Deep learning (DL)-based precipitation nowcasting algorithms have garnered significant attention in recent years. However, the presence of variable spatial scales in precipitation patterns poses challenges for methods that solely focus on capturing spatiotemporal correlations at a single scale. Moreover, current DL-based algorithms tend to model short-term (e.g., 10-min time span) rainfall locally neglecting long-term, global (e.g., 2-h time span) life-cycle evolution. Furthermore, widely used pixel-wise losses are prone to produce low effective-spatial-resolution predictions. To this end, we introduce a multiscale deformable transformer network to leverage echo contexts from image patches of varying spatial scales. Meanwhile, a multihead deformable self-attention mechanism is introduced for capturing precipitation spatiotemporal dynamics in a global manner. Moreover, to improve the spatial resolution of predictions, the Fourier space regularization and adversarial losses are proposed by narrowing the discrepancy of the Fourier spectra of predictions and references. Thanks to the introduced loss function, our model generates highly effective spatial-resolution predictions with abundant details. Extensive experiments on two real datasets show the substantial superiority of our method in terms of critical success index (CSI) compared to recent competitive approaches. At the same time, our predictions have more realistic precipitation details and significantly better fidelity. For example, on a vertically integrated liquid (VIL) product dataset, compared to baseline methods, our approach reduces the Fréchet inception distance (FID) value by a factor of$2\sim 4$while improves the CSI score by 3%~5% approximately.

As one of the crucial sensors for environment sensing, frequency modulated continuous wave (FMCW) radars are widely used in modern vehicles for driving assistance/autonomous driving. However, the limited frequency bandwidth and the increasing number of equipped radar sensors would inevitably cause mutual interference, degrading target detection and producing safety hazards. In this paper, a deep learning-based interference mitigation (IM) approach is proposed for FMCW radars by using the dilated convolution for network construction and a designated contrast learning strategy for training. The dilated convolution enlarges the receptive field of the neural network, and the designated contrastive learning strategy enforces to distinguish better between interferences and desired signals. The results of numerical simulation and experimental data processing show that the dilated convolution-based IM network, compared to the traditional convolution-based ones, can achieve a higher Signal-to-Interference-plus-Noise-Ratio (SINR) and target detection rate. Moreover, the designated contrastive learning strategy enables a better and more stable IM performance without increasing the complexity of the network, which can facilitate faster signal processing.

In this paper, the interference mitigation problem is tackled as a regression problem. A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. Considering the complex-valued nature of radar signals, complex-valued convolutional neural network, which is different from the conventional real-valued counterparts, is utilized as an architecture for implementation. Meanwhile, as the desired beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared with the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. The average SINR of interfered signals has been improved from -9.13 dB to 10.46 dB. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.

Nowadays deep learning based weather radar echo extrapolation methods have competently improved nowcasting quality. Current pure convolutional or convolutional recurrent neural network based extrapolation pipelines inherently struggle in capturing both global and local spatiotemporal interactions simultaneously, thereby limiting nowcasting performances, e.g., they not only tend to underestimate heavy rainfalls’ spatial coverage and intensity but also fail to precisely predict non-linear motion patterns. Furthermore, the usually adopted pixel-wise objective functions lead to blurry predictions. To this end, we propose a novel motion-guided global-local aggregation Transformer network for effectively combining spatiotemporal cues at different time scales, thereby strengthening global-local spatiotemporal aggregation urgently required by the extrapolation task. First, we divide existing observations into both short and long term sequences to represent echo dynamics at different time scales. Then, to introduce reasonable motion guidance to Transformer, we customize an end-to-end module for jointly extracting Motion Representation of Short and Long term echo sequences (MRS, MRL), while estimating optical flow. Subsequently, based on Transformer architecture, MRS is used as queries to retrospect the most useful information from MRL for an effective aggregation of global long-term and local short-term cues. Finally, the fused feature is employed for future echo prediction. Additionally, for the blurry prediction problem, predictions from our model trained with an adversarial regularization achieve superior performances not only in nowcasting skill scores but also in precipitation details and image clarity over existing methods. Extensive experiments on two challenging radar echo datasets demonstrate the effectiveness of our proposed method.

