The wingbeat frequency of insect migrant is regarded potentially valuable for species identification and has long drawn widespread attention in radar entomology. Principally, the radar echo signal can be used to extract wingbeat information, because both the signal amplitude and phase could be modulated by wing-beating. With respect to existing entomological radars, signal amplitude modulation has been used for wingbeat frequency measurement of large insects for many years, but the wingbeat frequency measurement of small insects remains a challenge. In our research, W-band and S-band coherent radars are used to measure the insect wingbeat frequency. The results show that the wingbeat-induced amplitude modulation of W-band radar is more intense than that of the S-band radar and the W-band radar could measure the wingbeat frequency of smaller insects. In addition, it is validated for the first time that the signal phase could also be used to measure the insect wingbeat frequency based on micro-Doppler effect. However, whether the wingbeat frequency measurement is based on the amplitude or phase modulation, it is found that the W-band coherent radar has better performance on both the measurement precision and the measurable minimum size of the insect.

Communal roosts are essential stopover sites for migratory birds. Monitoring the diurnal activity patterns of birds in communal roosts (DAPBCR) is crucial for understanding their migratory behavior and ecological needs. This information is crucial for guiding habitat conservation and management strategies and assessing the impact of environmental changes on bird populations. Traditional methods for extracting DAPBCR often rely on detecting high reflectivity factor arc features generated by birds collectively leaving or returning to communal roosts using weather radar data and deep learning target detection algorithms. However, many bird echoes do not produce these high reflectivity factor arc features, making pixel-level extraction of DAPBCR challenging. To address this, we propose a method for DAPBCR extraction based on the differences in the probability distribution function (PDF) of differential backscattering phase between birds and insects. This method first removes non-biological echoes from polarimetric weather radar data, retaining only biological echoes. By calculating the differential backscattering phase using the differential phase and system differential phase, we obtain the probability distribution function of the differential backscattering phase for biological echoes. We fit this PDF to a mixed Von-Mises distribution to obtain the PDFs for birds and insects. Using posterior probabilities for birds and insects, we estimate the bird-insect mixing ratio and further estimate the number of birds by combining the reflectivity factor and mean radar cross-section of birds. Applying the proposed method, we extracted DAPBCR data in the midsection of the Huai River Basin near Fuyang City from June to October 2021. We found that bird activity peaked in August and September. Based on normalized cumulative bird activity, we estimated the start, peak, and end times of DAPBCR to be July 6, August 19, and September 30, respectively.

Recent advances in deep learning have significantly improved the quality of precipitation nowcasting. Current approaches are either based on deterministic or generative models. Deterministic models perceive nowcasting as a spatiotemporal prediction task, relying on distance functions like L2-norm loss for training. While improving meteorological evaluation metrics, they inevitably produce blurry predictions with no reference value. In contrast, generative models aim to capture realistic precipitation distributions and generate nowcasting products by sampling within these distributions. However, designing a generative model that produces realistic samples satisfying meteorological evaluation indexes in real-time remains challenging, given the triple dilemma of generative learning: achieving high sample quality, mode coverage, and fast sampling simultaneously. Recently, diffusion models exhibit impressive sample quality but suffer from time-consuming sampling, severely hindering their application in nowcasting. Moreover, samples generated by the U-Net denoiser of current denoising diffusion model are prone to yield poor meteorological evaluation metrics such as CSI. To this end, we propose a spatiotemporal Transformer-based conditional diffusion model with rapid diffusion strategy. Concretely, we incorporate an adversarial mapping-based rapid diffusion strategy to overcome the time-consuming sampling process for standard diffusion models, enabling timely nowcasting. Additionally, a meticulously designed spatiotemporal Transformer-based denoiser is incorporated into diffusion models, remedying the defects in U-Net denoisers by estimating diffusion scores and improving nowcasting skill scores. Case studies of typical weather events such as thunderstorms, as well as quantitative indicators, demonstrate the effectiveness of the proposed method in generating sharper and more precise precipitation forecasts while maintaining satisfied meteorological evaluation metrics.

Multiple-Input Multiple-Output (MIMO) radar provides much more flexibility than the traditional radar thanks to its ability to realize far more observation channels than the actual number of transmit and receive (T/R) elements. In designing the MIMO imaging radar arrays, the commonly used virtual array theory generally assumes that all elements are on the same line. However, due to the physical size of the antennas and coupling effect between T/R elements, a certain height difference between T/R arrays is essential, which will result in the defocusing of edge points of the scene. On the other hand, the virtual array theory implies far-field approximation. Therefore, with a MIMO array designed by this theory, there will exist inevitable high grating lobes in the imaging results of near-field edge points of the scene. To tackle these problems, this paper derives the relationship between target’s point spread function (PSF) and pattern of T/R arrays, by which the design criterion is presented for near-field imaging MIMO arrays. Firstly, the proper height between T/R arrays is designed to focus the near-field edge points well. Secondly, the far-field array is modified to suppress the grating lobes in the near-field area. Finally, the validity of the proposed methods is verified by two simulations and an experiment.

Geosynchronous Synthetic Aperture Radar (GEO SAR) is a possible next generation SAR system, which has the excellent performance of less than one-day revisit and hundreds of kilometres coverage. However, Radio Frequency Interference (RFI) is a serious problem, because the specified primary allocation frequencies are shared by the increasing number of microwave devices. More seriously, as the high orbit of GEO SAR makes the system have a very large imaging swath, the RFI signals all over the illuminated continent will interfere and deteriorate the GEO SAR signal. Aimed at the RFI impact in GEO SAR case, this paper focuses on the performance evaluation and the system design requirement of GEO SAR in the presence of RFI impact. Under the RFI impact, Signal-to-Interference-plus-Noise Ratio (SINR) and the required power are theoretically deduced both for the ground RFI and the bistatic scattering RFI cases. Based on the theoretical analysis, performance evaluations of the GEO SAR design examples in the presence of RFI are conducted. The results show that higher RFI intensity and lower working frequency will make the GEO SAR have a higher power requirement for compensating the RFI impact. Moreover, specular RFI bistatic scattering will give rise to the extremely serious impact on GEO SAR, which needs incredible power requirements for compensations. At last, real RFI signal behaviours and statistical analyses based on the SMOS satellite, Beidou-2 navigation satellite and Sentinel-1 A data have been given in the ^appendix.

In radar automatic target recognition (RATR), high-resolution range profile (HRRP) has received intensive attention due to its low computational cost. As HRRP is sensitive to the aspect of the target, a training set covering sufficient aspects is essential to the success of an RATR model, which is however intractable in complex environment with noncooperative targets. In this article, an aspect-directed attention generative adversarial network is proposed to generate multiview HRRPs using real samples from few aspects. The key is that the HRRPs from the similar targets share the same aspect variation pattern. Hence, an HRRP is decomposed into its identity and aspect features via an aspect-directed disentangled representation network with self-attention modules. In the training stage, the decomposition network and the aspect variation pattern are learned from full aspect samples of cooperative targets. When generation, the desired multiview HRRPs of the noncooperative target are synthesized by its identity features extracted from few aspect samples and the learned aspect variation pattern. Three types of experiments on the simulated and measured datasets demonstrate the generation performances of our method. First, the generated HRRPs are visually compared with the truth. Second, the similarity of the scattering center power and handcrafted feature distributions are quantitatively evaluated. Finally, recognition experiments verify the feasibility of data augmentation with the generated HRRPs. Extensive results show the superior performance of our method over other state-of-the-art methods.