A Visual Simulation Method for 2-D Complex Situation of Electromagnetic Environment

2014 ◽  
Vol 568-570 ◽  
pp. 1516-1519
Author(s):  
Meng Zhong Hu ◽  
Tong Sheng Yi ◽  
Jia Liang Pan
Keyword(s):  
Simulation Method ◽  
Electromagnetic Environment ◽  
Complex Situation ◽  
Time Frequency ◽  
Energy Domain ◽  
Display Technology ◽  
Query Tool ◽  
Calculation Module ◽  
Software Receivers ◽  
Query Visualization

As the complex situation of electromagnetic environment is difficult to be analyzed and simulated visibly, interface program is developed based on the calculation module of Wireless Insite2.5 software. Equipments such as communication, radar, electronics resists and other radio devices are studied to build accurate theoretic models. Moreover, a method for creating software receivers quickly in MATLAB is proposed and other equipment models are created automatically just based on the parameters of equipments in the same way. Then, situations of time, frequency and energy-domain are calculated successfully with using Wireless Insite2.5. Complex 2-D situation of electromagnetic environment is displayed in the 2-D map by using MATLAB map display technology. At last, Display and query visualization for complex situation of electromagnetic environment is realized by designing a visual data query tool of mouse.

Automatic Recognition of the Lighting Whistler waves from the Wave Data of SCM Boarded on ZH-1 satellite

2021 ◽  
Author(s):  
Jing Yuan ◽  
Zijie Wang ◽  
Dehe Yang ◽  
Qiao Wang ◽  
Zeren Zima ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Satellite Observation ◽  
Original Data ◽  
Electromagnetic Environment ◽  
Mel Frequency Cepstral Coefficients ◽  
Frequency Profile ◽  
Time Frequency ◽  
Wave Data ◽  
Audio Clip ◽  
Long Short Term Memory

<p>Lightning whistlers, found frequently in electromagnetic satellite observation, are the important tool to study electromagnetic environment of the earth space. With the increasing data from electromagnetic satellites, a considerable amount of time and human efforts are needed to detect lightning whistlers from these tremendous data. In recent years, algorithms for lightning whistlers automatic detection have been conducted. However, these methods can only work in the time-frequency profile (image) of the electromagnetic satellites data with two major limitations: vast storage memory for the time-frequency profile (image) and expensive computation for employing the methods to detect automatically the whistler from the time-frequency profile. These limitations hinder the methods work efficiently on ZH-1 satellite. To overcome the limitations and realize the real-time whistler detection automatically on board satellite, we propose a novel algorithm for detecting lightning whistler from the original observed data without transforming it to the time-frequency profile (image).</p><p>The motivation is that the frequency of lightning whistler is in the audio frequency range. It encourages us to utilize the speech recognition techniques to recognize the whistler in the original data \of SCM VLF Boarded on ZH-1. Firstly, we averagely move a 0.16 seconds window on the original data to obtain the patch data as the audio clip. Secondly, we extract the Mel-frequency cepstral coefficients (MFCCs) of the patch data as a type of cepstral representation of the audio clip. Thirdly, the MFCCs are input to the Long Short-Term Memory (LSTM) recurrent neutral networks to classification. To evaluate the proposed method, we construct the dataset composed of 10000 segments of SCM wave data observed from ZH-1 satellite(5000 segments which involving whistler and 5000 segments without any whistler). The proposed method can achieve 84% accuracy, 87% in recall, 85.6% in F1score.Furthermore, it can save more than 126.7MB and 0.82 seconds compared to the method employing the YOLOv3 neutral network for detecting whistler on each time-frequency profile.</p><p> </p><p>Key words: ZH-1 satellite, SCM,lightning whistler, MFCC, LSTM</p>

scholarly journals A Spatially Distributed Network for Tracking the Pulsation Signal of Flow Field Based on CFD Simulation: Method and a Case Study

