scholarly journals Robust Noise Suppression Technique for a LADAR System via Eigenvalue-Based Adaptive Filtering

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2311
Author(s):  
Xianzhao Xia ◽  
Rui Chen ◽  
Pinquan Wang ◽  
Yiqiang Zhao
Keyword(s):  
Adaptive Filtering ◽  
Noise Level ◽  
Noise Suppression ◽  
Simulation Analysis ◽  
Asymptotic Properties ◽  
Estimation Method ◽  
System Stability ◽  
Noise Level Estimation ◽  
Sample Covariance ◽  
Guided Filtering

The laser detection and ranging system (LADAR) is widely used in various fields that require 3D measurement, detection, and modeling. In order to improve the system stability and ranging accuracy, it is necessary to obtain the complete waveform of pulses that contain target information. Due to the inevitable noise, there are distinct deviations between the actual and expected waveforms, so noise suppression is essential. To achieve the best effect, the filters’ parameters that are usually set as empirical values should be adaptively adjusted according to the different noise levels. Therefore, we propose a novel noise suppression method for the LADAR system via eigenvalue-based adaptive filtering. Firstly, an efficient noise level estimation method is developed. The distributions of the eigenvalues of the sample covariance matrix are analyzed statistically after one-dimensional echo data are transformed into matrix format. Based on the boundedness and asymptotic properties of the noise eigenvalue spectrum, an estimation method for noise variances in high dimensional settings is proposed. Secondly, based on the estimated noise level, an adaptive guided filtering algorithm is designed within the gradient domain. The optimized parameters of the guided filtering are set according to an estimated noise level. Through simulation analysis and testing experiments on echo waves, it is proven that our algorithm can suppress the noise reliably and has advantages over the existing relevant methods.

Large Noise Level Estimation

2003 ◽  
Vol 13 (08) ◽  
pp. 2309-2313 ◽  
Author(s):  
Alexandros Leontitsis ◽  
Jenny Pange ◽  
Tassos Bountis
Keyword(s):  
Time Series ◽  
Noise Level ◽  
Gaussian White Noise ◽  
Estimation Method ◽  
Noise Estimation ◽  
Accurate Estimation ◽  
Error Estimator ◽  
Correlation Integral ◽  
Noise Level Estimation ◽  
Curvature Effects

We generalize a method of noise estimation for chaotic time series due to [Schreiber, 1993] in cases where the noise level is relatively large. The noise estimation is based on the correlation integral, which, for small amounts of noise, is not affected by the attractor's curvature effects. When the noise is large, however, one has to increase the range of the correlation integral and this brings about significant inaccuracies in its evaluation due to both curvature effects and noise. In this Letter, we present a modification of Schreiber's noise level estimation method, which uses a robust error estimator based on L -∞ (rather than the usual L 2) norm in the computations. Since L -∞ was proved less sensitive to curvature effects, it gives a more accurate estimation of the noise standard deviation compared with Schreiber's results. Here, we illustrate our approach on the Hénon map corrupted by Gaussian white noise with zero mean, as well as on real data obtained from the Nasdaq Composite time series of daily returns.

Noise level estimation method with application to EMD-based signal denoising

2016 ◽  
Vol 27 (4) ◽  
pp. 763-771 ◽  
Author(s):  
Li Xiaoyu ◽  
◽  
Jin Jing ◽  
Shen Yi ◽  
Liu Yipeng ◽  
...  
Keyword(s):  
Noise Level ◽  
Estimation Method ◽  
Signal Denoising ◽  
Noise Level Estimation

An Absolute Phase Estimation Method for Interferometry Imagery

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim
Keyword(s):  
Noise Suppression ◽  
Estimation Method ◽  
Phase Image ◽  
Phase Estimation ◽  
Phase Reconstruction ◽  
Estimation Algorithms ◽  
Absolute Phase ◽  
Filtering Rate ◽  
Interferometric Phase ◽  
Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

scholarly journals Adaptive Air-Fuel Ratio Regulation for Port-Injected Spark-Ignited Engines Based on a Generalized Predictive Control Method

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 173 ◽  
Author(s):  
Lei Meng ◽  
Xiaofeng Wang ◽  
Chunnian Zeng ◽  
Jie Luo
Keyword(s):  
Predictive Control ◽  
Noise Suppression ◽  
Control Method ◽  
Simulation Analysis ◽  
Catalytic Converter ◽  
Exhaust Emission ◽  
Generalized Predictive Control ◽  
Si Engine ◽  
Fuel Ratio ◽  
Wall Wetting

The accurate air-fuel ratio (AFR) control is crucial for the exhaust emission reduction based on the three-way catalytic converter in the spark ignition (SI) engine. The difficulties in transient cylinder air mass flow measurement, the existing fuel mass wall-wetting phenomenon, and the unfixed AFR path dynamic variations make the design of the AFR controller a challenging task. In this paper, an adaptive AFR regulation controller is designed using the feedforward and feedback control scheme based on the dynamical modelling of the AFR path. The generalized predictive control method is proposed to solve the problems of inherent nonlinearities, time delays, parameter variations, and uncertainties in the AFR closed loop. The simulation analysis is investigated for the effectiveness of noise suppression, online prediction, and self-correction on the SI engine system. Moreover, the experimental verification shows an acceptable performance of the designed controller and the potential usage of the generalized predictive control in AFR regulation application.

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