Inshore ship detection is an important research direction of synthetic aperture radar (SAR) images. Due to the effects of speckle noise, land clutters and low signal-to-noise ratio, it is still challenging to achieve effective detection of inshore ships. To solve these issues, an inshore ship detection method based on the level set method and visual saliency is proposed in this paper. First, the image is fast initialized through down-sampling. Second, saliency map is calculated by improved local contrast measure (ILCM). Third, an improved level set method based on saliency map is proposed. The saliency map has a higher signal-to-noise ratio and the local level set method can effectively segment images with intensity inhomogeneity. In this way, the improved level set method has a better segmentation result. Then, candidate targets are obtained after the adaptive threshold. Finally, discrimination is employed to get the final result of ship targets. The experiments on a number of SAR images demonstrate that the proposed method can detect ship targets with reasonable accuracy and integrity.

Superpixel methods are widely used in the processing of synthetic aperture radar (SAR) images. In recent years, a number of superpixel algorithms for SAR images have been proposed, and have achieved acceptable results despite the inherent speckle noise of SAR images. However, it is still difficult for existing algorithms to obtain satisfactory results in the inhomogeneous edge and texture areas. To overcome those problems, we propose a superpixel generating method based on pixel saliency difference and spatial distance for SAR images in this article. Firstly, a saliency map is calculated based on the Gaussian kernel function weighted local contrast measure, which can not only effectively suppress the speckle noise, but also enhance the fuzzy edges and regions with intensity inhomogeneity. Secondly, superpixels are generated by the local k-means clustering method based on the proposed distance measure, which can efficiently sort pixels to different clusters. In this step, the distance measure is calculated by combining the saliency difference and spatial distance with a proposed adaptive local compactness parameter. Thirdly, post-processing is utilized to clean up small segments. The evaluation experiments on the simulated SAR image demonstrate that our proposed method dramatically outperforms four state-of-the-art methods in terms of boundary recall, under-segmentation error, and achievable segmentation accuracy under almost all of the experimental parameters at a moderate segment speed. The experiments on real-world SAR images of different sceneries validate the superiority of our method. The superpixel results of the proposed method adhere well to the contour of targets, and correctly reflect the boundaries of texture details for the inhomogeneous regions.

Monopulse radar is widely used in military. Jamming monopulse radar has always been a research hotspot in electronic warfare (EW). Cross-eye jamming has always been considered as the most effective measures to jam with monopulse radar. In this paper, we propose a multi-group three-tuple cross-eye jamming structure where each group contains three antenna elements with a definite phase and an amplitude relationship. Then, based on the principle of monopulse angle measurement, the error angle is deduced theoretically. Simulations show that such a multi-group three-tuple cross-eye jamming structure performs better than the multi-element cross-eye jamming structure previously proposed, and the analysis of the centroid shows that the centroid of the structure proposed in this paper is more widely distributed in space.

In this letter, a segmentation method is proposed based on an adaptive Bayesian non-local MRF (ABNL-MRF) model. Aimed at the multiplicative speckle existing in SAR images, a patch-similarity measure based on a ratio of probability is introduced firstly. Then non-local similar pixel-blocks are adopted to guide the segmentation of the noisy image. However, non-local method over-smoothes edge regions and makes them inaccurate though it is robust for speckle. A rectification index is designed according to the homogeneity degree of local window. Based on the value of rectification index, a non-local constraint term is adaptively integrated into the potential function. Thus a suitable prior is defined that guarantees both the smoothness of the denoised image and the preservation of its structure. Experimental results demonstrate the very good performance of the proposed method.

A planar ultra-wideband modular antenna array with an ultra-low profile based on a tight coupling effect is proposed. The antenna array is composed of multi-layer printed circuit boards. Since its frequency operation can extend up to the grading lobe frequency, the number of T/R components for the antenna aperture is reduced to the greatest extent. Specifically, a horizontal “fin” structure is added on the parallel feeding lines, which assists in improving the impedance matching of the array. In order to break the bandwidth limitation of the low-frequency loop mode, a capacitive short-circuit probe is introduced to push the common-mode resonance point into the low-frequency band. Finally, subject to Active VSWR < 3, the array can realize E-plane, H-plane, and D-plane ± 45° beam scan coverage across the 6:1 frequency band (3.48–20.97 GHz). A 128-element prototype was processed and tested to validate the design. This array not only has the advantages of low profile, modularity, easy assembly, and maintenance but also minimizes the number of T/R components and reduces the cost of phased array antennas.