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21 pages, 30099 KiB

Open AccessArticle

Spectral De-Aliasing Method of Micro-Motion Signals Based on a Complex-Valued U-Net Network

by Ming Long, Jun Yang, Saiqiang Xia, Mingjiu Lv, Bolin Cheng and Wenfeng Chen

Remote Sens. 2023, 15(17), 4299; https://doi.org/10.3390/rs15174299 - 31 Aug 2023

Viewed by 670

Abstract

Spectrum aliasing occurs in signal echoes when the sampling frequency does not comply with the Nyquist Sampling Theorem. In this scenario, the extraction of micro-motion parameters becomes challenging. This paper proposes a spectral de-aliasing method for micro-motion signals based on a complex-valued U-Net [...] Read more.

Spectrum aliasing occurs in signal echoes when the sampling frequency does not comply with the Nyquist Sampling Theorem. In this scenario, the extraction of micro-motion parameters becomes challenging. This paper proposes a spectral de-aliasing method for micro-motion signals based on a complex-valued U-Net network. Zero interpolation is employed to insert zeros into the echo, effectively increasing the sampling frequency. After zero interpolation, the micro-motion signal contains both real micro-motion signal frequency components and new frequency components. Short-Time Fourier Transform (STFT) is then applied to transform the zero-interpolated echo from the time domain to the time–frequency domain. Furthermore, a complex-valued U-Net training model is utilized to eliminate redundant frequency components generated by zero interpolation, thereby achieving the frequency reconstruction of micro-motion signal echoes. Finally, the training models are employed to process the measured data. The theoretical analysis, simulations, and experimental results demonstrate that this method is robust and feasible, and is capable of addressing the problem of micro-motion signal echo spectrum aliasing in narrowband radar. Full article

(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)

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18 pages, 11121 KiB

Open AccessArticle

Multi-Scale Feature Residual Feedback Network for Super-Resolution Reconstruction of the Vertical Structure of the Radar Echo

by Xiangyu Fu, Qiangyu Zeng, Ming Zhu, Tao Zhang, Hao Wang, Qingqing Chen, Qiu Yu and Linlin Xie

Remote Sens. 2023, 15(14), 3676; https://doi.org/10.3390/rs15143676 - 23 Jul 2023

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Abstract

The vertical structure of radar echo is crucial for understanding complex microphysical processes of clouds and precipitation, and for providing essential data support for the study of low-level wind shear and turbulence formation, evolution, and dissipation. Therefore, finding methods to improve the vertical [...] Read more.

The vertical structure of radar echo is crucial for understanding complex microphysical processes of clouds and precipitation, and for providing essential data support for the study of low-level wind shear and turbulence formation, evolution, and dissipation. Therefore, finding methods to improve the vertical data resolution of the existing radar network is crucial. Existing algorithms for improving image resolution usually focus on increasing the width and height of images. However, improving the vertical data resolution of weather radar requires a focus on improving the elevation angle resolution while maintaining distance resolution. To address this challenge, we propose a network for super-resolution reconstruction of weather radar echo vertical structures. The network is based on a multi-scale residual feedback network (MR-FBN) and uses new multi-scale feature residual blocks (MSRB) to effectively extract and utilize data features at different scales. The feedback network gradually generates the final high-resolution vertical structure data. In addition, we propose an elevation upsampling layer (EUL) specifically for this task, replacing the traditional image subpixel convolution layer. Experimental results show that the proposed method can effectively improve the elevation angle resolution of weather radar echo vertical structure data, providing valuable help for atmospheric detection. Full article

(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)

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18 pages, 4264 KiB

Open AccessArticle

Analysis of the Three-Dimensional Structure of the Misocyclones Generating Waterspouts Observed by Phased Array Weather Radar: Case Study on 15 May 2017 in Okinawa Prefecture, Japan

by Ryusho Imai and Nobuhiro Takahashi

Remote Sens. 2022, 14(21), 5293; https://doi.org/10.3390/rs14215293 - 22 Oct 2022

Cited by 1 | Viewed by 1339

Abstract

Tornadoes are one of the most severe meteorological phenomena on the earth and their high winds cause serious damage to society. It is well known that vortices (mesocyclone or misocyclone, depending on their scale) in convective clouds contribute to tornadogenesis. High temporal resolution [...] Read more.

