Lei Zhang

Resolution Enhancement for Inversed Synthetic Aperture Radar Imaging Under Low SNR via Improved Compressive Sensing

The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in radar imaging, which challenges current high-resolution imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.

Achieving Higher Resolution ISAR Imaging With Limited Pulses via Compressed Sampling

Recent theory of compressed sampling (CS) suggests that exact recovery of an unknown sparse signal with overwhelming probability can be achieved from very limited number of samples. In this letter, we adapt this idea and present a framework of high-resolution inverse synthetic aperture radar imaging with limited measured data. During the framework, we mathematically convert the imaging into a problem of signal reconstruction with orthogonal basis; hence, a conceptive upper bound of the cross-range resolution is presented based on the CS theory. Real data results show that the CS imaging approach outperforms the conventional range-Doppler one in resolution.

High-Resolution ISAR Imaging With Sparse Stepped-Frequency Waveforms

From the theory of compressive sensing (CS), we know that the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity-constrained optimization problem. For inverse synthetic aperture radar (ISAR) imaging, the backscattering field of a target is usually composed of contributions by a very limited amount of strong scattering centers, the number of which is much smaller than that of pixels in the image plane. In this paper, a novel framework for ISAR imaging is proposed through sparse stepped-frequency waveforms (SSFWs). By using the framework, the measurements, only at some portions of frequency subbands, are used to reconstruct full-resolution images by exploiting sparsity. This waveform strategy greatly reduces the amount of data and acquisition time and improves the antijamming capability. A new algorithm, named the sparsity-driven High-Resolution Range Profile (HRRP) synthesizer, is presented in this paper to overcome the error phase due to motion usually degrading the HHRP synthesis. The sparsity-driven HRRP synthesizer is robust to noise. The main novelty of the proposed ISAR imaging framework is twofold: 1) dividing the motion compensation into three steps and therefore allowing for very accurate estimation and 2) both sparsity and signal-to-noise ratio are enhanced dramatically by coherent integrant in cross-range before performing HRRP synthesis. Both simulated and real measured data are used to test the robustness of the ISAR imaging framework with SSFWs. Experimental results show that the framework is capable of precise reconstruction of ISAR images and effective suppression of both phase error and noise.

High-Resolution ISAR Imaging by Exploiting Sparse Apertures

Compressive sensing (CS) theory indicates that the optimal reconstruction of an unknown sparse signal can be achieved from limited noisy measurements by solving a sparsity-driven optimization problem. For inverse synthetic aperture radar (ISAR) imagery, the scattering field of the target is usually composed of only a limited number of strong scattering centers, representing strong spatial sparsity. This paper derives a new autofocus algorithm to exploit the sparse apertures (SAs) data for ISAR imagery. A sparsity-driven optimization based on Bayesian compressive sensing (BCS) is developed. In addition, we also propose an approach to determine the sparsity coefficient in the optimization by using constant-false-alarm-rate (CFAR) detection. Solving the sparsity-driven optimization with a modified Quasi-Newton algorithm, the phase error is corrected by combining a two-step phase correction approach, and well-focused image with effective noise suppression is obtained from SA data. Real data experiments show the validity of the proposed method.

Bayesian Inverse Synthetic Aperture Radar Imaging

In this letter, a novel algorithm of inverse synthetic aperture radar (ISAR) imaging based on Bayesian estimation is proposed, wherein the ISAR imaging joint with phase adjustment is mathematically transferred into signal reconstruction via maximum a posteriori estimation. In the scheme, phase errors are treated as model errors and are overcome in the sparsity-driven optimization regardless of the formats, while data-driven estimation of the statistical parameters for both noise and target is developed, which guarantees the high precision of image generation. Meanwhile, the fast Fourier transform is utilized to implement the solution to image formation, promoting its efficiency effectively. Due to the high denoising capability of the proposed algorithm, high-quality image also could be achieved even under strong noise. The experimental results using simulated and measured data confirm the validity.

A Robust Motion Compensation Approach for UAV SAR Imagery

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an essential tool for modern remote sensing applications. Owing to its size and weight constraints, UAV is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, a novel databased motion compensation (MOCO) approach is proposed for the UAV SAR imagery. The approach is implemented by a three-step process: 1) The range-invariant motion error is estimated by the weighted phase gradient autofocus (WPGA), and the nonsystematic range cell migration function is calculated from the estimate for each subaperture SAR data; 2) the retrieval of the range-dependent phase error is executed by a local maximum-likelihood WPGA algorithm; and 3) the subaperture phase errors are coherently combined to perform the MOCO for the full-aperture data. Both simulated and real-data experiments show that the proposed approach is appropriate for highly precise imaging for UAV SAR equipped with only low-accuracy inertial navigation system.

