scholarly journals A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 659
Author(s):  
Jian Wan ◽  
Guoqing Ruan ◽  
Qiang Guo ◽  
Xue Gong
Keyword(s):  
Recognition Rate ◽  
Recognition System ◽  
Inner Product ◽  
Radar Signal ◽  
Signal Recognition ◽  
Recognition Method ◽  
Electronic Warfare ◽  
Quantum Genetic Algorithm ◽  
Optimal Classification ◽  
The Difference

Radar electronic reconnaissance is an important part of modern and future electronic warfare systems and is the primary method to obtain non-cooperative intelligence information. As the task requirement of radar electronic reconnaissance, it is necessary to identify the non-cooperative signals from the mixed signals. However, with the complexity of battlefield electromagnetic environment, the performance of traditional recognition system is seriously affected. In this paper, a new recognition method based on optimal classification atom and improved double chains quantum genetic algorithm (IDCQGA) is researched, optimal classification atom is a new feature for radar signal recognition, IDCQGA with symmetric coding performance can be applied to the global optimization algorithm. The main contributions of this paper are as follows: Firstly, in order to measure the difference of multi-class signals, signal separation degree based on distance criterion is proposed and established according to the inter-class separability and intra-class aggregation of the signals. Then, an IDCQGA is proposed to select the best atom for classification under the constraint of distance criterion, and the inner product of the signal and the best atom for classification is taken as the eigenvector. Finally, the extreme learning machine (ELM) is introduced as classifier to complete the recognition of signals. Simulation results show that the proposed method can improve the recognition rate of multi-class signals and has better processing ability for overlapping eigenvector parameters.

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.

Phased Array Radar Signal Recognition Method Based on Ant Colony Optimization and SVM

2012 ◽  
Vol 263-266 ◽  
pp. 2566-2569
Author(s):  
Jian Feng Pu ◽  
Jun Lin ◽  
Yan Zhi Li ◽  
Wei Quan
Keyword(s):  
Ant Colony Optimization ◽  
Phased Array ◽  
Separation Efficiency ◽  
Experimental Results ◽  
Ant Colony ◽  
Radar Signal ◽  
Access Time ◽  
Signal Recognition ◽  
Recognition Method ◽  
Phased Array Radar

In order to improve the efficiency for phased array radar's ESM, an ACO and SVM conjoint method is used in this paper to solve the problem of phased array radar signal recognition. By introducing ACO to supervise SVM parametric selection, the method is able to quickly discover seemly parameter value and improve SVM separation efficiency. Experimental results show that textual algorithm possess upper exactness rate to phased array radar that the whole pulse signals sorting can be identified. With normal-SVM and RST-SVM means to compare, the algorithm SVM parameter access time is short, thereby shorten the monolithic hour.

A Fault-Tolerant Chinese Check Recognition System

1997 ◽  
Vol 11 (04) ◽  
pp. 571-593 ◽  
Author(s):  
Hui Su ◽  
Bin Zhao ◽  
Feng Ma ◽  
Song Wang ◽  
Shaowei Xia
Keyword(s):  
Fault Tolerant ◽  
Recognition Rate ◽  
Recognition System ◽  
Correct Recognition ◽  
Code Verification ◽  
Recognition Method ◽  
Substitution Error ◽  
Digit Recognition ◽  
Secret Code

In this paper, a complete fault-tolerant check recognition system is proposed which has no check substitution error under the secret code verification. The fault-tolerant recognition method proposed in this paper creates all possible candidates for verification under the limited fault-tolerant rate, and with three classifiers of high isolated digit recognition rate, the system can always find out the correct recognition results of checks if there exist the correct labels of all the digits. Since the three classifiers are designed independently by different methods and they extract different features of handwritten digits, they can compensate each other when confusing digits are met. The segmentation stage combines the three most popular strategies, and gives out a way for segmenting unconstrained handwritten numeral strings on Chinese checks.

scholarly journals Research on Speech Recognition Method in Multi Layer Perceptual Network Environment

2021 ◽  
Vol 15 ◽  
pp. 996-1004
Author(s):  
Kai Zhao ◽  
Dan Wang
Keyword(s):  
Speech Recognition ◽  
Speech Signal ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Average Energy ◽  
Signal Recognition ◽  
Network Environment ◽  
Recognition Method ◽  
Zero Crossing ◽  
Zero Crossing Rate