Tornadoes are one of the most severe meteorological phenomena on the earth and their high winds cause serious damage to society. It is well known that vortices (mesocyclone or misocyclone, depending on their scale) in convective clouds contribute to tornadogenesis. High temporal resolution radar observations are necessary to elucidate the mechanism of tornadogenesis because convective clouds change drastically over time. This study focused on waterspouts that occurred on 15 May 2017 near Okinawa, Japan. Using Phased Array Weather Radar (PAWR) data, which provide three-dimensional data with high temporal resolution (every 30 s), radar reflectivity factors and Doppler velocity data were used to detect the three-dimensional structure of vortices. Using PAWR data, vorticities and diameters of these misocyclones were detected every 30 s and their potential vorticities, which are only possible because of the three-dimensional observation by PAWR, were calculated to understand the vortex generation and advection. The structures of two misocyclones (MC1 and MC2) were detected from Doppler velocity patterns. Combined with the radar reflectivity analysis, MC2 can be divided into two misocyclones (MC2a and MC2b). Potential vorticity of MC1 increased with time, probably because an outflow from the strong echo enhanced the lower horizontal shear. Potential vorticities in MC2a and MC2b were conserved in each period, with MC2b being nearly twice as large as MC2a. Full article

(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)

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18 pages, 3422 KiB

Open AccessArticle

On the Problem of the Sea Ice Detection by Orbital Microwave Doppler Radar at the Nadir Sounding

by Vladimir Karaev, Yury Titchenko, Maria Panfilova, Kiril Ponur, Maria Ryabkova, Eugeny Meshkov and Dmitry Kovaldov

Remote Sens. 2022, 14(19), 4937; https://doi.org/10.3390/rs14194937 - 3 Oct 2022

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Abstract

Orbital radars are used to monitor the state of the sea ice in the Arctic and Antarctic. The backscattering radar cross section (RCS) is used to determine the type of scattering surface. The power of the reflected signal depends on many factors, so [...] Read more.

Orbital radars are used to monitor the state of the sea ice in the Arctic and Antarctic. The backscattering radar cross section (RCS) is used to determine the type of scattering surface. The power of the reflected signal depends on many factors, so the problem of separating sea ice and sea waves is not always unambiguous. Previous research has shown that microwave Doppler radar installed on aircrafts can be used to determine the boundary of sea ice. The width of the Doppler spectrum for wide or knife-like antenna beam depends on the statistical parameters of the reflecting surface, so sea ice and sea waves are easily separated. However, when installing a Doppler radar on a satellite, the spatial resolution becomes extremely low. In this research, we discuss the possibility of improving the spatial resolution by dividing the antenna footprint into elementary scattering cells. To do this, it is proposed to use the original incoherent synthesis procedure, which allows one to determine the dependence of the RCS on the incidence angle for an elementary scattering cell. Numerical modeling was performed and processing of model data confirmed that sea ice and sea waves are separated. The coefficient of kurtosis was used as a criterion in the algorithm. In addition, for sea waves, it is possible to determine the mean square slopes (mss) of large-scale waves, compared to the electromagnetic wavelength of sea waves along the sounding direction. Full article

(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)

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23 pages, 5496 KiB

Open AccessArticle

Application of the Doppler Spectrum of the Backscattering Microwave Signal for Monitoring of Ice Cover: A Theoretical View

by Vladimir Karaev, Yury Titchenko, Maria Panfilova, Maria Ryabkova, Eugeny Meshkov and Kirill Ponur

Remote Sens. 2022, 14(10), 2331; https://doi.org/10.3390/rs14102331 - 11 May 2022

Cited by 6 | Viewed by 1388

Abstract

In the radar remote sensing of sea ice, the main informative parameter is the backscattering radar cross section (RCS), which does not always make it possible to unambiguously determine the kind of scattering surface (ice/sea waves) and therefore leads to errors in estimating [...] Read more.

In the radar remote sensing of sea ice, the main informative parameter is the backscattering radar cross section (RCS), which does not always make it possible to unambiguously determine the kind of scattering surface (ice/sea waves) and therefore leads to errors in estimating the area of the ice cover. This paper provides a discussion of the possibility of using the Doppler spectrum of the reflected microwave signal to solve this problem. For the first time, a semi-empirical model of the Doppler spectrum of a radar microwave signal reflected by an ice cover was developed for a radar with a wide antenna beam mounted on a moving carrier at small incidence angles of electromagnetic waves (0°–19°). To describe the Doppler spectrum of the reflected microwave signal, the following parameters were used: shift and width of the Doppler spectrum, as well as skewness and kurtosis coefficients. Research was conducted on the influence of the main parameters of the measurement scheme (movement velocity, width of antenna beam, sounding direction, incidence angle) and the sea ice concentration (SIC) on the parameters of the Doppler spectrum. It was shown that, in order to determine the kind of scattering surface, it is necessary to use a wide or knife-like (by the incidence angle) antenna. Calculations confirmed the assumption that, when measured from a moving carrier, the Doppler spectrum is a reliable indicator of the transition from one kind of scattering surface to another. The advantage of using the coefficients of skewness and kurtosis in the analysis is that it is not necessary to keep the radar velocity unchanged during the measurement process. Full article

(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)

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19 pages, 14000 KiB

Open AccessCommunication

Low Observable Radar Target Detection Method within Sea Clutter Based on Correlation Estimation

by Zefeng Luo, Zhengzhou Li, Chao Zhang, Jiaqi Deng and Tianqi Qin

Remote Sens. 2022, 14(9), 2233; https://doi.org/10.3390/rs14092233 - 6 May 2022

Cited by 1 | Viewed by 1472

Abstract

The long-time coherent integration can effectively improve the detection ability of radar targets. However, this strategy usually shows poor effect in resisting the sea clutter, which produces difficulties for accurate estimation of sea clutter characteristics and results in the inability to differentiate between [...] Read more.

