scholarly journals Radiant Source Signal Recognition Algorithm based on SAE

2018 ◽  
Vol 173 ◽  
pp. 02018
Author(s):  
Ye Wen-qiang ◽  
Yu Zhi-fu ◽  
Zhang Kui ◽  
Wang Hu-bang
Keyword(s):  
Recognition Rate ◽  
Training Model ◽  
Recognition Algorithm ◽  
Identification Algorithm ◽  
Signal Recognition ◽  
Softmax Classifier ◽  
Time Frequency ◽  
Reduction Methods ◽  
Source Signal ◽  
Better Than

Aiming at the shortcomings of traditional radar identification based on artificial judgment and module matching, this paper proposes an intelligent identification algorithm based on joint time-frequency. The radar radiation source signal is transformed by time-frequency, and the processed signal is input into the automatic encoder through different kinds of dimensionality reduction methods, and the pre-training adjustment depth learning model is adopted, and the commonly used softmax classifier is adopted to the pre-training model. Oversee fine school and identification, and finally complete the identification task. The simulation results show that high recognition rate can be achieved by this algorithm, and the joint dimension reduction is better than other methods.

scholarly journals UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm

2021 ◽  
Vol 13 (6) ◽  
pp. 1205
Author(s):  
Caidan Zhao ◽  
Gege Luo ◽  
Yilin Wang ◽  
Caiyun Chen ◽  
Zhiqiang Wu
Keyword(s):  
Recognition Performance ◽  
Principal Component ◽  
Texture Features ◽  
Training Model ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Dynamic Feature ◽  
Complex Environments ◽  
Time Frequency ◽  
Model Training

A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.

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 A RASPBERRY PI SELF-DRIVING CART BASED ON OPENCV AND DEEP LEARNING .

2021 ◽  
Vol 10 (3) ◽  
pp. 61-67
Keyword(s):  
Deep Learning ◽  
Obstacle Avoidance ◽  
Target Recognition ◽  
Recognition Algorithm ◽  
Traffic Signal ◽  
The Self ◽  
Raspberry Pi ◽  
Signal Recognition ◽  
Ultrasonic Sensors ◽  
Better Than

The self-driving trolley created in this thesis uses cameras and ultrasonic sensors to obtain roadway information, and a deep learning based target recognition algorithm to find out which are the targets in the data obtained, so that the trolley can drive itself on a simulated roadway with functions such as obstacle avoidance and traffic signal recognition. Originally the car used a Raspberry Pi 3b+, but here the jetson nano, which is better than the Raspberry Pi 3b+, is used to implement it.

scholarly journals Recognition Algorithm of Popular Elements of Ethnic Minority Traditional Clothing Based on PCA

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hu Juan
Keyword(s):  
Ethnic Minority ◽  
National Culture ◽  
Euclidean Distance ◽  
Recognition Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Pca Method ◽  
Neural Network Algorithm ◽  
Matching Degree ◽  
Better Than

Image recognition of ethnic minority costumes is helpful for people to understand, carry forward, and inherit national culture. Taking the minority clothing image as the research object, the image enhancement and threshold segmentation are completed; the principal component features of the minority clothing image are extracted by PCA method; and the image matching degree is obtained according to the principle of minimizing the Euclidean distance. Finally, the calculation process of the PCA method is optimized by a wavelet transform algorithm to realize the recognition of popular elements of minority traditional clothing. The comparative experimental results show that the PCA + BP neural network algorithm is better than the other two recognition algorithms in recognition rate and recognition time.

scholarly journals A Recognition Algorithm for Modulation Schemes by Convolution Neural Network and Spectrum Texture

2019 ◽  
Vol 37 (4) ◽  
pp. 816-823
Author(s):  
Pengyuan Bai ◽  
Hua Xu ◽  
Li Sun
Keyword(s):  
Neural Network ◽  
Recognition Algorithm ◽  
Convolution Neural Network ◽  
Image Texture ◽  
Communication Signals ◽  
Softmax Classifier ◽  
Spectrum Monitoring ◽  
Traditional Algorithm ◽  
Modulation Schemes ◽  
Better Than

The recognition of modulation schemes for communication signals is an important part of communication surveillance and spectrum monitoring. An algorithm based on deep learning and spectrum texture is proposed to recognize modulation schemes. Based on imperceptible differences among various spectrums of modulation schemes, the algorithm uses Convolution Neural Network to capture the features of image texture and thus classify the features with a SOFTMAX classifier. The experiment shows the algorithm performs better than traditional algorithm based on feature parameters, while the features captured can better reveal the signal detail and reduces effort on feature parameter design.

