Hybrid realisation of an adaptive filter for real-time noise-cancelling applications

1979 ◽  
Vol 15 (21) ◽  
pp. 671
Author(s):  
T. Schwarz ◽  
D. Malah
Keyword(s):  
Real Time ◽  
Adaptive Filter ◽  
Noise Cancelling

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

A covariance shaping filtering method for tightly-coupled MIMU/GNSS of UAV

2019 ◽  
Vol 91 (10) ◽  
pp. 1257-1267 ◽  
Author(s):  
Bin Liu ◽  
Jiangtao Xu ◽  
Bangsheng Fu ◽  
Yong Hao ◽  
Tianyu An
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
Adaptive Filter ◽  
Dynamic Environments ◽  
Content Type ◽  
Filtering Method ◽  
Time Performance ◽  
Dual Channel ◽  
Tightly Coupled ◽  
Parallel Filter

Purpose Regarding the important roles of accuracy and robustness of tightly-coupled micro inertial measurement unit (MIMU)/global navigation satellite system (GNSS) for unmanned aerial vehicle (UAV). This study aims to explore the efficient method to improve the real-time performance of the sensors. Design/methodology/approach A covariance shaping adaptive Kalman filtering method is developed. For optimal performance of multiple gyros and accelerometers, a distribution coefficient of precision is defined and the data fusion least square method is applied with fault detection and identification using the singular value decomposition. A dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed. Findings Hardware-in-the-loop numerical simulation was adopted, the results indicate that the gain of the covariance shaping adaptive filter is self-tuning by changing covariance weighting factor, which is calculated by minimizing the cost function of Frobenius norm. With the improved method, the positioning accuracy with tightly-coupled MIMU/GNSS of the adaptive Kalman filter is increased obviously. Practical implications The method of covariance shaping adaptive Kalman filtering is efficient to improve the accuracy and robustness of tightly-coupled MIMU/GNSS for UAV in complex and dynamic environments and has great value for engineering applications. Originality/value A covariance shaping adaptive Kalman filtering method is presented and a novel dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed, to improve the real-time performance in complex and dynamic environments.

Smoothing spectral gamma logs: A simple but effective technique

Geophysics ◽  
1987 ◽  
Vol 52 (3) ◽  
pp. 363-367 ◽  
Author(s):  
Gary L. Mathis
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
Adaptive Filter ◽  
Gamma Ray ◽  
Error Function ◽  
The Other ◽  
Weighted Averaging ◽  
Effective Technique ◽  
Abrupt Changes ◽  
Counting Statistics

Spectral gamma‐ray logs suffer from unusually poor counting statistics because of the extremely low counting rates used to compute the concentrations of potassium (K), uranium (U), and thorium (T). Filtering is therefore a prerequisite to interpretation. Kalman filtering has been suggested, but this approach is complex and involves uncertain assumptions. Simple weighted averaging, on the other hand, fails to take into account abrupt changes that can occur in geologic response. Effective filtering of real logging data is possible, however, by a simple adaptive filter which uses the total gamma responses of a gamma‐ray tool to compute filter weights based on the error function. This filter is mathematically and computationally simple to implement for real time or postprocessing of spectral gamma logs.

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