Xia Bai

A Novel SAR Imaging Algorithm Based on Compressed Sensing

To reduce the amount of measurements, compressed sensing (CS) has been introduced to synthetic aperture radar (SAR). In this letter, a novel CS-SAR imaging algorithm is proposed, which consists of 2-D undersampling, range reconstruction, range-azimuth decoupling, and azimuth reconstruction. In the proposed algorithm, the range profile is reconstructed in the fractional Fourier domain, and range-azimuth decoupling in the case of azimuth undersampling is realized by using the reference function multiplication and chirp-z transform. Comparisons with the existing 2-D undersampling CS-SAR imaging algorithms are also presented. Experimental results from both simulated and real data demonstrate that the proposed algorithm can efficiently realize high-quality imaging with limited measurements.

Regularized smoothed ℓ 0 norm algorithm and its application to CS-based radar imaging

Sparse signal recovery is attractive in compressed sensing (CS). Based on the smoothed ?0 norm (SL0) algorithm, we have developed an error-tolerant regularized SL0 (ReSL0) algorithm, which has the same computational advantages as the SL0 algorithm while having better immunity against inaccuracy caused by noise or model mismatch. The performance of the ReSL0 is evaluated with simulated data. In addition, we have extended the ReSL0 to the matrix form (MReSL0), which is more suitable for dealing with matrix form signals and also has good resilience against inaccuracy. Finally we apply the ReSL0 and MReSL0 to joint CS-based radar imaging and phase error correction. Experimental results from both simulated and real data demonstrate that the proposed algorithms provide remarkable performance improvements in inaccurate scenarios (such as noisy data and mismatched settings) compared with the SL0 algorithm. HighlightsAn error-tolerant regularized SL0 algorithm is proposed to recover sparse signals.The matrix form regularized SL0 algorithm is derived to recover matrix form signals.The above algorithms are applied to joint CS-based radar imaging and phase error correction.

Parameter-searched OMP method for eliminating basis mismatch in space-time spectrum estimation

Abstract The basis mismatch in the sparse recovery based space-time adaptive processing (SR-STAP) can severely affect the reconstruction of the space-time spectrum. In this paper, we propose a parameter-searched orthogonal matching pursuit (PSOMP) algorithm to eliminate the effect of the basis mismatch in SR-STAP. In the PSOMP, the optimal parameters carried in the atom selected by the OMP are searched so that this atom can match with the true steering vector of the corresponding scatter in the data. The feasibility of the key step of the PSOMP is theoretically analyzed. Finally, simulation results demonstrate that the PSOMP has a high performance in eliminating the effect of the basis mismatch in SR-STAP.

Coherence-based analysis of modified orthogonal matching pursuit using sensing dictionary

Compressed sensing (CS) has attracted considerable attention in signal processing because of its advantage of recovering sparse signals with lower sampling rates than the Nyquist rates. Greedy pursuit algorithms such as orthogonal matching pursuit (OMP) are well-known recovery algorithms in CS. In this study, the authors study a modified OMP proposed by Schnass et al., which uses a special sensing dictionary to identify the support of a sparse signal while maintaining the same computational complexity. The performance guarantee of this modified OMP in recovering the support of a sparse signal is analysed in the framework of mutual (cross) coherence. Furthermore, they discuss the modified OMP in the case of bounded noise and Gaussian noise, and show that the performance of the modified OMP in the presence of noise relies on the mutual (cross) coherence and the minimum magnitude of the non-zero elements of the sparse signal. Finally, simulations are constructed to demonstrate the performance of the modified OMP.

Fast FOCUSS method based on bi-conjugate gradient and its application to space-time clutter spectrum estimation

The focal underdetermined system solver (FOCUSS) is a powerful tool for sparse representation in complex underdetermined systems. This paper presents the fast FOCUSS method based on the bi-conjugate gradient (BICG), termed BICG-FOCUSS, to speed up the convergence rate of the original FOCUSS. BICGFOCUSS was specifically designed to reduce the computational complexity of FOCUSS by solving a complex linear equation using the BICG method according to the rank of the weight matrix in FOCUSS. Experimental results show that BICG-FOCUSS is more efficient in terms of computational time than FOCUSS without losing accuracy. Since FOCUSS is an efficient tool for estimating the space-time clutter spectrum in sparse recoverybased space-time adaptive processing (SR-STAP), we propose BICG-FOCUSS to achieve a fast estimation of the space-time clutter spectrum in mono-static array radar and in the mountaintop system. The high performance of the proposed BICG-FOCUSS in the application is demonstrated with both simulated and real data.

