Target Imaging Algorithm Research Articles

Clutter suppression and moving target imaging approach for multichannel hypersonic vehicle borne radar

In this paper, a clutter suppression and ground moving target imaging algorithm is proposed for the hypersonic vehicle (HSV) borne multichannel synthetic aperture radar (SAR) system. Different from the traditional horizontal flight radar platforms, the HSV-borne radar platform is with a complex movement, and typically has a skipping trajectory. Therefore, the clutter suppression and imaging operations are difficult to implement. To deal with this problem, a descending stage signal model of multichannel SAR system is presented and studied for the HSV-borne radar. Then, the clutter and moving target echoes are transformed into the azimuth frequency domain. Considering that there exists a phase difference between clutter and the moving target, the clutter suppression approach is implemented with a series of spatial domain filters. After that, the moving target is focused based on the keystone transform. In addition, the performance analyses and related issues of the proposed method are presented. Finally, some simulation experiments are taken to validate the effectiveness of the proposed method.

Sparsity-Driven SAR Imaging for Highly Maneuvering Ground Target by the Combination of Time-Frequency Analysis and Parametric Bayesian Learning

In this paper, a well-focused synthetic aperture radar (SAR) image for highly maneuvering ground target is formed. For high-resolution SAR imaging, the phase modulation from the maneuverer's high-order movements severely degrades the focusing quality of the target image, if the conventional SAR imaging algorithm under the constant target velocity assumption is used. To deal with this problem, a new SAR ground moving target imaging (GMTIm) algorithm is presented with a two-step strategy to obtain a high-resolution maneuvering target image with highly focused responses. Pseudo Wigner–Ville distribution is first employed to access and compensate for the bulk of the high-order phase. Then, to further enhance the target image quality, the SAR-GMTIm problem is solved by sparse Bayesian learning (SBL), where an accurate phase autofocusing is incorporated for the compensation of the residual high-order phase. A novel time-frequency representation, known as Lv's distribution, is adopted to determine the parametric dictionary used in the SBL processing. To accommodate the possible multiple-target imaging scenario, the intended SAR-GMTIm algorithm is developed in a coarse-to-fine compensation procedure. Finally, both simulated data and real Gotcha data are applied to validate the effectiveness and superiority of the proposed SAR imaging algorithm for ground maneuvering targets.

An ISAR Imaging Algorithm for Maneuvering Targets With Low SNR Based on Parameter Estimation of Multicomponent Quadratic FM Signals and Nonuniform FFT

The inverse synthetic aperture radar (ISAR) imaging for maneuvering targets has always been a challenging task due to the time-varying Doppler parameter, especially in the low signal-to-noise ratio (SNR) environment. In this paper, a novel ISAR imaging algorithm for maneuvering targets in the low SNR environment based on the parameter estimation approach is presented. First, the received signals of the ISAR in a range bin are modeled as a multicomponent quadratic frequency-modulated (QFM) signal after the migration compensation. Then, to estimate the parameters of the QFM signal, two cubic phase functions (CPFs), namely coherently integrated generalized CPF (CIGCPF) and coherently integrated CPF (CICPF), are developed. The CIGCPF and CICPF are simple and only require the fast Fourier transform (FFT) and the nonuniform FFT (NUFFT). Due to the usage of the NUFFT, the computational cost is reduced, and the searching procedure is unnecessary for the nonuniformly spaced signal. Thanks to the coherent integrations and NUFFT, the CIGCPF and CICPF, which demonstrate the excellent noise-tolerant performance and reduce the error propagation effect, are efficient and suitable for the multicomponent QFM signals in the low SNR environment. Finally, several simulation examples, analyses of the noise-tolerant performance of the proposed method, and ISAR imaging results of the simulated data demonstrate the effectiveness of the proposed method.

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization

This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However, most of the existing compressive sensing (CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization (ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm (pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming (SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.

