Ship wake detection using synthetic aperture radar (SAR) imagery provides a way to obtain small marine ship information, but it often becomes unavailable and unreliable during a high sea state. Polarimetric information provides a potential way to solve this problem, which can enhance the ship target as well as the ship wake features. However, three challenges still exist in ship wake detection in polarimetric SAR imagery: the unwanted influences of bright and singular points on ship wake detection, the lack of performance analysis of wake detection by new-type polarimetric enhancement methods, and the difficulty of using the assessment criteria for ship wake detection. In this paper, we try to solve the above problems. Firstly, fully polarized SAR imagery of both ship turbulent and Kelvin wake is simulated based on the two-scale composite model, and the Polarimetric Whitening Filter (PWF) and Polarimetric Detection Optimization Filter (PDOF) are applied to the simulated fully polarized SAR imagery to enhance the ship wake features. Secondly, since the bright and singular points resulting from the ship echoes and the polarimetric enhancement methods may lead to misdetections, a logarithm process and z-score normalization pre-processing has been applied to the images. Then, a new assessment criterion for wake detection performance has been formulated, and the probability of missing detections (PMDs) and the probability of false alarms (PFAs) have been defined for two different requirements. And a Radon transform-based ship wake detection method for both ship turbulence and Kelvin wake has been carried out in horizontal–horizontal (HH), vertical–vertical (VV), horizontal–vertical (HV), PWF and PDOF SAR imagery. Finally, an analysis of the wake detection performance has been carried out. The PWF and PDOF can improve the wake detection performance by an average of nearly 50 percent compared with the HH and VV.

This work proposed a novel constant false alarm rate detection method based on Gaussian mixture model (GMM). The main contribution contains two aspects. First, the Gabor wavelet is used to determine the GMM component number. Second, the expression of probability of false alarm is derived based on the GMM parameter.

Synthetic aperture radar (SAR) imaging has important application potential in sea environments research, such as submarine detection. It has become one of the most significant research topics in the current SAR imaging field. In order to promote the development and application of SAR imaging technology, a MiniSAR experiment system is designed and developed, which provides a platform for related technology investigation and verification. A flight experiment is then conducted to detect the movement of an unmanned underwater vehicle (UUV) through the wake, which can be captured by SAR. This paper introduces the basic structure and the performance of the experimental system. The key technologies for Doppler frequency estimation and motion compensation, the implementation of the flight experiment, and the image data processing results are given. The imaging performances are evaluated, and the imaging capabilities of the system are verified. The system provides a good experimental verification platform to construct the follow-up SAR imaging dataset of UUV wake and investigate related digital signal processing algorithms.