Improved two dimensional autofocus of SAR imaging

With the resolution of SAR getting finer, the traditional 1-D autofocus algorithm may fail to cope with the situation that a targets echo exhibits a residual range migration error exceeding one or more range resolution cells during the course of the synthetic aperture. In this case 2-D autofocus becomes particularly important. Commonly used 2-D autofocus algorithm is based on range correlation and PGA, and is of high computational complexity. In this paper we propose an improved 2-D autofocus algorithm of SAR imaging, in which a subset of adjacent range resolution cells containing the prominent scatterers are selected and process by mean location method. Experiments on simulated data and real data show the effectiveness of improved method.

Improved RIP-based performance guarantees for multipath matching pursuit

The multipath matching pursuit (MMP) is a generalization of the orthogonal matching pursuit (OMP), which generates multiple child paths for every candidate in each iteration and selects the candidate having the minimal residual as the final support set when iteration ends. In this paper we analyze its performance in both noiseless and noisy cases. The restricted isometry property (RIP)-based condition of MMP that ensures accurate recovery of sparse signals in the noiseless case is derived by using a simple technique. The performance guarantees of the MMP for support recovery in noisy cases are also discussed. It is shown that under certain conditions on the RIP and minimum magnitude of nonzero components of the sparse signal, the MMP will exactly recover the true support of the sparse signal in cases of bounded noises and recover the true support with a high probability in the case of Gaussian noise. Our bounds on the RIP improve the existing results.

ISAR imaging of target with complex motion associated with the fractional Fourier transform

Abstract High-resolution inverse synthetic aperture radar (ISAR) imaging based on parameter estimation of polynomial phase signal is a quite significant research hotspot, in which the azimuth echo can be modeled as multi-component quadratic frequency modulation (QFM) signal after preprocessing, which leads to the time-varying Doppler frequency. In this paper, an effective parameter estimation method called the product form of symmetric correlation function based on the fractional Fourier transform (PFrSCF) is proposed. In proposed method, a novel symmetric correlation function is used to reduce phase order of QFM signal firstly. Then, the PFrSCF can estimate two parameters of QFM signal simultaneously by the fractional Fourier transform and suppress cross term by the product in fractional Fourier transform domain. Compared with other methods, the PFrSCF is capable of suppressing cross term effectively and ensuring the good accuracy of parameters. Moreover, the PFrSCF is robust in noisy environment. Finally, associated with range-instantaneous-Doppler imaging technology, a novel ISAR imaging algorithm is presented based on PFrSCF method. The performances of PFrSCF method and the corresponding ISAR imaging algorithm of target are verified by simulated and real data.

Adaptive noise depression CSISAR imaging via OMP with CFAR thresholding

Compressed sensing (CS)-based inverse synthetic aperture radar (ISAR) imaging with limited pulses performs well in the case of high signal-to-noise ratios. However, strong noise are usually inevitable in radar imaging, which challenges the CS-based approach. In this paper, we present an adaptive noise depression CS-ISAR imaging algorithm, which is based on constant false alarm rate (CFAR). Firstly, the noise level is estimated from the noise range cells which are discriminated by energy thresholding. Then the ISAR images are reconstructed via orthogonal matched pursuit (OMP), in which the iteration is terminated by a preseted residual thresholding (RT). The RT is set according to the estimated noise level for a certain CFAR. Experiments verify the efficiency of the proposed method.

A processing scheme for long integration time passive radar based on CZT and FRFD-sharpness

In this paper a problem of extending integration time in passive radar is addressed. To compensate range and Doppler walk, a processing scheme based on chirp-z transform (CZT) and fractional Fourier transform domain (FRFD)-sharpness is proposed: (1) divide the signal into snapshots; (2) perform Fourier transform (FT) and matched filtering on each snapshot; (3) perform CZT across the snapshots; (4) perform inverse FT (IFT) on each snapshot; (5) perform IFT and fractional Fourier transform (FRFT) across the snapshots; (6) obtain the final radar image of moving targets by sharpness metrics. Experiment result by using DTV-based passive radar data has shown that the processing scheme can effectively mitigate range and Doppler walk, and it allows meaningful increases to the integration time.