SAR Ground Moving Target Imaging Algorithm Based on Parametric and Dynamic Sparse Bayesian Learning

In this paper, a novel synthetic aperture radar (SAR) ground moving target imaging (GMTIm) algorithm is presented within a parametric and dynamic sparse Bayesian learning (SBL) framework. A new time–frequency representation, which is known as Lv's distribution (LVD), is employed on the moving targets to determine the parametric dictionary used in the SBL framework. To combat the inherent accuracy limitations of the LVD and extrinsic perturbation errors, a dynamical refinement process is further developed and incorporated into the SBL framework to obtain highly focused SAR image of multiple moving targets. An emerging inference technique, which is known as variational Bayesian expectation–maximization, is applied to achieve an efficient Bayesian inference for the focused SAR moving target image. A remarkable advantage of the proposed algorithm is to provide a fully posterior distribution (Bayesian inference) for the SAR moving target image, rather than a poor point estimate used in conventional methods. Because of utilizing high-order statistical information, the error propagation problem is desirably ameliorated in an iterative manner. The perturbations, known as the multiplicative phase error and additive clutter and noise, are both well adjusted for further improving the image quality. Experimental results by using simulated spotlight-SAR data and real Gotcha data have demonstrated the superiority of the proposed algorithm over other reported ones.

Airborne SAR Moving Target Signatures and Imagery Based on LVD

This paper presents a new ground moving target imaging (GMTIm) algorithm for airborne synthetic aperture radar (SAR) based on a novel time-frequency representation (TFR), Lv's distribution (LVD). We first analyze generic moving target signatures for a multichannel SAR and then derive the analytical spectrum of a point target moving at a constant velocity by a polar format algorithm for SAR image formation. SAR motion deviation from a predetermined flight track is considered to facilitate airborne SAR applications. LVD, as a recently developed TFR for the analysis of multicomponent linear-frequency-modulated signal, is adopted to represent the target kinematic spectrum in the Doppler centroid frequency and chirp rate domain. As a result, the proposed SAR-GMTIm algorithm is capable of imaging multiple moving targets even when they are located at the same range resolution cell. Some practical issues such as imaging maneuvering targets and small/weak targets are discussed to enhance the applicability of the proposed algorithm. Simulation results with isotropic point moving targets are presented to validate the effectiveness and superiority of the proposed algorithm. Raw data collected by an airborne multichannel SAR are also used to verify the performance improvement made by the proposed algorithm.

Detection and Imaging of Ground Moving Targets With Real SAR Data

Detection and imaging of multiple moving targets is a challenging task, particularly with real synthetic aperture radar (SAR) data. In this paper, moving targets with different motion parameters are classified based on the relative locations of their spectra to that of the clutter. Based on the classification, a novel moving target processing strategy with real SAR data is proposed, including a two-step ground moving target indication (GMTI) algorithm and a practical ground moving target imaging (GMTIm) algorithm with motion error compensation. The two-step GMTI algorithm has the ability of indicating multiple moving targets, particularly those submerged by the clutter; the practical GMTIm algorithm shows a robust performance in real data moving target imaging since the motion errors are estimated and compensated. Both simulated and real data processing results are provided to demonstrate the effectiveness of the proposed strategy.

High-Resolution SAR Imaging of Ground Moving Targets Based on the Equivalent Range Equation

In this letter, a novel ground moving target imaging algorithm suitable for high-resolution synthetic aperture radar (SAR) is proposed. The key step of the proposed algorithm is to make a moving target equivalent to a static one based on the derived equivalent range equation. Due to such equivalence, the commonly used standard algorithms for stationary scene imaging (e.g., the range Doppler algorithm) can be easily adapted to focus moving targets by adding only a search operation. The proposed algorithm does not make any approximation to the range equation and is thus rather suitable for high-resolution imaging of moving targets. In addition, due to the used equivalence, to focus a slow-moving target with four unknown parameters (two motion parameters plus two position parameters), the dimensionality of the parameter search space is reduced to one. Experimental results of the simulated data validate the proposed algorithm.

Robust Clutter Suppression and Moving Target Imaging Approach for Multichannel in Azimuth High-Resolution and Wide-Swath Synthetic Aperture Radar

This paper describes a clutter suppression approach and the corresponding moving target imaging algorithm for a multichannel in azimuth high-resolution and wide-swath (MC-HRWS) synthetic aperture radar (SAR) system. Incorporated with digital beamforming processing, MC-HRWS SAR systems are able to suppress the Doppler ambiguities to allow for HRWS SAR imaging and null the clutter directions to suppress clutter for ground moving target indication. In this paper, the degrees of freedom in azimuth for the multichannel SAR systems are employed to implement clutter suppression. First, the clutter and moving target echoes are transformed into the range compression and azimuth chirp Fourier transform frequency domain, i.e., coarse-focused images formation, when the clutter echoes are with azimuth Doppler ambiguity. Considering that moving targets are sparse in the imaging scene and that there is a difference between clutter and a moving target in the spatial domain, a series of spatial domain filters are constructed to extract moving target echoes. Then, using an extracted moving target echo, two groups of signals are formed, and slant-range velocity of a moving target can be estimated based on baseband Doppler centroid estimation algorithm and multilook cross-correlation Doppler centroid ambiguity number resolving approach. After the linear range cell migration correction and azimuth focus processing, a well-focused moving target image can be obtained. In addition, the proposed clutter suppression and imaging approach is not only adapted for uniformly displaced phase center sampling but also for the nonuniform sampling cases. Some simulation experiments are taken to demonstrate our proposed algorithms. Finally, some real measured data results are presented to validate the theoretical investigations and the proposed approaches.