Aiming at the problem of low recognition rate in speech recognition methods, a speech recognition method in multi-layer perceptual network environment is proposed. In the multi-layer perceptual network environment, the speech signal is processed in the filter by using the transfer function of the filter. According to the framing process, the speech signal is windowed and framing processed to remove the silence segment of the speech signal. At the same time, the average energy of the speech signal is calculated and the zero crossing rate is calculated to extract the characteristics of the speech signal. By analyzing the principle of speech signal recognition, the process of speech recognition is designed, and the speech recognition in multi-layer perceptual network environment is realized. The experimental results show that the speech recognition method designed in this paper has good speech recognition performance

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.

scholarly journals Signal Modulation Recognition Method Based on Differential Privacy Federated Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jibo Shi ◽  
Lin Qi ◽  
Kuixian Li ◽  
Yun Lin
Keyword(s):  
Deep Learning ◽  
Differential Privacy ◽  
Recognition Rate ◽  
Learning Approaches ◽  
Signal Recognition ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Recognition Method ◽  
Privacy And Security ◽  
Spectrum Detection

Signal modulation recognition is widely utilized in the field of spectrum detection, channel estimation, and interference recognition. With the development of artificial intelligence, substantial advances in signal recognition utilizing deep learning approaches have been achieved. However, a huge amount of data is required for deep learning. With increasing focus on privacy and security, barriers between data sources are sometimes difficult to break. This limits the data and renders them weak, so that deep learning is not sufficient. Federated learning can be a viable way of solving this challenge. In this article, we will examine the recognition of signal modulation based on federated learning with differential privacy, and the results show that the recognition rate is acceptable while data protection and security are being met.

A New Recognition Method of Radar Emitter Signal

2013 ◽  
Vol 380-384 ◽  
pp. 3509-3512
Author(s):  
Fei Ye ◽  
Xin Wang ◽  
Xing Rong Gao ◽  
Jun Luo
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Template Matching ◽  
Kernel Density ◽  
Recognition Algorithm ◽  
Radar Signal ◽  
Signal Recognition ◽  
Recognition Method ◽  
Signal Parameters ◽  
Probability Density Curve

According to the problem that the existing radar signal recognition method cannot effectively identify the radar signal, a new recognition method based on kernel density estimation is proposed. First using kernel density estimation gets the probability density curve of radar emitter signal parameters, then storing the cures into database as the characteristics, in the end a radar emitter signal recognition algorithm based on template matching is proposed.

An Effective Radar Signal Recognition Method Using Neural Architecture Search

2021 ◽  
Author(s):  
Min Zhang ◽  
Wang Luo ◽  
Yu Wang ◽  
Jinlong Sun ◽  
Jie Yang ◽  
...  
Keyword(s):  
Radar Signal ◽  
Signal Recognition ◽  
Recognition Method ◽  
Neural Architecture

scholarly journals A Design of a Hybrid Algorithm for Optical Character Recognition of Online Hand-Written Arabic Alphabets

2019 ◽  
pp. 2067-2079
Author(s):  
Waleed Noori Hussein ◽  
Haider N. Hussain
Keyword(s):  
Decision Tree ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Optical Character ◽  
Recognition Systems ◽  
The Difference ◽  
Artificial Neural Network Ann ◽  
Handwritten Recognition

     The growing relevance of printed and digitalized hand-written characters has necessitated the need for convalescent automatic recognition of characters in Optical Character Recognition (OCR). Among the handwritten characters, Arabic is one of those with special attention due to its distinctive nature, and the inherent challenges in its recognition systems. This distinctiveness of Arabic characters, with the difference in personal writing styles and proficiency, are complicating the effectiveness of its online handwritten recognition systems. This research, based on limitations and scope of previous related studies, studied the recognition of Arabic isolated characters through the identification of its features and dots in view of producing an efficient online Arabic handwriting isolated character recognition system. It proposes a hybrid of decision tree and Artificial Neural Network (ANN), as against being combined with other algorithms as found in previous studies. The proposed recognition process has four main steps with associated sub-steps. The results showed that the proposed method achieved the highest performance at 96.7%, whereas the benchmark methods which are EDMS and Naeimizaghiani had 68.88% and 78.5 % respectively. Based on this, ANN has the best performance recognition rate at 98.8%, while the best rate for decision tree was obtained at 97.2%.

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