A Novel Face Recognition Algorithm

2013 ◽  
Vol 718-720 ◽  
pp. 2055-2061
Author(s):  
Cai Rang Zhaxi ◽  
Yue Guang Li
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Feature Space ◽  
Recognition Algorithm ◽  
Analysis Method ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Discriminant Analysis Method ◽  
Pca Method ◽  
Better Than

This paper firstly analyzes the principle of face recognition algorithm, studies feature selection and distance criterion problem, puts forward the defects of PCA face recognition algorithm and LDA face recognition algorithm. According to the deficiencies and shortcomings of PCA face recognition algorithm and LDA face recognition algorithm, this paper proposes a solution -- PCA+LDA. The method uses the PCA method to reduce the dimensionality of feature space, it uses Fisher linear discriminant analysis method to classification, the realization of face recognition. Experiments show that, this method can not only improve the feature extraction speed, but also the recognition rate is better than single PCA method and LDA method.

scholarly journals LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 540 ◽  
Author(s):  
Qiang Guo ◽  
Xin Yu ◽  
Guoqing Ruan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Recognition Rate ◽  
Training Model ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Deep Convolution Network ◽  
Original Time

Low Probability of Intercept (LPI) radar waveform recognition is not only an important branch of the electronic reconnaissance field, but also an important means to obtain non-cooperative radar information. To solve the problems of LPI radar waveform recognition rate, difficult feature extraction and large number of samples needed, an automatic classification and recognition system based on Choi-Williams distribution (CWD) and depth convolution neural network migration learning is proposed in this paper. First, the system performs CWD time-frequency transform on the LPI radar waveform to obtain a 2-D time-frequency image. Then the system preprocesses the original time-frequency image. In addition, then the system sends the pre-processed image to the pre-training model (Inception-v3 or ResNet-152) of the deep convolution network for feature extraction. Finally, the extracted features are sent to a Support Vector Machine (SVM) classifier to realize offline training and online recognition of radar waveforms. The simulation results show that the overall recognition rate of the eight LPI radar signals (LFM, BPSK, Costas, Frank, and T1–T4) of the ResNet-152-SVM system reaches 97.8%, and the overall recognition rate of the Inception-v3-SVM system reaches 96.2% when the SNR is −2 dB.

The Application of the Fuzzy ART Neural Network Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1783-1786 ◽  
Author(s):  
Ming Gu
Keyword(s):  
Neural Network ◽  
Simulation Experiment ◽  
Recognition Rate ◽  
Learning Rule ◽  
Recognition Algorithm ◽  
Fuzzy Art ◽  
Fast Learning ◽  
Average Recognition Rate ◽  
Neural Network Algorithm ◽  
Better Than

An algorithm based on fuzzy ART neural network which can deal with online-learning and recognition of the known and unknown faces at the same time was designed and realized. Based on structure and learning rule of the fuzzy ART system, face recognition algorithm was designed. The simulation experiment results show that average recognition rate of not fast learning is better than fast learning. Not fast learning is accepted to get 89.83% online and 99.42% offline recognition rate.

A Resilient Novel Compressed-Domain Audio Recognition Method for Anti-Linear Speed Change

2014 ◽  
Vol 620 ◽  
pp. 613-618
Author(s):  
Li Ming Wu ◽  
Wei Han ◽  
Yao Fei Li ◽  
Song Bin Zhou ◽  
Si Cheng Chen
Keyword(s):  
Recognition Rate ◽  
Industrial Process ◽  
Recognition Algorithm ◽  
Practical Significance ◽  
Commercial Application ◽  
Compressed Domain ◽  
Linear Speed ◽  
Time Frequency ◽  
Common Time ◽  
Speed Change

Audio fingerprint technology has been increasingly played an important role in audio content identification, audio information security, industrial process monitoring, etc. Due to compressed format has become the main way for audio files storage and transmission, it owns more practical significance that directly extracting audio fingerprint from compressed-domain. In general, existing compressed-domain audio fingerprint schemes are robust to common time-frequency-domain distortion, including noise, echo, band-pass filtering, 32Kbps@MP3 and others. But they are difficult to deal with large linear speed change distortion which is a frequent audio processing means in the field of television and broadcast. This paper proposes a novel compressed-domain audio recognition algorithm, which can resist linear speed change in the range of-10% to 10% (recognition rate is higher than 90%), via extracting fingerprint after do Fourier-Mellin transform for sub-band energy sequence of MDCT spectrum. This is enough to cope with almost all situations of audio acceleration/deceleration occurred in commercial application. In addition, it shows similarity in other performance compared with existing excellent compressed-domain audio recognition algorithms.

The Recognition and Research of Improved Probabilistic Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 2819-2824
Author(s):  
Xiao Jing Shang
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Bp Network ◽  
Training Time ◽  
Neural Network Structure ◽  
Neural Network Algorithm ◽  
Better Than

Probabilistic neural network compared with the traditional BP neural network structure is simpler and it is faster to be identificated, so it is widely used in the field of pattern recognition. This paper is mainly focused on similar gesture recognition research, propose an probabilistic neural network gesture recognition algorithm. The simulation results show that the improved probabilistic neural network algorithm on the recognition rate and training time is better than the traditional BP network.

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