scholarly journals Algorithm for preliminary processing of charge coupled devices array data based on the adaptive Wiener filter

Informatics ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 72-83
Author(s):  
Hl. S. Litvinovich ◽  
I. I. Bruchkouski
Keyword(s):  
Vegetation Index ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Wiener Filter ◽  
Spectral Brightness ◽  
Signal To Noise ◽  
Statistical Parameters ◽  
Charge Coupled Devices ◽  
Noise Ratio ◽  
Priori Information

The researcher should choose the modes of recording spectra which allow to achieve the highest accuracy of spectral measurements in remote sensing systems. When registering a signal from aircraft which provide maximum coverage of the studied area, it is important to obtain a signal with the maximum signal-to- noise ratio in a minimum time, since the accumulation of spectra samples for averaging is impossible. The paper presents the experimental results of determining the noise components (readout noise, photon, electronic shot, pattern noise) for a monochrome uncooled CCD-line detector Toshiba TCD1304DG (CCD – charge-coupled devices) with various conditions of spectrum registration: detector temperature, exposition. Obtained dependences of the noise components make it possible to estimate the noise level for well-known conditions of spectra registration. The algorithm for processing CCD data based on an adaptive Wiener filter is proposed to increase the signal-to-noise ratio by using a priori information about the statistical parameters of the noise components. Such approach has allowed to increase the signal-to-noise ratio of sky spectral brightness by 4–9 dB for exposure times. The practical application of the algorithm has reduced the uncertainty in the vegetation index NDVI by 1.5 times when recording the reflection spectra of vegetation from the aircraft in the nadir measurement geometry.

scholarly journals An SNR Estimation Technique Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1139 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Fenghua Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Intermediate Frequency ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Application Range ◽  
Noise Ratio ◽  
Priori Information

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.

scholarly journals A study of the performance of the MSR vegetation index, using probabilistic and geostatistical methods

2007 ◽  
Vol 40 (4) ◽  
pp. 2048
Author(s):  
Aim. G. Skianis ◽  
D. Vaiopoulos ◽  
K. Nikolakopoulos
Keyword(s):  
Vegetation Index ◽  
Signal To Noise Ratio ◽  
Mean Value ◽  
High Signal ◽  
Signal To Noise ◽  
Infrared Band ◽  
Geostatistical Methods ◽  
Noise Ratio ◽  
Good Contrast ◽  
Modified Soil

In the present paper is studied the effect of the MSR (Modified Soil Ratio) vegetation index on multispectral digital images, with the aid of probability theory and geostatistics. Using proper distributions to describe the histograms of the image at the red and infrared band zones, an analytical expression of the distribution g of the MSR values is deduced. The study of the behaviour of g shows that the ratio of the standard deviation to the mean value of the MSR image is higher than that of the NDVI vegetation index, which is quite often used. This means that the MSR vegetation index produces images with a good contrast. It was also observed that the MSR image has a better signal to noise ratio than that of the NDVI image. Finally, the autocorrelograms of the MSR and NDVI images showed that the tonality differences between adjacent pixels of the MSR image are slightly stronger than those of the NDVI image. The general conclusion is that the MSR vegetation index produces images with a good contrast and a high signal to noise ratio, which could aid in making a reliable mapping of the vegetation cover of the area under study

scholarly journals A PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fei Zhang ◽  
Zijing Zhang ◽  
Luxi Yang ◽  
Xinyu Zhang
Keyword(s):  
Prior Information ◽  
Likelihood Function ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Estimation Accuracy ◽  
Indoor Environments ◽  
Signal To Noise ◽  
Probability Hypothesis Density ◽  
Localization And Mapping ◽  
Noise Ratio

The Simultaneous Localization and Mapping (SLAM) method of mobile robots has the problem of low accuracy in complex environments with dense clutter and various map features, such as complex indoor environments and underwater environments. This problem is mainly embodied in estimating the location and number of feature points on the map and the position of the robot itself. In order to solve this problem, a new method based on the probability hypothesis density (PHD) SLAM is proposed in this paper, a PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information (AI-MBMI-PHD-SLAM). Firstly, this paper proposes a PHD-SLAM method based on amplitude information (AI-PHD-SLAM). The method uses the amplitude information of map features to obtain more precise map features. Then, the clutter likelihood function is used to improve the estimation accuracy of the feature map in the SLAM process. Meanwhile, this paper studies the performance of the PHD-SLAM method with the amplitude information under the condition of the known signal-to-noise ratio or the unknown signal-to-noise ratio. Secondly, aiming at the problem that PHD-SLAM lacks a priori information in the prediction stage, an AI-PHD-SLAM-based mixed birth map information method is added. In this method, map information that has been detected before the previous moment is added to the observation information in the map prediction phase as a new map information set in the prediction phase. This can increase the prior information and improve the problem of insufficient prior information in the prediction stage. The results of the experiments show that the proposed method and the improved method outperform the RB-PHD-SLAM method in estimating the number and location accuracy of map features and have higher computational efficiency.

