It is difficult for multichannel maneuvering synthetic aperture radar (SAR) to achieve ground moving target 2D velocity estimation and refocusing. In this paper, a novel method based on back projection (BP) and velocity SAR (VSAR) is proposed to cope with the issues. First, the static scene is reconstructed by BP to solve the imaging problem of multichannel maneuvering SAR. Then, the static clutter is suppressed, and the range velocity is estimated via VSAR processing. As for azimuth velocity estimation and refocusing, a velocity search method based on velocity-aided BP (VA-BP) and VSAR is proposed to accomplish them. First, each azimuth velocity in the search and the estimated range velocity are used to image the moving target in a small-sized subimage space by VA-BP, i.e., matching the range history and the Doppler phase of the moving target in the image processing. Then, multiple sets of multichannel SAR subimages corresponding to different azimuth velocities are generated, and the clutter of each set of multichannel SAR subimages is also suppressed by VSAR processing. After that, the azimuth velocity is estimated by searching the clutter-suppressed subimage of the first spatial receiving channel in each set of multichannel SAR subimages with the best refocusing quality measured by the minimum entropy. Simulation results show the proposed method can reach high accuracy in moving target 2D velocity estimation and refocusing with the absolute error of 2D velocity estimation smaller than 0.1 m/s.

Compressed sensing (CS) algorithms are used for linear array synthetic aperture radar (LASAR) three-dimensional (3D) imaging. However, it is difficult to obtain imaging results with both high computational efficiency and promising imaging quality. Because of the high-dimensional matrix-operations, the computational complexity of several CS algorithms is huge such as the iterative adaptive approach (IAA), bayesian compressed sensing (BCS), and sparsity bayesian recovery via iterative minimum (SBRIM) algorithm. Besides, the greedy pursuit algorithms such as the orthogonal matching pursuit (OMP) algorithm cannot acquire ideal imaging results on account of the preset sparsity of the imaging scene. To solve the problem, we present a fast sparse recovery algorithm via resolution approximation (FSRARA) in this paper. Firstly, the whole imaging scene is divided into 3D scattering units with large spacing, and SBRIM algorithm is used to obtain its low-resolution imaging results quickly. Secondly, the low-resolution imaging results are conducted image segmentation by the fuzzy c-means (FCM) clustering algorithm to extract the possible targets' areas coarsely. Then we re-divide the imaging scene by higher imaging resolution and extract the possible targets' areas according to the coarsely possible targets' areas. FSRARA achieves improved computational efficiency with low-dimensional matrix-operations on the possible targets' areas instead of the high-dimensional one on the whole imaging scene. Meanwhile, FSRARA performs better in suppressing the false targets and sidelobe interference and improves the imaging quality than the SBRIM algorithm. Simulation and experimental results prove that FSRARA improves the computational efficiency by hundreds of times at most than SBRIM algorithm and its computational efficiency is higher than smoothed $L_{0} $ norm (SL0), IAA, and BCS algorithm. Besides, FSRARA improves the imaging quality compared with OMP, IAA, SL0, BCS, and SBRIM algorithms.

Recently, plenty of millimeter-wave image concealed object detection models have achieved superior performance on benchmark datasets. The success of these models heavily depends on a large amount of fully-annotated data. However, noisy-label data often exist in a large-scale dataset, which deteriorate the performance of models. In this paper, we delve into the training of the concealed object detector with label noise, where we recognize divergent types of noisy annotations. To tackle the multi-type label noise problem, we propose a unified framework including two stages: the label noise modeling and the correction training. We design the region-level label noise modeling, which analyzes and models label noise via the classification branch loss curves. To correct the labels during training, we design the uncertainty-weighted loss for label correction. The experiments on a millimeter-wave security image dataset demonstrate the effectiveness of our proposed training framework, which can alleviate the negative influence of noisy annotations on network training. By introducing our framework, the object detection network can achieve a prominent performance improvement on mean average precision (0.176 on average) compared to the one trained with noisy labels.

In order to suppress the periodic narrowband interference in partial discharge (PD) online measurement, this paper presents a novel de-noising method based on singular value reconstruction. Firstly, the Hankel matrix is constructed by the PD sample sequence and singular value decomposition (SVD) is performed on it. Then the singular values corresponding to the narrowband interference are adaptively extracted by the K-means algorithm. Finally, the narrowband interference is reconstructed and removed from the original sample sequence to obtain the PD pulses. The de-noised results of the simulated and measured signals show that: Compared with the FFT threshold method and the wavelet de-noising method, the proposed method has better suppression effect on the narrowband interference; The de-noised PD waveform of the proposed method is more similar to the original PD pulses, which is more conducive to the subsequent analysis of the PD pulses.

Due to the spectrum of a moving target being almost submerged in ground clutter, it is difficult for high-speed platform synthetic aperture radar to indicate a slowly moving target by traditional methods. To solve this problem, a detection and azimuth velocity estimation method is proposed based on along-track dual-beam synthetic aperture radar mode. Back projection is adopted to focus the forward- and backward-looking-beam images due to abilities such as automatic coregistration and geometric correction. The positions of the moving target in the two images are deduced, and we show that there is an azimuth location offset for the moving target in the forward-and-backward-looking images proportional to its azimuth velocity, which can be used to detect the moving target and estimate its azimuth velocity. Furthermore, a refocusing method is proposed to obtain a more accurate estimation. Simulation results show that the proposed method can detect slowly moving targets, especially those only with an azimuth velocity, which is difficult for many existing methods. In addition, the accuracy of azimuth velocity estimation can reach 0.01 m / s when the signal-to-clutter ratio is greater than 5 dB under simulation conditions.

Due to the reduction of imaging accuracy caused by the approximations of signal models, traditional synthetic aperture radar(SAR) moving target motion parameter estimation methods based on frequency domain imaging algorithms may suffer from the problem of accuracy reduction. To solve this problem, a novel moving target motion parameter estimation scheme is proposed for dual-channel SAR based on the time domain back projection(BP) algorithm. First, the BP imaging model of a moving target is constructed for the dual-channel SAR, and the focus position and the phase response of the moving target are analyzed. We show that the radial velocity of the moving target is proportional to the center frequency of the azimuth wavenumber spectrum, which can be used to estimate the radial velocity. Afterwards, the displaced phase center antenna based on BP is deduced for the clutter suppression, and the constant false alarm rate detector is used to detect moving targets. Then, the azimuth offset of the moving target between the two sub-aperture images is used to estimate the azimuth velocity, since it is proportional to the azimuth velocity. Meanwhile, a modified refocussing method is applied for a more accurate azimuth velocity estimation. Furthermore, the slant-range velocity is calculated by the geometric relationship among the radial velocity, azimuth velocity, and the slant-range velocity. The simulation and semi-physical simulation experiments verify that the proposed scheme can achieve higher accuracy in motion parameter estimation than the method based on the frequency domain imaging algorithm in both the side-looking and squint-looking dual-channel SAR.