2021 ◽  
Vol 5 (4) ◽  
pp. 181
Author(s):  
Faye Jin ◽  
Ran Tao ◽  
Zhaoheng Lu ◽  
Ruofu Xiao
Keyword(s):  
Vortex Shedding ◽  
Frequency Conversion ◽  
Cfd Simulation ◽  
Simulation Method ◽  
Time Frequency ◽  
Flow Problems ◽  
Spatially Distributed ◽  
Edge Vortex ◽  
Vortex Shedding Frequency ◽  
Temporal And Spatial Characteristics

The pulsating characteristics in turbulent flow are very important physical quantities. There are many studies focused on the temporal characteristics of pulsation. However, the spatial distribution of temporal states with pulsations rarely receives attention. Therefore, the pulsation tracking network (PTN) method is proposed to track the pulsating characteristics of turbulence. Based on the computational fluid dynamics (CFD) simulation result, the PTN is arranged in a specific region of the flow domain. The fast Fourier Transform (FFT) method is used for time-frequency conversion. As shown in the example of trailing-edge vortex-shedding flow over NACA0009 hydrofoil, important pulsation quantities, including the total pulsation intensity, dominant frequencies, amplitude of frequencies, and the phase and phase difference, can be obtained with a high spatial resolution. The source, reason and attenuation of the vortex-shedding frequency fvs and the 2 fvs frequency caused by vortex-interaction are well indicated. The dominant regions of fvs and 2 fvs are shown and analysed. The propagation and attenuation of vortex-shedding induced pulsation are understood in detail. Based on the comparison against traditional analysis, PTN is found to function as a good supplement for the CFD post-processing by tracking unknown temporal and spatial characteristics. These findings represent a potential breakthrough in terms of solving actual pulsation-excited flow problems.

scholarly journals A Radar Signal Recognition Approach via IIF-Net Deep Learning Models

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ji Li ◽  
Huiqiang Zhang ◽  
Jianping Ou ◽  
Wei Wang
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Software Radio ◽  
Radar Signal ◽  
Signal Recognition ◽  
Electromagnetic Environment ◽  
Time Frequency ◽  
Low Snr ◽  
Radar Signals

In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.

ON THE FILTERING PROPERTIES OF THE EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 02 (04) ◽  
pp. 397-414 ◽  
Author(s):  
ZHAOHUA WU ◽  
NORDEN E. HUANG
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Wavelet Decomposition ◽  
Mathematical Expression ◽  
Delta Function ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Energy Domain ◽  
The Fourier Transform ◽  
Special Case

The empirical mode decomposition (EMD) based time-frequency analysis has been used in many scientific and engineering fields. The mathematical expression of EMD in the time-frequency-energy domain appears to be a generalization of the Fourier transform (FT), which leads to the speculation that the latter may be a special case of the former. On the other hand, the EMD is also known to behave like a dyadic filter bank when used to decompose white noise. These two observations seem to contradict each other. In this paper, we study the filtering properties of EMD, as its sifting number changes. Based on numerical results of the decompositions using EMD of a delta function and white noise, we conjecture that, as the (pre-assigned and fixed) sifting number is changed from a small number to infinity, the EMD corresponds to filter banks with a filtering ratio that changes accordingly from 2 (dyadic) to 1; the filter window does not narrow accordingly, as the sifting number increases. It is also demonstrated that the components of a delta function resulted from EMD with any prescribed sifting number can be rescaled to a single shape, a result similar to that from wavelet decomposition, although the shape changes, as the sifting number changes. These results will lead to further understandings of the relations of EMD to wavelet decomposition and FT.

scholarly journals Fully Nonstationary Spatially Variable Ground Motion Simulations Based on a Time-Varying Power Spectrum Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Huiguo Chen ◽  
Yingmin Li ◽  
Junru Ren
Keyword(s):  
Power Spectrum ◽  
Ground Motion ◽  
Response Spectrum ◽  
Power Spectra ◽  
Response Spectra ◽  
Simulation Method ◽  
Time Varying ◽  
Time Frequency ◽  
Response Characteristics ◽  
Spectrum Model

By analyzing the evolutionary spectrum method for multivariate nonstationary stochastic processes, a simulation method for fully nonstationary spatially variable ground motion is proposed based on the Kameda time-varying power spectrum model. This method can properly simulate nonstationary spatially variable ground motion based on a target response spectrum. Two numerical examples, in which the Kameda time-varying power spectra are calculated for different conditions, are presented to demonstrate the capabilities of the proposed method. In the first example, the nonstationary spatially variable ground motion that satisfies the time-frequency characteristics and response characteristics of the original ground motion is simulated by identifying the parameters of the given time-varying power spectrum. In the second example, the ground motion that satisfies the design response spectra is simulated by defining the parameters of the time-varying power spectrum directly. The results demonstrate that the method can effectively simulate nonstationary spatially variable ground motion, which implies that the proposed method can be used in engineering applications.

