scholarly journals A signal adaptive array interpolation approach with reduced transformation bias for DOA estimation of highly correlated signals

2014 ◽  
Author(s):  
Marco A. M. Marinho ◽  
Felix Antreich ◽  
Joao Paulo. C. L. da Costa ◽  
Josef A. Nossek
Keyword(s):  
Doa Estimation ◽  
Adaptive Array ◽  
Highly Correlated ◽  
Transformation Bias ◽  
Correlated Signals

DoA estimation of highly correlated stochastic sources using neural model

2018 ◽  
Vol 38 (8) ◽  
pp. 500-516
Author(s):  
Zoran Stanković ◽  
Nebojsa Dončov ◽  
Ivan Milovanović ◽  
Bratislav Milovanović
Keyword(s):  
Neural Model ◽  
Doa Estimation ◽  
Highly Correlated ◽  
Stochastic Sources

scholarly journals Uncertainty Characterization and Propagation in the Community Long-Term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS)

2019 ◽  
Vol 11 (10) ◽  
pp. 1227 ◽  
Author(s):  
Nadia Smith ◽  
Christopher D. Barnet
Keyword(s):  
Uncertainty Propagation ◽  
State Variables ◽  
Product System ◽  
Atmospheric Infrared Sounder ◽  
Cloud Properties ◽  
Infrared Measurements ◽  
Highly Correlated ◽  
Cross Track ◽  
Correlated Signals

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves multiple Essential Climate Variables (ECV) about the vertical atmosphere from hyperspectral infrared measurements made by the Atmospheric InfraRed Sounder (AIRS, 2002–present) and its successor, the Cross-track Infrared Sounder (CrIS, 2011–present). CLIMCAPS ECVs are profiles of temperature and water vapor, column amounts of greenhouse gases (CO2, CH4), ozone (O3) and precursor gases (CO, SO2) as well as cloud properties. AIRS (and CrIS) spectral measurements are highly correlated signals of many atmospheric state variables. CLIMCAPS inverts an AIRS (and CrIS) measurement into a set of discrete ECVs by employing a sequential Bayesian approach in which scene-dependent uncertainty is rigorously propagated. This not only linearizes the inversion problem but explicitly accounts for spectral interference from other state variables so that the correlation among ECVs (and their uncertainty) may be minimized. Here, we outline the CLIMCAPS retrieval methodology with specific focus given to its sequential scene-dependent uncertainty propagation system. We conclude by demonstrating continuity in two CLIMCAPS ECVs across AIRS and CrIS so that a long-term data record may be generated to study the feedback cycles characterizing our climate system.

scholarly journals Robust Nonlinear Array Interpolation for Direction of Arrival Estimation of Highly Correlated Signals

2018 ◽  
Vol 144 ◽  
pp. 19-28 ◽  
Author(s):  
Marco A.M. Marinho ◽  
Felix Antreich ◽  
Stefano Caizzone ◽  
João Paulo C.L. da Costa ◽  
Alexey Vinel ◽  
...  
Keyword(s):  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Highly Correlated ◽  
Correlated Signals

scholarly journals Finite Sample Correlations of Quantized Gaussians

1968 ◽  
Vol 21 (3) ◽  
pp. 273 ◽  
Author(s):  
T Cole
Keyword(s):  
Finite Length ◽  
Finite Sample ◽  
Record Length ◽  
Continuous Multiplication ◽  
Gaussian Statistics ◽  
Highly Correlated ◽  
The One ◽  
Correlated Signals

A theory is developed for the treatment of quantized signals and for the estimate of correlation from quantized signals of only finite length. Different quantization systems can be compared on the accuracy of estimation of correlation for the same record length. For Gaussian statistics it is shown that with low values of correlation the efficiencies of one, two, and three bit quantizations are respectively 64, 89, and 95% with respect to continuous multiplication. For highly correlated signals the one bit system becomes the most efficient

scholarly journals DOA Estimation for Highly Correlated and Coherent Multipath Signals with Ultralow SNRs

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Li Cheng ◽  
Yang Li ◽  
Lianying Zou ◽  
Yong Qin
Keyword(s):  
Multipath Propagation ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Doa Estimation ◽  
Time Shift ◽  
Direct Path ◽  
Multipath Signal ◽  
Highly Correlated ◽  
Multipath Signals ◽  
Cramer Rao Bound

In a typical multipath propagation environment, there exists a strong direct path signal accompanying with several weak multipath signals. Due to the strong direct path interference and other masking effects, the Direction-of-Arrival (DOA) of a weak multipath signal is hard to be estimated. In this paper, a novel method is proposed to estimate the DOA of multipath signals with ultralow signal-to-noise ratio (SNR). The main idea is to increase the SNR and signal-to-interference ratio (SIR) of the desired multipath signal in time-delay domain before DOA estimation processing. Firstly, the cross-correlation functions of the direct path signal and the received array signal are calculated. Then, they are combined and constructed to an enhanced array signal. Under certain conditions, the SNR and SIR of the desired signal can be significantly increased. Finally, the DOAs of multipath signals can be estimated by conventional technologies, and the associated time delays can be measured on the DOA-time-shift map. The SNR and SIR gains of the desired signal are analyzed theoretically, and theoretical analysis also indicates that the Cramer–Rao bound can be reduced. Simulation examples are presented to verify the advantages of the proposed method.

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