scholarly journals A Gaussian mixture regression model based adaptive filter for non-Gaussian noise without a priori statistic

2021 ◽  
pp. 108314
Author(s):  
Haoran Cui ◽  
Xiaoxu Wang ◽  
Shuaihe Gao ◽  
Tiancheng Li
Keyword(s):  
Regression Model ◽  
Adaptive Filter ◽  
Gaussian Noise ◽  
A Priori ◽  
Gaussian Mixture ◽  
Mixture Regression ◽  
Model Based ◽  
Non Gaussian ◽  
Mixture Regression Model

scholarly journals Smoothing and Interpolating Noisy GPS Data with Smoothing Splines

2020 ◽  
Vol 37 (3) ◽  
pp. 449-465 ◽  
Author(s):  
Jeffrey J. Early ◽  
Adam M. Sykulski
Keyword(s):  
Gaussian Noise ◽  
A Priori ◽  
Smoothing Splines ◽  
Sampled Data ◽  
Trajectory Data ◽  
Tension Parameter ◽  
Comprehensive Method ◽  
Gps Trajectory Data ◽  
Non Gaussian ◽  
Irregularly Sampled Data

AbstractA comprehensive method is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen a priori from physical reasoning. We also show how to allow for non-Gaussian noise and outliers that are typical in global positioning system (GPS) signals. We demonstrate the effectiveness of our methods on GPS trajectory data obtained from oceanographic floating instruments known as drifters.

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.

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