Resolution performance of Wiener filters

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 887-899 ◽  
Author(s):  
S. H. Bickel ◽  
D. R. Martinez
Keyword(s):  
Time Constant ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Resolving Power ◽  
General Definition ◽  
Resolution Time ◽  
Signal To Noise ◽  
Temporal Separation ◽  
Noise Ratio

To improve the resolution of seismic events, one often designs a Wiener inverse filter that optimally (in the least‐squares sense) transforms a measured source signature into a spike. When this filter is applied to seismic data, the bandwidth of any noise which is present increases along with the bandwidth of the signal. Thus the signal‐to‐noise ratio is degraded. To reduce signal ambiguity it is common practice to prewhiten the Wiener filter. Prewhitening the filter improves the output signal‐to‐ambient noise ratio, but at the same time it reduces resolution. The ability to resolve the temporal separation between events is determined by the resolution time constant which we define as the ratio of signal energy to peak signal power from the filter. For unfiltered wavelets the resolution time constant becomes the reciprocal of resolving power recently described by Widess (1982). For matched filter signals the resolution time constant can be regarded as the inverse of the frequency span of the signal. Although it is satisfying that the resolution time constant definition agrees with other measures of resolution, this more general definition has two major advantages. First, it incorporates the effect of filtering; second, it is easily generalized to incorporate the effects of noise by assuming that the filter is a Wiener filter. For a given amount of noise the Wiener filter is a generalization of the matched filter. Marine seismic wavelets demonstrate how reducing the noise level improves the resolution of a Wiener filter relative to a matched filter. For these wavelets a point of diminishing return is reached, such that, to realize a further small increase in resolution, a large increase in input signal‐to‐noise ratio is required to maintain interpretable information at the output.

AN AUDITORY BRAIN-COMPUTER INTERFACE WITH ACCURACY PREDICTION

2012 ◽  
Vol 22 (03) ◽  
pp. 1250009 ◽  
Author(s):  
M. A. LOPEZ-GORDO ◽  
F. PELAYO ◽  
A. PRIETO ◽  
E. FERNANDEZ
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Signal To Noise ◽  
Computer Interfaces ◽  
Dichotic Listening Task ◽  
Shift Keying ◽  
Trial Basis ◽  
Noise Ratio ◽  
Listening Task ◽  
Binary Phase Shift Keying

Fully auditory Brain-computer interfaces based on the dichotic listening task (DL-BCIs) are suited for users unable to do any muscular movement, which includes gazing, exploration or coordination of their eyes looking for inputs in form of feedback, stimulation or visual support. However, one of their disadvantages, in contrast with the visual BCIs, is their lower performance that makes them not adequate in applications that require a high accuracy. To overcome this disadvantage, we employed a Bayesian approach in which the DL-BCI was modeled as a Binary phase shift keying receiver for which the accuracy can be estimated a priori as a function of the signal-to-noise ratio. The results showed the measured accuracy to match the predefined target accuracy, thus validating this model that made possible to estimate in advance the classification accuracy on a trial-by-trial basis. This constitutes a novel methodology in the design of fully auditory DL-BCIs that let us first, define the target accuracy for a specific application and second, classify when the signal-to-noise ratio guarantees that target accuracy.

A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation

2018 ◽  
Vol 18 (04) ◽  
pp. 1850023 ◽  
Author(s):  
Hadi Salehi ◽  
Javad Vahidi ◽  
Homayun Motameni
Keyword(s):  
Genetic Algorithm ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Bilateral Filter ◽  
Fuzzy Evaluation ◽  
Signal To Noise ◽  
Denoising Method ◽  
Final Model ◽  
Noise Ratio

In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulations and evaluations mentioned in this paper are accomplished on some standard images such as Lena, boy, fruit, mandrill, Barbara, butterfly, and boat. Next, weaker models are omitted by studying of the various models. Establishment of new generations performs in a form that a generation emendation is carried out, and final model has a more optimum quality compared to each two filters in order to obviate the noise. At the end, the results of this system are studied so that a comprehensive model with the best performance is to be found. Experiments show that the proposed method has better performance than wavelet, bilateral, Butterworth, and some other filters.

scholarly journals Automatic Modulation Recognition for MFSK Using Modified Covariance Method

2015 ◽  
Vol 5 (3) ◽  
pp. 429
Author(s):  
Hanan M.Hamee ◽  
Jafer Wadi
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Modulation Index ◽  
Modulation Classification ◽  
A Priori Information ◽  
Modulation Recognition ◽  
Digital Signals ◽  
Signal To Noise ◽  
Automatic Modulation Recognition ◽  
Priori Information

This paper presents modulation classification method capable of classifying<br />MFSK digital signals without a priori information using modified covariance<br />method. This method using for calculation features for FSK modulation<br />should have a good properties of sensitive with FSK modulation index and<br />insensitive with signal to noise ratio SNR variation. The numerical<br />simulations and investigation of the performance by the support vectors<br />machine one against all (SVM-OAA) as a classifier for classifying 6 digitally<br />modulated signals which gives probability of correction classification up to<br />85.85 at SNR=-15dB.

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