Millimeter-wave (MMW) imaging scanners can see through clothing to form a three-dimensional holographic image of the human body and suspicious objects, providing a harmless alternative for non-contacting searches in security check. Suspicious object detection in MMW images is challenging, since most of them are small, reflection-weak, shape, and reflection-diverse. Conventional detectors with artificial neural networks, like convolution neural network (CNN), usually take the problem of finding suspicious objects as an object recognition task, yielding difficulties in developing large-amount and complete sample sets of objects. In this paper, a new algorithm is developed using the human pose segmentation followed by the deep CNN detection. The algorithm is emphasized to learn the similarity with humans’ body clutter applied to training corresponding CNNs after the image segmentation base of the pose estimation. Moreover, the suspicious object recognition in the MMW image is converted to a binary classification task. Instead of recognizing all sorts of suspicious objects, the CNN detector determines whether the body part images present the abnormal patterns containing suspicious objects. The proposed algorithm that is based on CNN with the pose segmentation has concise configuration, but optimal performance in the suspicious object detection. Extensive experiments confirm the effectiveness and superiority of the proposal.

Forward-looking multi-channel synthetic aperture radar (FLMC-SAR) has the capability to generate unambiguous two-dimensional images in the forward-looking direction. In contrast to traditional SAR systems, phase error compensation for FLMC-SAR imaging encounters the following two challenges. On one hand, the array errors caused by the three-dimensional attitude angles introduce time-varying phase errors, adding complexity to the compensation. On the other hand, the spatial-domain resources used to resolve Doppler ambiguity affect time-domain SAR imaging, resulting in space-time coupling. In this paper, a pulse-by-pulse Doppler ambiguity resolving algorithm for FLMC-SAR imaging based on fast factorized back-projection is proposed to achieve the phase errors compensation. In the proposed method, the processing of imaging and resolving Doppler ambiguity over the full synthetic aperture is decomposed into several pulse-by-pulse processing. Within this pulse-by-pulse processing framework, we modified the Back Projection (BP) integral function after incorporating motion compensation and array error compensation, ultimately achieving unambiguous FLMC-SAR imaging results through the processing of "space-time combination." Additionally, the fast factorized back-projection (FFBP) framework is utilized to accelerate the proposed FLMC-SAR imaging and Doppler ambiguity resolving. Extensive simulations and real-data experiments confirm that the proposed algorithm is capable of addressing time-varying phase errors and achieving unambiguous FLMC-SAR imaging results.

Radio frequency interference (RFI) has become a serious problem for synthetic aperture radar (SAR) systems. In recent years, numerous methods have emerged for the suppression of radio frequency interference (RFI). These methods often rely on assumptions regarding the narrowband characteristics and low-rank nature of radar echoes or interference signals. However, these assumptions may not hold true when dealing with complex RFI. To mitigate complex interference in dual-polarization (dual-pol) synthetic aperture radar (SAR) systems, a proposed method is the utilization of a filtering algorithm based on sidelobe cancellation (SLC) technology. This method leverages the discrepancies in gain and coherence between observed targets and RFI signals in the co-polarization (co-pol) and cross-polarization (cross-pol) channels. It suppresses interference through SLC filtering on a range line by range line basis in the focused image domain, serving as a post-processing step. The method is capable of tackling multiple complex interferences optimally under the least mean square criterion, such as narrowband, wideband, and noise-modulated pulsed interferences. Both simulation and real data experiments show that the method can remove RFI artifacts and effectively recover the desired SAR images.

For widefield synthetic aperture radar (SAR) imag ing, range-dependent motion errors and range curvature are the main obstacle for range cell migration correction (RCMC) and azimuth focusing. The parametric polar format algorithm (PPFA) can handle the range-dependent motion errors after RCMC, but the Non-system range cell migration (NsRCM) is ignored. Furthermore, the residual range curvature is neglected, causing the undesired range cell migration. In this paper, a novel widefield PPFA (WPPFA) is developed for widefield spotlight SAR imaging. A precise range-compressed domain motion compensation (RCMOCO) algorithm is introduced for the range-dependent motion errors. It can precisely compensate for the range-dependent motion errors before RCMC. After RCMC, an accurate range curvature correction algorithm is carried out, accelerated by the scaling Fourier transform (SFT). Based on these improvements, an azimuth wavenumber resampling integrating azimuth-variant phase error compensation is implemented to achieve well-focused images. Finally, extensive simulated and real-measured SAR data have validated the algorithm's effectiveness.

High-squint and wide-beam Synthetic Aperture Radar (SAR) imaging is challenging for current popular SAR imaging algorithms because the severe spatial-variant motion error precludes the precise Motion Compensation (MOCO). Especially the azimuth-variant motion error (AVME) would bring not only the azimuth-variant phase error but the non-negligible Nonsystemic Range Cell Migration (NsRCM). This paper proposes a novel SAR imaging algorithm to deal with the NsRCM and the azimuth-variant phase error in high-squint and wide-beam SAR imaging. For Range Cell Migration Correction (RCMC), a Parametric Keystone Transform Algorithm (PKTA) introducing the three-axis trajectory deviations as parameters is developed. It can correct the nominal RCM and the NsRCM through a time-variant scaling transform along slow time. The robust RCMC paves the way to precisely compensate for the azimuth-variant phase error. Following, a fast and precise subaperture MOCO algorithm, which applies the Recursive Discrete Fourier Transform (RDFT), is embedded in the azimuth focus procedure to adjust the azimuth-variant phase error. The proposed algorithm can handle the high-squint and wide-beam SAR data with severe motion errors based on these improvements. Finally, extensive experiments with simulated and real-measured SAR data demonstrate the proposal’s superiority and robustness.

The existing Ground Cartesian Back-Projection (GCBP) algorithm is limited by its low effectiveness in spectral compression, which makes it unsuitable for processing high-squint and large-scale strip-map Synthetic Aperture Radar (SAR) data. To address this issue, we propose a novel algorithm called Ground Squint Fast Back Projection (GSFBP) for high-squint strip-map SAR imaging. First, we introduce a Ground Squint Coordinate (GSC) system that replaces the conventional Ground Cartesian Coordinate (GCC) system. The unique geometry of the GSC allows for precise rotation of the two-dimensional wavenumber spectrum without the need for auxiliary operations, significantly easing the constraints related to scene size.Moreover, the GSC framework facilitates seamless sub-image fusion through translation, eliminating the need for interpolation. Building on the original two-step spectral compression method, we have developed a GSC-specific relative spectrum inclination correction function to enhance spectral compression effectiveness. These innovations enable GSFBP to effectively manage large-scale scenes in high-squint SAR imaging. Experimental validation, using both simulated and real measured SAR data, confirms the superiority of the proposed algorithm.