Wavenumber-Domain Autofocusing for Highly Squinted UAV SAR Imagery

Being capable of enhancing the flexibility and observing ability of synthetic aperture radar (SAR), squint mode is one of the most essential operating modes in SAR applications. However, processing of highly squinted SAR data is usually a challenging task attributed to the spatial-variant range cell migration over a long aperture. The Omega-k algorithm is generally accepted as an ideal solution to this problem. In this paper, we focus on using the wavenumber-domain approach for highly squinted unmanned aerial vehicle (UAV) SAR imagery. A squinted phase gradient autofocus (SPGA) algorithm is proposed to overcome the severe motion errors, including phase and nonsystematic errors. Herein, the inconsistence of phase error and range error in the squinted wavenumber-domain imaging is first presented, which reveals that even the motion error introduces very small phase error, it causes considerable range error due to the Stolt mapping. Based on this, two schemes of SPGA-based motion compensation are developed according to the severity of motion error. By adapting the advantages of weighted phase gradient autofocus and quality phase gradient autofocus, the robustness of SPGA is ensured. Real measured data sets are used to validate the proposed approach for highly squinted UAV-SAR imagery.

Compensation for the NsRCM and Phase Error After Polar Format Resampling for Airborne Spotlight SAR Raw Data of High Resolution

When the range migration caused by motion error exceeds the range cell resolution, the performance of a conventional phase autofocus approach degrades. In this paper, a new adaptive motion compensation (MoCo) algorithm with the removal of the migration that is nonsystematic has been developed for airborne spotlight synthetic aperture radar (SAR) imagery with high resolution. In the algorithm, the relationship between nonsystematic range cell migration (NsRCM) and phase error was first explicitly revealed after the polar format algorithm resampling. The NsRCM could be readily calculated by coarse but reliable phase error estimation. Subsequently, the NsRCM and the bulk of the azimuth phase error were corrected. After the removal of the NsRCM, degradation of the conventional phase autofocus resulting from sidelobe increase as well as mainlobe broadening was avoided. Finally, a fine MoCo procedure was performed to remove the residual azimuth phase error satisfactorily. Through the analysis of the airborne spotlight SAR raw data with high-resolution and wide-swath illumination, a well-focused imagery was obtained. Quantitative assessment of the image quality was satisfactory. The MoCo algorithm was validated.

Multichannel HRWS SAR Imaging Based on Range-Variant Channel Calibration and Multi-Doppler-Direction Restriction Ambiguity Suppression

In order to obtain high-resolution wide-swath (HRWS) images, the multichannel in azimuth synthetic aperture radar (SAR) system has been adopted to deal with the contradiction problem between high resolution and low pulse repetition frequency (PRF). In this paper, a novel channel-calibration method is proposed for the multichannel in azimuth HRWS SAR imaging system. During the channel calibration, the mismatch between the channels, which results from the gain-phase error and the range sampling time error, is first corrected by the coarse-calibration processing in the range frequency domain. Then, the along azimuth baseline measurement error is estimated. Considering the range variance in the residual phase error, the data are processed in blocks along the range time domain, and the error of every subblock data is estimated. After that, a fitting and filtering is implemented along the range to the estimated values of the phase error of all subblocks. The range-variant phase error is then compensated using their estimated values. After channel calibration, this paper also presents a new Doppler ambiguity suppression algorithm which nulls the ambiguity components in the Doppler domain. The newly proposed algorithm outperforms the post-Doppler ambiguity suppression algorithm. The airborne real measured scan synthetic aperture radar data, which are acquired by a seven-channel in azimuth SAR imaging system with the system working at X-band, are utilized to demonstrate the performance of the newly proposed channel-calibration method and the new Doppler ambiguity suppression algorithm.

Sparse Apertures ISAR Imaging and Scaling for Maneuvering Targets

In advanced multifunctional radar, inverse synthetic aperture radar (ISAR) imaging of sparse apertures for maneuvering targets is a challenge problem. In general, the Doppler modulation of rotation motion can be modeled as linear frequency for uniformly accelerated rotation targets, which is spatial-variant in two-dimension (2-D). The signal diversity inherently reflects the maneuverability and provides a rationale of rotation motion estimation. In this paper, we focus on the problem of sparse apertures ISAR imaging and scaling for maneuvering targets. The maneuvering signal model is formulated as chirp code and represented using a chirp-Fourier basis. Then sparse representation is applied to realize range-Doppler (RD) imaging from the sparse apertures, where the superposition of chirp parameters is acquired using the modified discrete chirp Fourier transform (MDCFT). After preprocessing, such as sample selection, rotation center determination, and noise reduction, the chirp parameters are used to estimate the parameters of rotation motion using the weighted least square (WLS) method. Finally, a high-resolution scaled-ISAR image is achieved by rescaling the acquired RD image using the estimated rotation velocity. Experiments are performed to confirm the effectiveness of the proposal.