Improved HHT and its application in narrowband radar imaging for precession cone-shaped targets

Imaging the spatial precession cone-shaped targets with narrowband radar is a new technical approach in mid-course recognition problem. However, most existing time-frequency methods still have some inevitable deficiencies for extracting microDoppler information in practical applications, which leads to blurring of the image. A new narrowband radar imaging algorithm for the precession cone-shaped targets is proposed. The instantaneous frequency of each scattering point is gained by using the improved Hilbert-Huang transform, then the positions of scattering points in the parameter domain are reconstructed. Numerical simulation and experiment results confirm the effectiveness and high precision of the proposed algorithm.

ISAR Imaging of Targets With Complex Motion Based on the Chirp Rate–Quadratic Chirp Rate Distribution

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motion such as fluctuating ships with oceanic waves and high maneuvering airplanes, the azimuth echo signals can be modeled as cubic phase signals (CPSs) after the migration compensation. The chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities of the CPS, which deteriorate the azimuth focusing quality due to the Doppler frequency shift. With these two quantities, other parameters can be estimated by using the fast Fourier transform (FFT). Therefore, the CPS can be uniquely determined by both CR and QCR. In this paper, based on the proposed generalized keystone transform and the parametric instantaneous autocorrelation function, a novel distribution of the CPS, known as the CR–QCR distribution (CRQCRD), is presented and applied in a newly proposed ISAR imaging algorithm for targets with complex motion. The CRQCRD is simple and only requires the FFT and the nonuniform FFT (NUFFT). Owing to the application of the NUFFT, the computational cost is saved, and the searching procedure is unnecessary for the nonuniformly spaced signal. Compared to other four representative methods for CPSs, the CRQCRD, which can acquire higher antinoise performance and no error propagation, is searching-free and more suitable for the situation of multitargets. Several simulation examples, analyses of the antinoise performance, and ISAR images validate the effectiveness of the CRQCRD and the corresponding ISAR imaging algorithm.

Ground moving target imaging algorithm for single channel airborne CSSAR

Airborne circular stripmap synthetic aperture radar (CSSAR) has the advantages of short revisit time and large coverage, and thus is an attractive tool for wide area surveillance. A novel ground moving target imaging algorithm for single channel airborne CSSAR is proposed. The proposed algorithm can be implemented without knowing a target’s motion parameters. Simulation results indicate that moving targets can be well focused by the proposed algorithm.

A Novel ISAR Imaging Algorithm for Micromotion Targets Based on Multiple Sparse Bayesian Learning

A novel inverse synthetic aperture radar (ISAR) imaging algorithm for micromotion targets based on multiple sparse Bayesian learning (MSBL) is proposed. First, the signal of the main body is reconstructed by the MSBL method based on the property of its common profile. Subsequently, the signal of rotating parts can be obtained by removing the main body signal from the original signal. Finally, a clear ISAR image of the main body and the micromotion parameter of the rotating parts can be obtained. Numerical results based on simulated and measured data show that the proposed algorithm can not only acquire a clear ISAR image of the main body but also extract the micromotion parameter of the rotating parts effectively.

A Novel Moving Target Imaging Algorithm for HRWS SAR Based on Local Maximum-Likelihood Minimum Entropy

For high-resolution wide-swath (HRWS) SAR based on multiple receive apertures in azimuth, this paper proposes a novel imaging approach for moving targets. This approach utilizes the wide bandwidth characteristics of the transmitted signal (multiple wavelengths) to estimate the moving target velocity. First, this paper explains that there is a phase mismatch (PM) between azimuth channels for the echo of a moving target, which depends on range frequency. In order to correct the PM, an algorithm based on local maximum-likelihood minimum entropy is proposed. The linear dependence of the PM on range frequency is employed to estimate the target velocity. Second, after the signal reconstruction in Doppler frequency and the compensation of the PM for a moving target, the estimated target velocity is utilized to implement the linear range cell migration correction and the Doppler centroid shifting. Then, the quadratic range cell migration is corrected by the keystone processing. After that, the focused moving target image can be obtained using the existing azimuth focusing approaches. Theoretical analysis shows that no interpolation is needed. The effectiveness of the imaging algorithm for moving targets is demonstrated via simulated and real measured ship HRWS ScanSAR data.