The Complexity Evaluation Method of Electromagnetic Environment Based on Statistical Characteristics Analysis

2013 ◽  
Vol 321-324 ◽  
pp. 779-784
Author(s):  
Fan Wang ◽  
Hui Han ◽  
Jian Wang ◽  
Ya Nan Liu
Keyword(s):  
Evaluation Method ◽  
Average Energy ◽  
Objective Evaluation ◽  
Statistical Characteristics ◽  
Electromagnetic Environment ◽  
Frequency Space ◽  
Time Frequency ◽  
Power Spectral ◽  
Characteristics Analysis ◽  
Variation Characteristics

An objective complexity evaluation method of electromagnetic environment is presented based on statistical characteristics analysis. In order to quantize variation characteristics and statistical law of electromagnetic environment, evaluation factors are introduced such as relative variation, average variational ratio, maximum variational ratio, standard deviation and skewness of power spectral density. Besides, evaluation factors such as time occupancy, spectrum occupancy, space occupancy and Average energy value are utilized to conduct the objective evaluation comprehensively and accurately in time, frequency, space and energy domains. The practicability of this method is validated by monitoring data. Results show that this method can effectively evaluate the complexity of electromagnetic environment.

Evaluation Method for Complex Electromagnetic Environment

2019 ◽  
Vol 23 (5) ◽  
pp. 891-897
Author(s):  
Yan Li ◽  
Yigang He ◽  
Baiqiang Yin ◽  
◽  
Keyword(s):  
Background Noise ◽  
Noise Intensity ◽  
Evaluation Method ◽  
Electromagnetic Environment ◽  
Environmental Complexity ◽  
Training Time ◽  
Time Frequency ◽  
S Transform ◽  
Fast Convergence Rate ◽  
Learning Machine

To perform a complexity evaluation for an electromagnetic environment (EME), a new method based on the S-transform is proposed, which can simultaneously count the time occupancy, frequency occupancy, and energy occupancy in the time–frequency domain. The frequency coincidence, modulation similarity, and background noise intensity are selected as important evaluation indices, and their physical interpretations are analyzed and calculated. The Extreme Learning Machine (ELM) method is adopted to evaluate the environmental complexity. The proposed method (S-ELM) requires less training time and has a fast convergence rate. The simulation and experimental results confirm that the proposed method is accurate and efficient.

scholarly journals A Method against Interrupted-Sampling Repeater Jamming Based on Energy Function Detection and Band-Pass Filtering

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Yuan ◽  
Chun-yang Wang ◽  
Xin Li ◽  
Lei An
Keyword(s):  
Frequency Modulation ◽  
Energy Function ◽  
Large Time ◽  
Echo Signal ◽  
Time Frequency ◽  
Low Sidelobe ◽  
Energy Domain ◽  
Band Pass ◽  
Simulation Results ◽  
Filtering Function

Interrupted-sampling repeater jamming (ISRJ) is a new kind of coherent jamming to the large time-bandwidth linear frequency modulation (LFM) signal. Many jamming modes, such as lifelike multiple false targets and dense false targets, can be made through setting up different parameters. According to the “storage-repeater-storage-repeater” characteristics of the ISRJ and the differences in the time-frequency-energy domain between the ISRJ signal and the target echo signal, one new method based on the energy function detection and band-pass filtering is proposed to suppress the ISRJ. The methods mainly consist of two parts: extracting the signal segments without ISRJ and constructing band-pass filtering function with low sidelobe. The simulation results show that the method is effective in the ISRJ with different parameters.

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