Recently, frequency-modulated continuous-wave (FMCW) radar-based hand gesture recognition (HGR) using deep learning has achieved favorable performance. However, many existing methods use extracted features separately, i.e., using one of the range, Doppler, azimuth, or elevation angle information, or a combination of any two, to train convolutional neural networks (CNNs), which ignore the interrelation among the 5-D time-varying-range-Doppler-azimuth-elevation feature space. Although there have been methods using the 5-D information, their mining of the interrelation among the 5-D feature space is not sufficient, and there is still room for improvements. This article proposes a new processing scheme of HGR based on 5-D feature cubes that are jointly encoded by a 3-D fast Fourier transform (3-D-FFT)-based method. Then, a CNN is proposed by building two novel blocks, i.e., the spatiotemporal deformable convolution (STDC) block and the adaptive spatiotemporal context-aware convolution (ASTCAC) block. Concretely, STDC is designed to cope with hand gestures’ large spatiotemporal geometric transformations in the 5-D feature space. Moreover, ASTCAC is designed for modeling long-distance global relationships, e.g., relationships between pixels of the feature at the upper left corner and lower right corner, and exploring the global spatiotemporal context, in order to enhance the target feature representation and suppress interference. Finally, our presented method is verified on a large radar dataset, including 19 760 sets of 16 common hand gestures, collected by 19 subjects. Our method obtains a recognition rate of 99.53% on the validation dataset and that of 97.22% on the test dataset, which is significantly better than state-of-the-art methods.

In this paper, constant false alarm rate (CFAR) detector-based approaches are proposed for interference mitigation of Frequency modulated continuous wave (FMCW) radars. The proposed methods exploit the fact that after dechirping and low-pass filtering operations the targets’ beat signals of FMCW radars are composed of exponential sinusoidal components while interferences exhibit short chirp waves within a sweep. The spectra of interferences in the time-frequency ($t$-$f$) domain are detected by employing a 1-D CFAR detector along each frequency bin and then the detected map is dilated as a mask for interference suppression. The proposed approaches are applicable to the scenarios in the presence of multiple interferences. Compared to the existing methods, the proposed methods reduce the power loss of useful signals and are very computationally efficient. Their interference mitigation performances are demonstrated through both numerical simulations and experimental results.

3-D imaging with irregular planar multiple-input-multiple-output (MIMO) arrays is discussed. Due to signal acquisition on irregular spatial sampling grids by using these antenna arrays, the fast Fourier transform (FFT)-based imaging algorithms cannot readily be used for image formation. To avoid the application of computationally intensive coherent summation algorithms such as filtered backprojection or Kirchhoff migration, we propose a nonuniform FFT (NUFFT)-based MIMO Range Migration Algorithm (i.e., NUFFT-based MIMO-RMA) for efficient microwave imaging. The algorithm exploits NUFFT to reconstruct the wavenumber–domain spectra related to each Fourier frequency. It is generic and applicable to 3-D imaging with irregular planar MIMO arrays. The effects of irregular spatial sampling and signal bandwidth on the imaging performance and computational efficiency of the proposed algorithm are analyzed. Finally, some numerical simulations and experimental results are presented to demonstrate the performance of the proposed imaging algorithm.

Ground penetrating radar imaging from the data acquired with arbitrarily oriented dipole-like antennas is considered. To take into account variations of antenna orientations resulting in spatial rotation of antenna radiation patterns and polarizations of transmitted fields, the full-wave method that accounts for the near-, intermediate-, and far-field contributions to the radiation patterns is applied for image reconstruction, which is formulated as a linear inversion problem. Two approaches, namely, an interpolation-based method and a nonuniform fast Fourier transform-based method, are suggested to efficiently implement the full-wave method by computing exact Green’s functions. The effectiveness and accuracy of the method proposed have been verified via both numerical simulations and experimental measurements, and significant improvement of the reconstructed image quality compared with the traditional scalar-wave-based migration algorithms is demonstrated. The results can be directly utilized by forward-looking microwave imaging sensors such as installed at tunnel boring machine or can be used for the observation matrix computation in regularization-based inversion algorithms.

In this paper, a wavenumber-domain matrix-pencil-based multiband signal fusion approach was proposed for multiband microwave imaging. The approach proposed is based on the Born approximation of the field scattered from a target resulting in the fact that in a given scattering direction, the scattered field can be represented over the whole frequency band as a sum of the same number of contributions. Exploiting the measured multiband data and taking advantage of the parametric modeling for the signals in a radial direction, a unified signal model can be estimated for a large bandwidth in the wavenumber domain. It can be used to fuse the signals at different subbands by extrapolating the missing data in the frequency gaps between them or coherently integrating the overlaps between the adjacent subbands, thus synthesizing an equivalent wideband signal spectrum. Taking an inverse Fourier transform, the synthesized spectrum results in a focused image with improved resolution. Compared with the space–time domain fusion methods, the proposed approach is applicable for radar imaging with the signals collected by either collocated or noncollocated arrays in different frequency bands. Its effectiveness and accuracy are demonstrated through both numerical simulations and experimental imaging results.