To achieve efficient ship detection in high-resolution synthetic aperture radar images, an improved superpixel-level constant false alarm rate (CFAR) detection method is proposed with three modifications. First, the weighted information entropy (WIE) describes the statistical characteristics of superpixels, yielding a better distinction between target and clutter superpixels. Second, a two-stage CFAR detection scheme is proposed to detect target superpixels, including global detection and local detection. Specifically, the WIE-based global detector is utilized to prescreen candidate target superpixels (CTSs) and then the local CFAR detector is adaptively conducted over the selected CTSs to refine target superpixels. Third, the superpixels selected by the global detection can be excluded as outliers for clutter estimation, thus the disturbance from adjacent targets in multitarget situations can be reduced and more accurate detection results can be obtained. Compared with CFAR methods implemented with the sliding window technique, the computational burden of the proposed method is significantly reduced without loss of detection performance. Experiments of ship detection on three TerraSAR-X datasets are presented to validate the effectiveness of the proposed method.

For the automatic target recognition (ATR) based on synthetic aperture radar (SAR) images, enough training data are required to effectively characterize target features and obtain good recognition performance. However, in practical applications, it is difficult to collect sufficient training data. To tackle the limitation, a novel end-to-end expansion method, called conditional Wasserstein deep convolutional generative adversarial network with gradient penalty (CWDCGAN), is proposed to achieve SAR image expansion with specified category. To be specific, the CWDCGAN innovatively designed a generative adversarial network architecture based on convolutional and deconvolution networks to improve the quality of generated images. At the same time, conditional information is introduced to control the categories of generated images, and Wasserstein distance and gradient penalty are used to modify the loss function, which makes the network training more stable. Besides, feature extraction and classifier design in a typical ATR system often rely heavily on subjective expert knowledge, which seriously affects its generalization performance. Therefore, a joint recognition method of Resnet18 and support vector machine (Renset18-SVM) is adopted to improve the generalization capacity and the recognition performance. Experimental results with public measured data show that the CWDCGAN can generate higher quality SAR images, and by feeding expanded data to Renset18-SVM, the recognition accuracy is improved under different proportions of training samples.

Dual-circular polarimetric (DCP) high-resolution range profile (HRRP) provides similar target information to the full polarimetric HRRP and has the same data volume as the dual-linear polarimetric HRRP, thus it is significant to investigate the capability of DCP HRRP for target recognition. In this paper, a novel feature extraction method is proposed for the DCP HRRP-based target recognition. First, due to the good capability of characterizing the target polarimetric structure, the m-χ decomposition is exploited to obtain three scattering components of targets corresponding to the odd-bounce, even-bounce and randomly polarized scattering mechanisms along the radar line-of-sight (LOS), respectively. However, these three scattering components are infeasible to be directly utilized for target recognition due to the high dimensionality and redundancy. Considering this, a novel feature fusion method named as sparsity-preserving discriminant correlation analysis (SPDCA) is proposed to fuse the three scattering components. The SPDCA method can obtain the low-dimensional projection feature with good class separability by preserving both the global structure and local sparsity property of the original data. Besides, the redundancy of the three scattering components is eliminated by the SPDCA method. The results of experiments conducted on the simulated data of 10 civilian vehicles and real data of 3 military vehicles demonstrate the effectiveness and robustness of the proposed feature extraction method.

Compressed sensing (CS) based imaging technology has attracted a lot of interest because it can enhance imaging resolution. Targets of interest for forward-looking imaging radar are typically few in comparison to the entire imaging region. This sparsity allows for the natural application of CS to the reconstruction of high-resolution forward-looking images. However, conventional CS-based imaging methods can only perform well when the signal-to-noise ratio (SNR) is high. Strong noise in radar imaging prevents CS-based methods from producing excellent imaging results. Inspired by the low-rank propertity of the received radar target echo and the sparsity of the forward-looking image targets, we present a combined low-rank and sparse prior restricted model for forward-looking imaging with a multi-channel array radar to overcome strong noise. In order to solve the low-rank joint sparse double prior constraint optimization problem, an augmented Lagrange multiplier (ALM) reconstruction method under the framework of the alternating direction multiplier method (ADMM) is proposed. Finally, the results of simulation and real measurement data indicate that our presented method is fairly effective at enhancing the azimuth resolution and robustness of forward-looking radar imaging in comparison to other current methods.

For inverse synthetic aperture radar (ISAR) imaging of a ship target moving with ocean waves, the image constructed with the standard range-Doppler (RD) technique is blurred and the range-instantaneous-Doppler (RID) technique has to be used to improve the image quality. In this paper, azimuth echoes in a range cell of the ship target are modeled as noisy multicomponent cubic phase signals (CPSs) after the motion compensation and a RID ISAR imaging algorithm is proposed based on the integrated cubic phase bilinear autocorrelation function (ICPBAF). The ICPBAF is bilinear and based on the two-dimensionally coherent energy accumulation. Compared to five other estimation algorithms, the ICPBAF can acquire higher cross term suppression and anti-noise performance with a reasonable computational cost. Through simulations and analyses with the synthetic model and real radar data, we verify the effectiveness of the ICPBAF and corresponding RID ISAR imaging algorithm.