenna, there is strong spatial coherence between adjacent pulses. Based on this fact, forward and backward pulse information can be predicted, and the equivalent number of pulses in each coherent processing interval (CPI) will be doubled based on the autoregressive (AR) technique by taking advantage of the spatial continuity property of echoes. Finally, the predicted forward and backward pulses are utilized to merge with the initial pulses, then the newly merged pulses in each CPI are utilized to perform the DBS imaging. Since the number of newly merged pulses in KA-DBS is twice larger than that in the conventional DBS algorithm with the same dwell time, the cross-range resolution in the proposed KA-DBS algorithm can be improved by a factor of two. The imaging performance assessment conducted by resorting to real airborne data set, has verified the effectiveness of the proposed algorithm.

The maiden flight of Hinotori-C: The first C band full polarimetric circularly polarized synthetic aperture radar in the world

Synthetic Aperture Radar (SAR) is a very powerful tool in microwave remote sensing due to its capability of all-weather and day-to-night time operation [1]. Carl Wiley invented SAR in 1951 to overcome the poor azimuth resolution in conventional Side-Looking Airborne Radar (SLAR), followed by a patent filed in 1954 [2]. Unlike SLAR, using the forward motion of the platform and the principle of Doppler beam sharpening, the azimuth resolution of a SAR is equal to half the antenna length and is independent of the range distance [3]. Since the invention of SAR, steered by the breakthrough in science and technology, many advanced SAR techniques have been proposed and realized. These techniques include, but are not limited to, spotlight SAR for a finer image resolution [4], scan SAR for a wider swath coverage [5], and the remarkable polarimetric [6] and interferometric [7] SAR techniques using multichannel SAR system [8], [9], [10] for advanced remote sensing applications. Over the years, SAR has been widely used in different types of application, particularly in Earth observation such as disaster damage assessment [11], land deformation observation [12], oceanography [13], terrain classification [14], target detection [15], and so on. The diversified applications of SAR have encouraged the rapid development of airborne and spaceborne SAR sensors.

Doppler Beam Sharpening Using Estimated Doppler Centroid Based on Edge Detection and Fitting

Doppler beam sharpening (DBS) technology is widely used in applications, such as helicopter rescue and early warning surveillance. To obtain the desired DBS images with high quality, accurate Doppler centroid estimation (DoCE) is necessary. Conventional methods for Doppler centroid estimation based on navigational devices are sensitive to the errors of the measured motion parameters. Hence, several alternative data-depended approaches have been developed to reduce the error. In this paper, a novel data-depended Doppler centroid estimation method is proposed to improve the image quality of DBS. We begin the method by analyzing the characteristics of range-Doppler distribution in different regions of interests. Then, the edge feature of range-Doppler distribution in forward-looking direction is extracted using morphological filtering and edge detection methods. We will show that the edge feature defines the required Doppler centroid parameters, which can be utilized to estimate the Doppler centroids of the full scene. At last, the estimation error is reduced through fitting the edge with the minimum mean square error (MMSE) algorithm. As compared with conventional Doppler centroid estimation methods, the proposed method can significantly provide reliable estimation accuracy under low echo signal to noise ratio, independent of conditions that strictly required by conventional methods. Simulations and experiments verify the proposed method.

Application of Doppler beam sharpening for azimuth refinement in prospective low‐THz automotive radars

In this study, the authors investigate the application of the Doppler beam sharpening (DBS) technique for angular refinement to the emerging area of low-terahertz (THz) radar sensing. Ultimately this is to improve radar image quality in the azimuth plane to complement the excellent range resolution and thus improve object classification in low-THz radar imaging systems for autonomous platforms. The study explains the fundamental theory behind the process of DBS and describes the applicability of DBS to automotive sensing, indicating the potential for synthetic beamwidths of a fraction of a degree. Low-THz DBS was experimentally tested under controlled laboratory conditions, not only to accurately localised target objects in Cartesian space but also to provide unique object imaging at low-THz frequencies with wide azimuthal beamwidth antennas. It was shown that a stationary (i.e. non-scanned) wide beam antenna mounted on a moving platform can deliver imagery at least comparable to that produced by physical beamforming, be that steering arrays or narrow beam scanning antennas as in the experimental case presented.