New SAR target imaging algorithm based on oblique projection for clutter reduction

We have developed a new synthetic aperture radar (SAR) algorithm based on physical models for the detection of a man-made target (MMT) embedded in strong clutter (trunks in a forest). The physical models for the MMT and the clutter are represented by low-rank subspaces and are based on scattering and polarimetric properties. Our SAR algorithm applies the oblique projection of the received signal along the clutter subspace onto the target subspace. We compute its statistical performance in terms of probabilities of detection and false alarms. The performances of the proposed SAR algorithm are improved compared to those obtained with existing SAR algorithms: the MMT detection is greatly improved, and the clutter is rejected. We also studied the robustness of our SAR algorithm to interference modeling errors. Results on real foliage penetration data showed the usefulness of this approach.

Research on the 3D imaging algorithm of spin target based on the Hough transform

As one of the most typical characteristics in space target motion, spin phenomenon has good 3D imaging application potential. Conventional target imaging algorithm fails to make full use of the rotating features of the target to obtain the characteristics of target space, and the speedy spin of targets will cause the dramatic changes in the positions of scattering center within short observation session, which may lead to the failure of imaging algorithm. Aiming at such a special phenomenon of the space target, the time frequency distribution curve of echoes in each scattering center could be mapped onto the parameter space to obtain the position of each scattering center by taking advantage of Hough transformation, thus the 3D features of spin target could be obtained. In this article, the 3D imaging algorithm was studied on the basis of Hough transformation, and its effectiveness was tested with simulation. Meanwhile, the translational motion and shielding effect of space target were discussed, and favorable imaging results were achieved.

ENERGY ESTIMATION BASED TR-MUSIC MICROWAVE IMAGING FOR EXTENDED TARGETS

A simple and efiective double-thresholding strategy based on energy estimation is proposed to choose the optimal boundary between the signal subspace and noise subspace in TR-MUSIC algorithm for microwave imaging of extended targets. Simulations and imaging results are given to demonstrate its strong noise rejection and super-resolution capability. In the new method, the shape details of extended targets can be obtained from single frequency or multi- frequency scattering data.

Accurate and Nonparametric Imaging Algorithm for Targets Buried in Dielectric Medium for UWB Radars

Accurate and Nonparametric Imaging Algorithm for Targets Buried in Dielectric Medium for UWB Radars

Lean CLEAN: Deconvolution Algorithm for Radar Imaging of Contiguous Targets

High-resolution microwave images of contiguous targets often suffer from sidelobe artifacts. Deconvolution algorithms, such as Coherent CLEAN, do not perform well in these scenarios because of spurious peaks and peak mislocations. These spurious and mislocated peaks are often interpreted as real targets by the CLEAN algorithm. In this paper, we propose the Lean CLEAN technique that addresses this problem. The Lean CLEAN technique consists of a postprocessing block that works with the CLEAN algorithm. It detects and discards the spurious peaks and then consolidates the multiple small peaks corresponding to mislocated targets. Extensive simulations suggest that the proposed technique works well with different CLEAN techniques.

Analysis of wide-angle radar imaging

In wideband radar imaging, it is necessary to increase the target observation angle, that is, the imaging time to obtain high azimuth resolution. For wide-angle targets, however, migration through resolution cell will occur and the Doppler information of the scatterers will be time varying, which can lead to image defocusing in both the range and azimuth domain if the range–Doppler (RD) method is applied. Aimed at this problem, this study builds up the imaging model for steadily moving targets with a constant rotation axis. Then it compares the RD algorithm, the Keystone algorithm and the polar format algorithm with theoretical analysis and deduces the maximum observation angles that fit for these methods, respectively. Additionally, this study proposes a new algorithm for imaging of targets with large observation angles based on the complex inverse Radon transform. Instead of applying the Taylor's series approximation, this method strictly follows the signal model for scatterers with a wide observation angle and utilises curvilinear integral with phase information for image formation. Therefore it is valid in wide-angle scenarios. Finally, imaging results for both the simulated and measured data are given to analyse the performance of the available algorithms and to prove the validity of the proposed algorithm.