A W‐band dual‐band waveguide band‐pass filter composed of two dual‐mode (TE ₁₀₁ and TE ₂₀₁ ) cavities and four single‐mode (TE ₁₀₁ ) ones is presented. This dual‐band filter with each passband of fourth‐order Chebyshev response is synthesised and designed with the centre frequencies 85.35 and 101.45 GHz, as well as the 3 dB fractional bandwidths of 5.3 and 2.1% by adopting the dual‐mode resonators and the controllable coupling technique. The prototype is fabricated by the conventional computer numerical control milling technology easily. The measured results are very close to the simulations in full W‐band.

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.

For near-field Synthetic Aperture Interferometric Radiometer (SAIR) systems, imaging distance plays an important role in the imaging reconstruction. However, most current SAIR imaging algorithms treat the imaging distance as an exact known value, which may have a serious influence on imaging reconstruction quality caused by the misestimate of the practical imaging distance. In this letter, we propose a distance adaptive near-field SAIR imaging algorithm based on deep learning approach to handle this problem. The Complex Correlation Operation Block (CCOB) is proposed to predict the distance parameter and reconstruct the image firstly. And the residual attention enhanced module is utilized to further improve the imaging quality. The simulation results indicate that the proposed algorithm can accurately predict the imaging distance and achieve high-precision image reconstruction at different distances.