The long-time coherent integration can effectively improve the detection ability of radar targets. However, this strategy usually shows poor effect in resisting the sea clutter, which produces difficulties for accurate estimation of sea clutter characteristics and results in the inability to differentiate between the target and sea clutter. To solve this problem, a two-stage method is proposed, which consists of the sea clutter suppression stage and target decision stage. In the sea clutter suppression stage, the correlation time differences in the time and the space domains are adopted to estimate the correlation of sea clutter. Then, a selective whitening filter is proposed, which is performed more adaptively according to the estimation results. In the decision stage, the peak average ratio in the fractional Fourier domain (FRFT-PAR) is presented, which can make better use of the energy accumulation characteristics and further suppress the interference of sea clutter. Experiments on the IPIX datasets with various observation times and polarization modes are included. The results indicate that the proposed method could not only effectively suppress sea clutter but also achieve better target detection performance than baseline algorithms. Full article

(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)

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Other

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14 pages, 1871 KiB

Open AccessTechnical Note

Enhanced Micro-Doppler Feature Extraction Using Adaptive Short-Time Kernel-Based Sparse Time-Frequency Distribution

by Yang Yang, Yongqiang Cheng, Hao Wu, Zheng Yang and Hongqiang Wang

Remote Sens. 2024, 16(1), 146; https://doi.org/10.3390/rs16010146 - 29 Dec 2023

Viewed by 552

Abstract

The extraction of the micro-Doppler (m-D) feature based on time-frequency distribution (TFD) is of great significance for target detection and identification. To improve the feature extraction performance, numerous TFDs have been developed, with the majority falling under Cohen’s class. Nevertheless, these TFDs basically [...] Read more.

The extraction of the micro-Doppler (m-D) feature based on time-frequency distribution (TFD) is of great significance for target detection and identification. To improve the feature extraction performance, numerous TFDs have been developed, with the majority falling under Cohen’s class. Nevertheless, these TFDs basically face a trade-off between artifact suppression and energy concentration. The main reason is that each Cohen’s class TFD is constructed by applying the two-dimensional Fourier transform to a kerneled ambiguity function directly, while existing kernels generally attenuate artifacts at the expense of losing valuable information. In this paper, a TFD reconstruction method employing an adaptive short-time kernel (ASTK) is developed in the framework of sparse representation (SR) theory to overcome this trade-off and enhance the m-D feature. Firstly, the task of the optimal kernel is explained from the viewpoint of the instantaneous auto-correlation function (IAF). Secondly, based on the quasi-linear frequency modulation feature of most m-D signals during short-time periods, the distribution rule of the short-time IAF (STIAF) in the ambiguity plane is concluded. Guided by this rule, an ASTK that can effectively remove unwanted artifacts with the least information loss is designed. Finally, an SR-based reconstruction procedure is conducted on the kerneled STIAF to generate an artifact-free TFD with high energy concentration, which can effectively enhance the m-D feature. Experiments using both simulated and real-world m-D signals demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)

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15 pages, 7020 KiB

Open AccessTechnical Note

New Application: A Hand Air Writing System Based on Radar Dual View Sequential Feature Fusion Idea

by Yinan Zhao, Tao Liu, Xiang Feng, Zhanfeng Zhao, Wenqing Cui and Yu Fan

Remote Sens. 2022, 14(20), 5177; https://doi.org/10.3390/rs14205177 - 16 Oct 2022

Cited by 3 | Viewed by 1474

Abstract

In recent years, non-contact human–computer interactions have aroused much attention. In this paper, we mainly propose a dual view observation system based on the frontal and side millimeter-wave radars (MWR) to collect echo data of the Air writing digits “0~9”, simultaneously. Additionally, we [...] Read more.

In recent years, non-contact human–computer interactions have aroused much attention. In this paper, we mainly propose a dual view observation system based on the frontal and side millimeter-wave radars (MWR) to collect echo data of the Air writing digits “0~9”, simultaneously. Additionally, we also propose a novel distance approximation method to make the trajectory reconstruction more efficient. To exploit these characteristics of spatial-temporal adjacency in handwriting digits, we propose a novel clustering algorithm, named the constrained density-based spatial clustering of application with noise (CDBSCAN), to remove background noise or clutter. Moreover, we also design a robust gesture segmentation method by using twice-difference and high–low thresholds. In our trials and comparisons, based on the trajectories formulated by echo data series of time–distance and time–velocity of dual views, we present a lightweight-based convolution neural network (CNN) to realize these digits recognition. Experiment results show that our system has a relatively high recognition accuracy, which would provide a feasible application for future human–computer interaction scenarios. Full article

(This article belongs to the Special Issue Doppler Radar: Signal, Data and Applications)

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