Imaging Scheme of Joint Processing of Boundary Array Based on Fast Convolution

When using boundary multiple-input–multiple-output (MIMO) arrays for large-scale imaging, the requirement for a large number of array elements leads to high cost. To tackle this challenge, this article presents a novel joint signal-processing framework for boundary array (BA) configurations in near-field millimeter-wave (MMW) imaging systems. By jointly processing the transmit–receive array data of $3\times 3$ adjacent BA elements, the imaging performance equivalent to that of a $5\times 5$ BA is achieved, significantly reducing the number of required elements and improving imaging efficiency. A fast convolution algorithm (FCA) based on the fast Fourier transform (FFT) is proposed to enable fast imaging, which avoids plane-wave approximation and enhances imaging accuracy. To adapt to the joint processing of transmit–receive elements between adjacent BAs, subscenes data correction rules are established by analyzing the distance differences between reference points and other scattering points relative to antenna elements, and experimental verification was conducted. The experimental results demonstrate that the resolutions achieved with the joint FCA and range migration algorithm (RMA) processing are 3.18 and 3.37 mm, respectively, exhibiting no significant degradation compared to the full array resolutions of 2.98 and 3.31 mm. In the experiments, the root-mean-square error (RMSE) for the joint-processed steel plate imaging result is approximately −24 dB, compared to only −13 dB for the nonjoint processing. For human body imaging, joint processing significantly improves the presentation of fine details. Furthermore, the efficiency of the spatial single-plane search for the proposed methodology is approximately three orders of magnitude superior to that of the back-projection algorithm (BPA), ensuring both imaging speed and accuracy while substantially reducing hardware costs.