scholarly journals Radar Signal Sorting Method Based on Radar Coherent Characteristic

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1144
Author(s):  
Jian Xue ◽  
Lan Tang ◽  
Xinggan Zhang ◽  
Lin Jin
Keyword(s):  
Simulation Analysis ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Central Moment ◽  
Radar Signal ◽  
Accuracy Rate ◽  
Sorting Method ◽  
Feature Difference ◽  
New Feature ◽  
Moment Feature

Aiming at the problem of reliability reduction of signal sorting in terms of the traditional five parameters and intrapulse feature in a complex electromagnetic environment, a new signal sorting method based on radar coherent characteristics is proposed. The main idea of this method is using spectrum analysis to obtain the spectrum images of coherent and noncoherent signals. Image-processing technology is used to extract the feature difference between the two spectrum images, and the central-moment feature is introduced to describe this difference. Through simulation analysis, the feasibility of using the central-moment feature as the coherent feature for signal sorting was proved. In order to check the effectiveness of the proposed feature, a number of simulations were conducted to demonstrate the sorting capability in terms of the coherent feature. From the simulations, it can be seen that the proposed feature not only can be used as a new feature for signal sorting but also that it can be utilized as a supplement for five typical parameters and the intrapulse feature to improve the sorting accuracy rate. Simulations also showed the proposed method could achieve satisfactory sorting results in a low signal-to-noise ratio (SNR). When the SNR was 5 dB, the sorting accuracy rate could reach 98%.

scholarly journals A Novel Method of Radar Emitter Identification Based on the Coherent Feature

2020 ◽  
Vol 10 (15) ◽  
pp. 5256
Author(s):  
Jian Xue ◽  
Lan Tang ◽  
Xinggan Zhang ◽  
Lin Jin
Keyword(s):  
Autocorrelation Function ◽  
Simulation Analysis ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Main Peak ◽  
Support Vector ◽  
Coherent Signals ◽  
Feature Extraction Method ◽  
New Feature ◽  
Peak Value

To deal with the problem of reliability degradation of radar emitter identification (REID) based on the traditional five parameters in a complex electromagnetic environment, a new feature extraction method based on the autocorrelation function of coherent signals, which makes full use of the coherent characteristic of modern radar emitters, is proposed in this paper. The main idea of this paper is utilizing the instantaneous autocorrelation function to obtain the correlation results of coherent and noncoherent signals. To this end, a new feature parameter, named the ratio of the secondary peak value to the main peak value (SMR), is defined to describe the difference of correlation results between coherent and noncoherent signals. Through simulation analysis, the feasibility of using SMR as the coherent feature for REID is verified. In order to evaluate the effectiveness of the coherent feature, an analytical hierarchy process (AHP) was introduced to compare the comprehensive performance of the coherent feature and the existing parameters, and then convolution neural network (CNN) and support vector machine (SVM) were selected as the classifier to check the recognition capability of the proposed feature. Simulation results show that the proposed feature can not only be used as a new feature for REID but can also be used as a supplement to existing feature parameters to improve the accuracy of REID as it is more insensitive to the signal-to-noise ratio (SNR) and signal modulation type changes.

New Feature Vectors using GFCC for Speaker Identification

2018 ◽  
Vol 6 (8) ◽  
pp. 243
Author(s):  
A. Nagesh
Keyword(s):  
Speaker Recognition ◽  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Extraction Methods ◽  
Identification System ◽  
Identification Performance ◽  
Feature Vectors ◽  
Overall Performance ◽  
New Feature

The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system.  The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies have shown very good robustness against noise and acoustic change. The main idea is  GFCC features based on GMM feature extraction is to improve the overall speaker identification performance in low signal to noise ratio (SNR) conditions.

Study on the Performance of Chaotic Oscillator in Weak Signal Detection

2012 ◽  
Vol 263-266 ◽  
pp. 516-520
Author(s):  
Yan Mang Su ◽  
Zhen Bin Gao ◽  
Xiao Zhe Liu ◽  
Na Zheng
Keyword(s):  
Initial Phase ◽  
Signal To Noise Ratio ◽  
Sampling Frequency ◽  
Signal Frequency ◽  
Chaotic Oscillator ◽  
Reference Frequency ◽  
Weak Signal Detection ◽  
Accuracy Rate ◽  
Frequency Detection ◽  
Output Variance

For the frequency detection by using chaotic oscillator, in this paper, the results which are under the influence of the sampling frequency on the basis of theory that the output variance of the system will reach a maximum when the reference frequency equals to the signal frequency are analyzed. Experiments have indicated that the accuracy rate of the results will be improved and the signal to noise ratio (SNR) threshold will be reduced by increasing the sampling frequency to a certain degree. Besides, we have a further research on detecting the frequency of a signal with an initial phase based on the theory mentioned above, simulation experimental results have verified the output variance still has a drastic change when the reference frequency is equal to the signal frequency.

A High-Performance Signal Processing System for Monopulse Tracking Radar

2011 ◽  
Vol 383-390 ◽  
pp. 471-475
Author(s):  
Yong Bin Hong ◽  
Cheng Fa Xu ◽  
Mei Guo Gao ◽  
Li Zhi Zhao
Keyword(s):  
Signal Processing ◽  
High Performance ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Processing System ◽  
Digital Signal ◽  
Radar Signal ◽  
Signal Processing System ◽  
Parallel Pipelined ◽  
Tracking Radar

A radar signal processing system characterizing high instantaneous dynamic range and low system latency is designed based on a specifically developed signal processing platform. Instantaneous dynamic range loss is a critical problem when digital signal processing is performed on fixed-point FPGAs. In this paper, the problem is well resolved by increasing the wordlength according to signal-to-noise ratio (SNR) gain of the algorithms through the data path. The distinctive software structure featuring parallel pipelined processing and “data flow drive” reduces the system latency to one coherent processing interval (CPI), which significantly improves the maximum tracking angular velocity of the monopulse tracking radar. Additionally, some important electronic counter-countermeasures (ECCM) are incorporated into this signal processing system.

