Improvement of the sensitivity of gas detection by signal processing method in frequency domain

2013 ◽  
Author(s):  
Xi Chen ◽  
Xiaopeng Dong
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Processing Method ◽  
Gas Detection ◽  
Signal Processing Method

A Broadband Array Signal Processing Method Based on Joint Sparsity of Signal Spatial Domain

2019 ◽  
Author(s):  
Daqian He ◽  
Dahai Zhang ◽  
Congying Wang ◽  
Xirui Peng
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Acoustic Signal ◽  
Array Signal Processing ◽  
Doa Estimation ◽  
Spatial Domain ◽  
Processing Method ◽  
Arrival Angle ◽  
Joint Sparsity ◽  
Signal Processing Method

Abstract Broadband underwater acoustic signal direction of arrival (DOA) estimation method is an important part of underwater array signal processing. The commonly used array signal DOA estimation algorithms due to the restriction of algorithm principles, are unable to process broadband array signal effectively, at the case of the arriving signals have strong correlation, small sampling snapshots or small arrival angle. Therefore, we need a new efficient algorithm to meet the increasing demand of broadband under water acoustic signal processing method. This paper makes use of the broadband acoustic signal similarity of joint sparsity in signal spatial domain received by underwater sonar arrays, establishes the whole space grid covering all broadband frequency domain slices. On the basis, the global sparsity of each frequency domain slice is combined with sparse element extraction class algorithm. By integrating the energy of signal on each slice, the spatial sparsity of each slice is obtained, from which we can get the directions of the arriving broadband wave signals. Through the simulation analysis and experimental verification on lake, we can be see that: The SDJS algorithm improves the performance and signal processing capability of the algorithm compared with the traditional algorithms. Therefore SDJS algorithm has a widely range of research value and application space.

Acceleration Signal Processing Based on EMD Adaptive Filtering and Frequency Domain Integration

2015 ◽  
Vol 719-720 ◽  
pp. 1038-1042
Author(s):  
Ping An Shi ◽  
Lei Wu
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Frequency Domain ◽  
Adaptive Filtering ◽  
High Frequency ◽  
Processing Method ◽  
Acceleration Signal ◽  
Signal Processing Method ◽  
Domain Integration ◽  
Better Than

In order to improve the accuracy of the phase and amplitude of acceleration integration results, a new method to transform acceleration signal into displacement was presented which combines Empirical Modal Decomposition (EMD) adaptive filtering with FFT based frequency domain integration. The acceleration signal is decomposed by EMD into n IMF, and by certain rules, the number of IMF pertaining high frequency (h) is determined, and the h IMF are adaptively filtered to cancel noises. After that, the FFT transform is applied to the n processed IMF, frequency domain integration is done, and finally the displacements time series is obtained by IFFT. Simulation shows that this acceleration signal processing method is better than the pure frequency domain integration transformation.

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