Transient Signal Detection in Non-Gaussian Noise Using Stochastic Resonance and Wavelet Domain Power-Law Detector

2014 ◽  
Vol 945-949 ◽  
pp. 2043-2047
Author(s):  
Hua Yu Dong ◽  
Zhi Yang
Keyword(s):  
Stochastic Resonance ◽  
Power Law ◽  
Gaussian Noise ◽  
Mixture Distribution ◽  
Resonance Method ◽  
Gaussian Mixture ◽  
Transient Signal ◽  
Wavelet Domain ◽  
The Matrix ◽  
Non Gaussian

The detection of weak transient signal buried in non-Gaussian noise is investigated. Non-Gaussian noise is modeled by Gaussian mixture distribution. 3-level quantizer is used as a nondynamic stochastic resonance method to enhance SNR of weak signal. NL-length samples of signal are arranged into a matrix. Every column of the matrix is calculated into M-level decomposition. Based on the squared value of the detail and approximation coefficients, a novel Power-Law detector in wavelet domain is established. Numerical experiments and comparison show that, on the same SNR and false alarm rate, proposed method could provide higher detection probability.

scholarly journals Rate-compatible LDPC convolutional codes over non-gaussian noise channel

2020 ◽  
Vol 309 ◽  
pp. 01010
Author(s):  
Qiang Liu
Keyword(s):  
Gaussian Noise ◽  
Iterative Decoding ◽  
Belief Propagation ◽  
Additive White Gaussian Noise ◽  
Convolutional Codes ◽  
Gaussian Mixture ◽  
Performance Study ◽  
Shift Keying ◽  
Non Gaussian ◽  
The Impact

This paper is aimed to study the characteristics of the underwater acoustic channel with non-Gaussian noise channel. And Gaussian mixture model (GMM) is utilized to fit the background noise over the non-Gaussian noise channel. Furthermore, coding techniques which use a sequence of rate-compatible low-density parity-check (RC-LDPC) convolutional codes with separate rates are constructed based on graph extension method. The performance study of RC-LDPC convolutional codes over non-Gaussian noise channel and the additive white Gaussian noise (AWGN) channel is performed. Study implementation of simulation is that modulation with binary phase shift keying (BPSK), and iterative decoding based on pipeline log-likelihood rate belief propagation (LLRBP) algorithm. Finally, it is shown that RC-LDPC convolutional codes have good bit-rate-error (BER) performance and can effectively reduce the impact of noise.

scholarly journals Errata: Estimation of non-Gaussian noise parameters in the wavelet domain using the moment-generating function

2012 ◽  
Vol 21 (3) ◽  
pp. 039802-1
Author(s):  
Jan Švihlík ◽  
Karel Fliegel ◽  
Jaromír Kukal ◽  
Eva Jerhotová ◽  
Petr Páta ◽  
...  
Keyword(s):  
Generating Function ◽  
Gaussian Noise ◽  
Moment Generating Function ◽  
Wavelet Domain ◽  
Noise Parameters ◽  
The Moment ◽  
Non Gaussian

scholarly journals Stochastic resonance in FHN neural system driven by non-Gaussian noise

2012 ◽  
Vol 61 (13) ◽  
pp. 130502
Author(s):  
Zhang Jing-Jing ◽  
Jin Yan-Fei
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Neural System ◽  
Non Gaussian

scholarly journals Underwater surveillance in non-Gaussian noisy environment

2020 ◽  
Vol 53 (1-2) ◽  
pp. 250-261
Author(s):  
B Omkar Lakshmi Jagan ◽  
S Koteswara Rao
Keyword(s):  
Kalman Filter ◽  
Lower Bound ◽  
Extended Kalman Filter ◽  
Gaussian Noise ◽  
Unscented Kalman Filter ◽  
Doppler Frequency ◽  
Gaussian Mixture ◽  
Noisy Environment ◽  
Sampled Measurements ◽  
Non Gaussian

The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly throughout the process and (b) repeatedly at some particular time samples. The efficiency of these filters with the increase in non-Gaussian noise samples is discussed in this paper. The performance of filters is compared with that of Cramer-Rao Lower Bound. Doppler-bearing extended Kalman filter and Doppler-bearing unscented Kalman filter are chosen for this work.

STOCHASTIC RESONANCE IN BISTABLE AND EXCITABLE SYSTEMS: EFFECT OF NON-GAUSSIAN NOISES

2003 ◽  
Vol 03 (04) ◽  
pp. L365-L371 ◽  
Author(s):  
M. A. FUENTES ◽  
C. J. TESSONE ◽  
H. S. WIO ◽  
R. TORAL
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Excitable System ◽  
Ratio Curve ◽  
Bistable Systems ◽  
Excitable Systems ◽  
Noise Ratio ◽  
Non Gaussian

We analyze stochastic resonance in systems driven by non-Gaussian noises. For the bistable double well we compare the signal-to-noise ratio resulting from numerical simulations with some quasi-analytical results predicted by a consistent Markovian approximation in the case of a colored non-Gaussian noise. We also study the FitzHugh–Nagumo excitable system in the presence of the same noise. In both systems, we find that, as the noise departs from Gaussian behavior, there is a regime (different for the excitable and the bistable systems) in which there is a notable robustness against noise tuning since the signal-to-noise ratio curve broadens and becomes less sensitive to the actual value of the noise intensity. We also compare our results with some experiments in sensory systems.

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