scholarly journals Empirical Mode Decomposition in Discrete Time Signals Denoising

2019 ◽  
Vol 16 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Zoltán Germán-Salló
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Discrete Wavelet ◽  
Intrinsic Mode Functions ◽  
Nonlinear Signal Processing ◽  
Mode Decomposition ◽  
Time Signals ◽  
Nonlinear Signal ◽  
Filtering Procedure

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

Nano-copper Enhanced Flexible Device for Simultaneous Measurement of Human Cardio-pulmonary Activities

2020 ◽  
Author(s):  
Li Wang ◽  
Feng Zhang ◽  
Kechao Lu ◽  
Mohammed Abdulaziz ◽  
Chao Li ◽  
...  
Keyword(s):  
Simulation Analysis ◽  
Signal To Noise Ratio ◽  
Simultaneous Detection ◽  
Vital Signs ◽  
Electrical Measurement ◽  
Sensing Element ◽  
Flexible Device ◽  
System Disease ◽  
Compact Size ◽  
Human Respiration

Abstract Background: Cardiopulmonary activities reflect the ability of the human heart to pump blood and the lungs to inhale oxygen. Thus, a device could simultaneously measure electro-cardiac signal and respiratory pressure could provide vital signs for predicting early warning of cardio-pulmonary function-related chronic diseases such as cardiovascular disease, and respiratory system disease. Results: In this study, a flexible device integrated with piezo-resistive sensing element and voltage-sensing element was developed to simultaneously measure human respiration and electro-cardiac signal (including respiratory pressure, respiration frequency, and respiration rhythm; electro-cardio frequency, electro-cardio amplitude, and electro-cardio rhythm). When applied to the measurement of respiratory pressure, the piezo-resistive performance of the device was enhanced by nano-copper modification, which detection limitation of pressure can reduce to 100 Pa and the sensitivity of pressure can achieve to 0.053 ± 0.00079 kPa-1. In addition, the signal-to-noise ratio during bio-electrical measurement was increased to 10.7 ± 1.4, five times better than that of the non-modified device. Conclusion: This paper presents a flexible device for the simultaneous detection of human respiration and cardiac electrical activity. To avoid interference between the two signals, the layout of the electrode and the strain sensor was optimized by FEA simulation analysis. To improve the piezo-resistive sensitivity and bio-electric capturing capability of the device, a feather-shaped nano-copper was modified onto the surface of carbon fiber. The operation simplicity, compact size, and portability of the device open up new possibilities for multi-parameter monitoring.

Protein structure prediction based on BN-GRU method

2020 ◽  
Vol 18 (06) ◽  
pp. 2050045
Author(s):  
Lina Yang ◽  
Pu Wei ◽  
Cheng Zhong ◽  
Xichun Li ◽  
Yuan Yan Tang
Keyword(s):  
Neural Network ◽  
Protein Structure ◽  
Spatial Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Main Idea ◽  
Traditional Methods ◽  
Analysis Of Similarity ◽  
New Feature ◽  
Activity Mechanism

The spatial structure of the protein reflects the biological function and activity mechanism. Predicting the secondary structure of a protein is the basis content for predicting its spatial structure. Traditional methods based on statistics and sequential patterns do not achieve higher accuracy. In this paper, the application of BN-GRU neural network in protein structure prediction is discussed. The main idea is to construct a Gated Recurrent Unit (GRU) neural network. The GRU neural network can learn long-term dependencies. It can handle long sequences better than traditional methods. Based on this, BN is combined with GRU to construct a new network. Position Specific Scoring Matrix (PSSM) is used to associate with other features to build a completely new feature set. It can be proved that the application of BN on GRU can improve the accuracy of the results. The idea in this paper can also be applied to the analysis of similarity of other sequences.

scholarly journals LPI Radar Waveform Recognition Based on CNN and TPOT

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 725 ◽  
Author(s):  
Jian Wan ◽  
Xin Yu ◽  
Qiang Guo
Keyword(s):  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Recognition System ◽  
Radar Signal ◽  
Automatic Identification ◽  
Identification System ◽  
Electronic Warfare ◽  
Time Frequency ◽  
Barker Codes ◽  
Intelligence Support

The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB.

scholarly journals MalDeep: A Deep Learning Classification Framework against Malware Variants Based on Texture Visualization

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yuntao Zhao ◽  
Chunyu Xu ◽  
Bo Bo ◽  
Yongxin Feng
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Feature Space ◽  
Image Texture ◽  
Accuracy Rate ◽  
Texture Representation ◽  
Malware Classification ◽  
Classification Framework ◽  
Average Accuracy ◽  
New Feature

The increasing sophistication of malware variants such as encryption, polymorphism, and obfuscation calls for the new detection and classification technology. In this paper, MalDeep, a novel malware classification framework of deep learning based on texture visualization, is proposed against malicious variants. Through code mapping, texture partitioning, and texture extracting, we can study malware classification in a new feature space of image texture representation without decryption and disassembly. Furthermore, we built a malware classifier on convolutional neural network with two convolutional layers, two downsampling layers, and many full connection layers. We adopt the dataset, from Microsoft Malware Classification Challenge including 9 categories of malware families and 10868 variant samples, to train the model. The experiment results show that the established MalDeep has a higher accuracy rate for malware classification. In particular, for some backdoor families, the classification accuracy of the model reaches over 99%. Moreover, compared with other main antivirus software, MalDeep also outperforms others in the average accuracy for the variants from different families.

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