This paper describes the design and realization of a novel Ka/W dual-band shared-aperture horn antenna employing the concept of mutual enhancement. The W-band antenna cells within the Ka-band antenna structure enhance the aperture utilization efficiency. The proposed design enables gain enhancement in the Ka-band horn antenna through incorporation of W-band horn antenna cells, while the W-band array antenna achieves low sidelobe levels by adopting a sparse array configuration. The W-band antenna consists of a radiation sparse array, an irregular interconnected waveguide-based network, and a power divider network. A mutually enhanced Ka/W dual-band shared-aperture horn antenna is fabricated and evaluated. The results reveal a gain improvement of 1.5 dB at 35 GHz compared to the original horn antenna. Moreover, the measured |S11|≤-10 dB bandwidth of the W-band array antenna spans over 16 GHz (ranging from 92 GHz to 108 GHz). At the central frequency, the maximal gain of the radiation pattern is approximately 20.8 dBi, with low sidelobe levels (in both the E-plane and the H-plane) of less than -19.0 dB.

One of the radar high‐resolution range profile (HRRP) recognition issues is the target‐aspect sensitivity. Both theoretical analysis and real‐world data show that the HRRP shows a high correlation only in a very small aspect region. To overcome this problem, in traditional methods, the scattering centre model and averaged range profile are utilised. In this study, the authors present a graph‐based semi‐supervised method, called geodesic weighted sparse representation (GWSR), to overcome the target‐aspect sensitivity problem. It is assumed that HRRP from different targets is located on different manifolds and the correlation information is utilised to separate these manifolds. In GWSR, the geodesic distance is calculated firstly and then the labelled HRRP is reconstructed by the geodesic weight. The nonlinear structure of HRRP can be transformed into a linear one through the reconstruction process. Then, the unlabelled HRRP is sparsely reconstructed and the sparse reconstruction weight can be utilised to estimate the label of the unlabelled HRRP from the given labels. Experiments on three kinds of ground target HRRPs with different backgrounds demonstrate the effectiveness of the authors’ method.

The classification algorithms of polarimetric synthetic aperture radar (PolSAR) images are generally composed of the feature extractors that transform the raw data into discriminative representations, followed by trainable classifiers. Traditional approaches always suffer from the handdesigned features and misclassification of boundary pixels. Following the great success of convolutional neural network (CNN), a novel data-driven classification framework based on the fusion of CNN and superpixel algorithm is presented in this paper. First, the region-based complex-valued network utilizes both the intensity and phase information to predict the label of each pixel and constructs the label map based on spatial relations. Second, superpixel generating algorithm is adopted to produce the superpixel representation of the Pauli decomposition image, and the contour information which reflects the boundary of each category is preserved. Finally, the original label map and contour information are fused to make the decision of each pixel, outputting the final label map. Experimental results on public datasets illustrate that the proposed method can automatically learn the intrinsic features from the PolSAR image for classification purpose. Besides, the fusion of the superpixel features can effectively correct the misclassification of the boundary and singular pixels, thus achieving superior performance.

High‐resolution millimetre wave images of contiguous targets often suffer from the influence of sidelobe artefacts, partial correlation between targets and so on. Owing to the characteristics of near‐field synthetic aperture imaging radiometers [such as the changing point spread function (PSF) and slender sideline], the existing CLEAN algorithms are unsuitable for near‐field synthetic aperture imaging. This study is devoted to establishing a novel CLEAN algorithm (named adaptive CLEAN) to clean the reconstructed millimetre wave images accurately. First, the characteristics of near‐field synthetic aperture imaging are studied. Then, the adaptive CLEAN algorithm is established based on these characteristics. In this study, the authors amend the amplitude of the targets and select the matching PSF for them according to their azimuths. Unlike other CLEAN algorithms, the parameters of this adaptive CLEAN algorithm are calculated by a formula, which is concluded from a lot of simulation experiments. Finally, the effectiveness of the proposed adaptive CLEAN algorithm is tested by several simulation experiments, and the superiority is also demonstrated by comparing it with the existing CLEAN algorithm. The results demonstrate that the proposed method is an efficient, feasible algorithm for cleaning the reconstructed images, irrespective of the point or contiguous targets.