An algorithm for artillery noise signal detection and classification in time-domain

2012 ◽  
Vol 131 (4) ◽  
pp. 3221-3221
Author(s):  
Yinlong Zhou
Keyword(s):  
Signal Detection ◽  
Time Domain ◽  
Noise Signal

The Radar Signal Detection Based on Higher Order Statistics

2013 ◽  
Vol 310 ◽  
pp. 421-423
Author(s):  
Chun Yu Wang ◽  
Xing Long Qi ◽  
Run Lan Tian ◽  
Lin Ren
Keyword(s):  
Signal Detection ◽  
Order Statistics ◽  
Gaussian Noise ◽  
Signal Detection Theory ◽  
Detection Method ◽  
Noise Signal ◽  
Higher Order ◽  
Radar Signal ◽  
Higher Order Statistics ◽  
Colored Gaussian Noise

Radar signal detection theory is significant for the radar signal detection, and there are many radar signal detection method at present. In this paper, higher order statistics was used to achieve the radar signal detection. It analyzed the basic theory of higher order statistics and higher order statistics in radar signal detection. And it achieved radar signal detection in the MATLAB software, colored Gaussian noise signal detection method based on dual-spectrum was used to detect the radar signal mixed with man-made noise.

scholarly journals Research on the Detection of Vehicle Vibration and Noise Signal Based On Negative Entropy Detection Algorithm

2021 ◽  
Vol 2074 (1) ◽  
pp. 012055
Author(s):  
Naibin Zhai ◽  
Haijun Zhao ◽  
Xintao Cui
Keyword(s):  
Signal Detection ◽  
Detection Method ◽  
Recognition Accuracy ◽  
Detection Algorithm ◽  
Noise Signal ◽  
Noise Model ◽  
Vehicle Noise ◽  
Negative Entropy ◽  
And Control ◽  
Coding Bits

Abstract As an important part of vehicle noise signal detection and processing, negative entropy detection algorithm can accurately reduce the number of speech coding bits, ameliorate the recognition accuracy, and establish the noise model in the process of noise reduction. Based on this, this paper first analyses the source and control of vehicle vibration and noise, then studies the principle of negative entropy detection algorithm of vehicle vibration and noise signal, and finally gives the vehicle vibration and noise signal detection method based on negative entropy detection algorithm.

Time-Domain Coherent Anti-Stokes Raman Scattering Signal Detection for Terahertz Vibrational Spectroscopy Using Chirped Femtosecond Pulses

2010 ◽  
Vol 3 (7) ◽  
pp. 072401 ◽  
Author(s):  
Masahiko Tani ◽  
Toshiyuki Koizumi ◽  
Hisashi Sumikura ◽  
Mariko Yamaguchi ◽  
Kohji Yamamoto ◽  
...  
Keyword(s):  
Raman Scattering ◽  
Signal Detection ◽  
Vibrational Spectroscopy ◽  
Time Domain ◽  
Femtosecond Pulses ◽  
Anti Stokes ◽  
Scattering Signal

scholarly journals A Novel Anti-Noise Fault Diagnosis Approach for Rolling Bearings Based on Convolutional Neural Network Fusing Frequency Domain Feature Matching Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5532
Author(s):  
Xiangyu Zhou ◽  
Shanjun Mao ◽  
Mei Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Time Domain ◽  
Feature Matching ◽  
Evidence Theory ◽  
Noise Signal ◽  
Matching Algorithm ◽  
Noise Environment

The development of deep learning provides a new research method for fault diagnosis. However, in the industrial field, the labeled samples are insufficient and the noise interference is strong so that raw data obtained by the sensor are occupied with noise signal. It is difficult to recognize time-domain fault signals under the severe noise environment. In order to solve these problems, the convolutional neural network (CNN) fusing frequency domain feature matching algorithm (FDFM), called CNN-FDFM, is proposed in this paper. FDFM extracts key frequency features from signals in the frequency domain, which can maintain high accuracy in the case of strong noise and limited samples. CNN automatically extracts features from time-domain signals, and by using dropout to simulate noise input and increasing the size of the first-layer convolutional kernel, the anti-noise ability of the network is improved. Softmax with temperature parameter T and D-S evidence theory are used to fuse the two models. As FDFM and CNN can provide different diagnostic information in frequency domain, and time domain, respectively, the fused model CNN-FDFM achieves higher accuracy under severe noise environment. In the experiment, when a signal-to-noise ratio (SNR) drops to -10 dB, the diagnosis accuracy of CNN-FDFM still reaches 93.33%, higher than CNN’s accuracy of 45.43%. Besides, when SNR is greater than -6 dB, the accuracy of CNN-FDFM is higher than 99%.

scholarly journals Time Domain Signal Detection for MIMO OFDM

2010 ◽  
pp. 151-158
Author(s):  
SARALA PATCHALA ◽  
T. GNANA PRAKASH ◽  
Dr. S. V. SUBBA RAO ◽  
Dr. K. PADMA RAJU
Keyword(s):  
Signal Detection ◽  
Fading Channels ◽  
Time Domain ◽  
Communication Systems ◽  
Channel Length ◽  
System Model ◽  
Ofdm System ◽  
Channel Matrix ◽  
Time Domain Signal ◽  
Mimo Ofdm

The MIMO techniques with OFDM is regarded as a promising solution for increasing data rates, for wireless access qualities of future wireless local area networks, fourth generation wireless communication systems, and for high capacity, as well as better performance. Hence as part of continued research, in this paper an attempt is made to carry out modelling, analysis, channel matrix estimation, synchronization and simulation of MIMO-OFDM system. A time domain signal detection algorithm can be based on Second Order Statistics (SOS) proposed for MIMO-OFDM system over frequency selective fading channels. In this algorithm, an equalizer is first inserted to reduce the MIMO channels to ones with channel length shorter than or equal to the Cyclic Prefix (CP) length. A system model in which the ith received OFDM block left shifted by j samples introduced. MIMO OFDM system model which uses the equalizer can be designed using SOS of the received signal vector to cancel the most of the Inter Symbol Interference (ISI). The transmitted signals are then detected from the equalizer output. In the proposed algorithm, only 2P (P transmitted antennas / users in the MIMO-OFDM system) columns of the channel matrix need to be estimated and channel length estimation is unnecessary, which is an advantage over an existing algorithms. In addition, the proposed algorithm is applicable for irrespective of whether the channel length is shorter than, equal to or longer than the CP length. Simulation results verify the effectiveness of the proposed algorithm and shows that it out performs the existing one in all cases.

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