scholarly journals Angle of Arrival Estimation Using Cholesky Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Saleh O. Al-Jazzar
Keyword(s):  
Signal To Noise Ratio ◽  
Cholesky Decomposition ◽  
Eigenvalue Decomposition ◽  
Angle Of Arrival ◽  
Signal To Noise ◽  
Autocorrelation Matrix ◽  
Space Processing ◽  
Wireless Positioning ◽  
Esprit Algorithm ◽  
Processing Techniques

An angle of arrival (AOA) estimator is presented. Many applications require accurate AOA estimates such as wireless positioning and signal enhancement using space-processing techniques. The proposed AOA estimator depends on the Cholesky decomposition of the received signal autocorrelation matrix. The resultant decomposed matrices are used to modify the crosscorrelation matrix of the received signals at the antenna array doublets. The proposed method is named the Cholesky-decomposition-based-AOA (CDBA) estimator. In comparison with the TLS-ESPRIT algorithm which utilizes the eigenvalue decomposition (EVD) of the received signal autocorrelation and crosscorrelation matrices, the CDBA method has better performance than the TLS-ESPRIT algorithm especially in low signal-to-noise-ratio (SNR) cases. Simulations for the proposed CDBA method are shown to assess its performance.

scholarly journals Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3381 ◽  
Author(s):  
Shakeb Deane ◽  
Nicolas P. Avdelidis ◽  
Clemente Ibarra-Castanedo ◽  
Hai Zhang ◽  
Hamed Yazdani Nezhad ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Non Destructive Testing ◽  
Signal To Noise ◽  
Destructive Testing ◽  
Ir Camera ◽  
Thermal Cameras ◽  
Aerial Vehicle ◽  
Noise Ratio ◽  
Processing Techniques ◽  
Non Destructive

This work aims to address the effectiveness and challenges of non-destructive testing (NDT) by active infrared thermography (IRT) for the inspection of aerospace-grade composite samples and seeks to compare uncooled and cooled thermal cameras using the signal-to-noise ratio (SNR) as a performance parameter. It focuses on locating impact damages and optimising the results using several signal processing techniques. The work successfully compares both types of cameras using seven different SNR definitions, to understand if a lower-resolution uncooled IR camera can achieve an acceptable NDT standard. Due to most uncooled cameras being small, lightweight, and cheap, they are more accessible to use on an unmanned aerial vehicle (UAV). The concept of using a UAV for NDT on a composite wing is explored, and the UAV is also tracked using a localisation system to observe the exact movement in millimetres and how it affects the thermal data. It was observed that an NDT UAV can access difficult areas and, therefore, can be suggested for significant reduction of time and cost.

Integrated processing techniques to low signal-to-noise ratio OBC dual-sensor seismic data

2015 ◽  
Author(s):  
Baoqing Zhang* ◽  
Huawei Zhou ◽  
Zaiyu Ding ◽  
Ran Li ◽  
Zhaoquan He ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Dual Sensor ◽  
Integrated Processing ◽  
Noise Ratio ◽  
Processing Techniques

scholarly journals Agro Image De-Noising (Aid) for Enhanced Agricultural Images

2019 ◽  
Vol 9 (2) ◽  
pp. 2474-2478
Keyword(s):  
Image Processing ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Photographic Images ◽  
Spline Wavelets ◽  
Soft Threshold ◽  
Noise Ratio ◽  
Processing Techniques

Several Noises may be present in acquired images. This is an undesired feature for image processing techniques that analyze these images. Image de-noising helps improve efficiency of image processing. Many image de-noising methods have been proposed and exist in literature. Image de-noising methods for agricultural images have been proposed to a lesser extent when compared to the bright medical or photographic images. This paper proposes Agricultural Image De-noising (AID) which uses a discrete wavelet transform (DWT) to eliminate noise in agricultural images. This study uses specific kind of wavelet family spline wavelet transforms with appropriate decomposition level and the wavelet coefficients are analysed with hard and soft threshold methods. The denoised image using various spline wavelets is compared of hard threshold and soft threshold are assessed. The performance of AID is calculated using the peak signal to noise ratio (PSNR) and signal to noise ratio (SNR).

scholarly journals Experimental Results of a Differential Angle-of-Arrival Based 2D Localization Method Using Signals of Opportunity

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
M. A. Aziz ◽  
C. T. Allen
Keyword(s):  
Signal To Noise Ratio ◽  
Angle Of Arrival ◽  
Signal To Noise ◽  
Localization Accuracy ◽  
Localization Method ◽  
Precise Knowledge ◽  
Fm Radio ◽  
Signal Characteristics ◽  
Radio Signals ◽  
Signals Of Opportunity

This paper presents a study of differential AoA (Angle-of-Arrival) based 2D localization method utilizing FM radio signals (88 MHz–108 MHz) as Signals of Opportunity (SOP). Given prior knowledge of the transmitters’ position and signal characteristics, the proposed technique utilizes triangulation to localize receiver’s 2D position. Dual antenna interferometry provides the received signals’ AoA required for triangulation. Reliance on precise knowledge of antenna system’s orientation is removed by utilizing differential Angle of Arrivals (dAoAs). The 2D localization accuracy is improved by utilizing colocated transmitters, a concept proposed in this paper as supertowers. Analysis, simulation, and ground-based experiments have been presented; results showed that when the SNR (Signal-to-Noise Ratio) is higher than 45 dB, the proposed method localizes the receiver’s 2D position with an error of less than 15 m.

Signal-to-noise ratio estimation for DVB-S2 based on eigenvalue decomposition

2016 ◽  
Vol 10 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Hao Wu ◽  
Zhi-Tao Huang ◽  
Zhi-Chao Sha ◽  
Yi-Yu Zhou
Keyword(s):  
Signal To Noise Ratio ◽  
Eigenvalue Decomposition ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals A Low Complexity Subspace-Based DOA Estimation Algorithm with Uniform Linear Array Correlation Matrix Subsampling

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Do-Sik Yoo
Keyword(s):  
Linear Array ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Low Complexity ◽  
Eigenvalue Decomposition ◽  
Complex Eigenvalue ◽  
Uniform Linear Array ◽  
Autocorrelation Matrix ◽  
Value Decomposition

We propose a low complexity subspace-based direction-of-arrival (DOA) estimation algorithm employing a direct signal space construction method (DSPCM) by subsampling the autocorrelation matrix of a uniform linear array (ULA). Three major contributions of this paper are as follows. First of all, we introduce the method of autocorrelation matrix subsampling which enables us to employ a low complexity algorithm based on a ULA without computationally complex eigenvalue decomposition or singular-value decomposition. Secondly, we introduce a signal vector separation method to improve the distinguishability among signal vectors, which can greatly improve the performance, particularly, in low signal-to-noise ratio (SNR) regime. Thirdly, we provide a root finding (RF) method in addition to a spectral search (SS) method as the angle finding scheme. Through simulations, we illustrate that the performance of the proposed scheme is reasonably close to computationally much more expensive MUSIC- (MUltiple SIgnal Classification-) based algorithms. Finally, we illustrate that the computational complexity of the proposed scheme is reduced, in comparison with those of MUSIC-based schemes, by a factor ofO(N2/K), whereKis the number of sources andNis the number of antenna elements.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

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