Super-resolution Doppler beam sharpening method using fast iterative adaptive approach-based spectral estimation

Doppler beam sharpening (DBS) is a critical technology for airborne radar ground mapping in forward-squint region. In conventional DBS technology, the narrow-band Doppler filter groups formed by fast Fourier transform (FFT) method suffer from low spectral resolution and high side lobe levels. The iterative adaptive approach (IAA), based on the weighted least squares (WLS), is applied to the DBS imaging applications, forming narrower Doppler filter groups than the FFT with lower side lobe levels. Regrettably, the IAA is iterative, and requires matrix multiplication and inverse operation when forming the covariance matrix, its inverse and traversing the WLS estimate for each sampling point, resulting in a notably high computational complexity for cubic time. We propose a fast IAA (FIAA)-based super-resolution DBS imaging method, taking advantage of the rich matrix structures of the classical narrow-band filtering. First, we formulate the covariance matrix via the FFT instead of the conventional matrix multiplication operation, based on the typical Fourier structure of the steering matrix. Then, by exploiting the Gohberg–Semencul representation, the inverse of the Toeplitz covariance matrix is computed by the celebrated Levinson–Durbin (LD) and Toeplitz-vector algorithm. Finally, the FFT and fast Toeplitz-vector algorithm are further used to traverse the WLS estimates based on the data-dependent trigonometric polynomials. The method uses the Hermitian feature of the echo autocorrelation matrix R to achieve its fast solution and uses the Toeplitz structure of R to realize its fast inversion. The proposed method enjoys a lower computational complexity without performance loss compared with the conventional IAA-based super-resolution DBS imaging method. The results based on simulations and measured data verify the imaging performance and the operational efficiency.

Cross-Range Resolution Enhancement for DBS Imaging in a Scan Mode Using Aperture-Extrapolated Sparse Representation

This letter addresses the problem of cross-range superresolution in Doppler beam sharpening (DBS). The coherence of echoes in the azimuth direction and the sparsity of the DBS image in the Doppler domain are fully exploited; thus, a superresolution DBS imaging framework using aperture-extrapolated sparse representation (SR) is proposed. In this framework, aperture extrapolation based on the autoregressive model is utilized to predict the forward and backward information in the azimuth direction, and SR is exploited to extract the Doppler spectrum information. In addition, the resolution ability with different coherent processing intervals is analyzed. The sharpening ratio in this proposed algorithm can be improved by a factor of two or four theoretically in comparison with the conventional DBS imaging method. Experimental results demonstrate that the proposed framework can lead to noticeable performance improvement.

Regularized Deconvolution Method for the Resolution Enhancement of a Dual-Frequency Polarized Scatterometer on WCOM

A dual-frequency polarized scatterometer (DPSCAT) is proposed for the Chinese Water Cycle Observation Mission (WCOM) to be launched around 2020. DPSCAT is used to measure the snow water equivalent (SWE) and the freeze/thaw state, which requires a measurement precision of 0.5 dB and a relatively higher spatial resolution (2–5 km) than the regular scatterometers (about 25 km). Therefore, the conventional range-gate dechirping along with the Doppler beam sharpening (DBS) technique is used by DPSCAT to achieve high range and azimuth resolution simultaneously. However, DBS cannot improve the azimuth resolution over the nadir swath; thus, a new data processing, namely regularized deconvolution method (RDM), is explored to address this problem. In this paper, a quantitative analysis model is developed for RDM in order to study two crucial issues, i.e., the spatial resolution (mainly for the nadir swath) and the accuracy/precision of the backscatter measurements after resolution enhancement. Normally, the measurement precision and spatial resolution cannot be improved simultaneously using RDM. The accuracy/precision degrades as the spatial resolution improves, and vice versa. Moreover, they both degrade as the measurement noise or uncertainty increases, which latter is usually defined as the normalized standard deviation of the measurements ( $K_p$ ). In case of SWE retrieval that requires a reconstructed measurement precision of 0.5 dB, the best spatial resolution resolved by RDM is 3 km for $K_p$ = 7%, 4 km for $K_p$ = 10%, and 5 km for $K_p$ = 12%.

DBS(Doppler Beam Sharpening) 영상에서 표적 탐지 방안

DBS(Doppler Beam Sharpening) algorithm is a way to improve azimuth resolution performance in radar. Since DBS image includes the is information about the search area of radar, various clutter components exist besides the target to be detected. To detect and track the desired target in a DBS image, it must be able to identify a target and the clutter components. In this paper, we describe how to use image size and terrain information(DTED) to identify the target in a DBS image. By using morphological filter and chain code, it acquires image size and excludes the clutter components. By matching with DTED, we determine target.