Direction Finding and Likelihood Ratio Detection for Oceanographic HF Radars

2021 ◽  
Author(s):  
Anthony Kirincich ◽  
Libe Washburn
Keyword(s):  
Likelihood Ratio ◽  
Correlation Coefficients ◽  
Likelihood Estimation ◽  
Radar Data ◽  
Hf Radar ◽  
Estimation Methods ◽  
Spatial Coverage ◽  
Similar Accuracy ◽  
The Cost ◽  
Improved Accuracy

Abstract Previous work with simulations of oceanographic HF radars has identified possible improvements when using Maximum Likelihood Estimation (MLE) for directional-of-arrival (DOA), however methods for determining the number of emitters (here defined as spatially distinct patches of the ocean surface) have not realized these improvements. Here we describe and evaluate the use of the Likelihood Ratio (LR) for emitter detection, demonstrating its application to oceanographic HF radar data. The combined detection-estimation methods MLE-LR are compared with MUSIC and MUSIC parameters for SeaSonde HF radars, along with a method developed for 8-channel systems known as MUSIC-Highest. Results show that the use of MLE-LR produces similar accuracy in terms of the RMS difference and correlation coefficients squared, as previous methods. We demonstrate that improved accuracy can be obtained for both methods, at the cost of fewer velocity observations and decreased spatial coverage. For SeaSondes, accuracy improvements are obtained with less commonly used parameter sets. The MLE-LR is shown to be able to resolve simultaneous closely spaced emitters, which has the potential to improve observations obtained by HF radars operating in complex current environments.

scholarly journals Gap Filling of the CALYPSO HF Radar Sea Surface Current Data through Past Measurements and Satellite Wind Observations

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Adam Gauci ◽  
Aldo Drago ◽  
John Abela
Keyword(s):  
Oil Spills ◽  
Surface Current ◽  
Radar Data ◽  
Current Data ◽  
Hf Radar ◽  
Sea Surface ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Spatial Coverage ◽  
Essential Components

High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.

scholarly journals Variability in the air–sea interaction patterns and timescales within the south-eastern Bay of Biscay, as observed by HF radar data

Ocean Science ◽  
2013 ◽  
Vol 9 (2) ◽  
pp. 399-410 ◽  
Author(s):  
A. Fontán ◽  
G. Esnaola ◽  
J. Sáenz ◽  
M. González
Keyword(s):  
Radar Data ◽  
Hf Radar ◽  
Bay Of Biscay ◽  
Interaction Patterns ◽  
The South ◽  
South Eastern ◽  
Continental Shelves ◽  
Surface Circulation ◽  
Spatial Coverage ◽  
Current Interaction

Abstract. Two high-frequency (HF) radar stations were installed on the coast of the south-eastern Bay of Biscay in 2009, providing high spatial and temporal resolution and large spatial coverage of currents in the area for the first time. This has made it possible to quantitatively assess the air–sea interaction patterns and timescales for the period 2009–2010. The analysis was conducted using the Barnett–Preisendorfer approach to canonical correlation analysis (CCA) of reanalysis surface winds and HF radar-derived surface currents. The CCA yields two canonical patterns: the first wind–current interaction pattern corresponds to the classical Ekman drift at the sea surface, whilst the second describes an anticyclonic/cyclonic surface circulation. The results obtained demonstrate that local winds play an important role in driving the upper water circulation. The wind–current interaction timescales are mainly related to diurnal breezes and synoptic variability. In particular, the breezes force diurnal currents in waters of the continental shelf and slope of the south-eastern Bay. It is concluded that the breezes may force diurnal currents over considerably wider areas than that covered by the HF radar, considering that the northern and southern continental shelves of the Bay exhibit stronger diurnal than annual wind amplitudes.

scholarly journals Regional Assessments of Surface Ice Elevations from Swath-Processed CryoSat-2 SARIn Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2213
Author(s):  
Natalia Havelund Andersen ◽  
Sebastian Bjerregaard Simonsen ◽  
Mai Winstrup ◽  
Johan Nilsson ◽  
Louise Sandberg Sørensen
Keyword(s):  
Radar Data ◽  
The Arctic ◽  
Validation Data ◽  
Sea Levels ◽  
Ice Surface ◽  
Spatial Coverage ◽  
Elevation Data ◽  
The Stability ◽  
Surface Ice ◽  
The Cost

The Arctic responds rapidly to climate change, and the melting of land ice is a major contributor to the observed present-day sea-level rise. The coastal regions of these ice-covered areas are showing the most dramatic changes in the form of widespread thinning. Therefore, it is vital to improve the monitoring of these areas to help us better understand their contribution to present-day sea levels. In this study, we derive ice-surface elevations from the swath processing of CryoSat-2 SARIn data, and evaluate the results in several Arctic regions. In contrast to the conventional retracking of radar data, swath processing greatly enhances spatial coverage as it uses the majority of information in the radar waveform to create a swath of elevation measurements. However, detailed validation procedures for swath-processed data are important to assess the performance of the method. Therefore, a range of validation activities were carried out to evaluate the performance of the swath processor in four different regions in the Arctic. We assessed accuracy by investigating both intramission crossover elevation differences, and comparisons to independent elevation data. The validation data consisted of both air- and spaceborne laser altimetry, and airborne X-band radar data. There were varying elevation biases between CryoSat-2 and the validation datasets. The best agreement was found for CryoSat-2 and ICESat-2 over the Helheim region in June 2019. To test the stability of the swath processor, we applied two different coherence thresholds. The number of data points was increased by approximately 25% when decreasing the coherence threshold in the processor from 0.8 to 0.6. However, depending on the region, this came with the cost of an increase of 33–65% in standard deviation of the intramission differences. Our study highlights the importance of selecting an appropriate coherence threshold for the swath processor. Coherence threshold should be chosen on a case-specific basis depending on the need for enhanced spatial coverage or accuracy.

scholarly journals Variability in the air–sea interaction patterns and time-scales within the Southeastern Bay of Biscay, as observed by HF radar data

2012 ◽  
Vol 9 (4) ◽  
pp. 2793-2815
Author(s):  
A. Fontán ◽  
G. Esnaola ◽  
J. Sáenz ◽  
M. González
Keyword(s):  
Time Scales ◽  
Radar Data ◽  
Hf Radar ◽  
Bay Of Biscay ◽  
Interaction Patterns ◽  
Synoptic Variability ◽  
Continental Shelves ◽  
Surface Circulation ◽  
Spatial Coverage ◽  
Current Interaction

Abstract. Two high frequency (HF) radar stations were installed on the Southeastern Bay of Biscay in 2009, providing high spatial and temporal resolution and large spatial coverage currents for the first time in the area. This has enabled to determine quantitatively the air–sea interaction patterns and time-scales for the period 2009–2010. The analysis was conducted by using the Barnett-Preisendorfer approach to canonical correlation analysis (CCA) of reanalysis surface winds and HF radar-derived currents. The results reveal that the CCA yields two canonical patterns. The first wind-current interaction pattern corresponds to the classical Ekman drift at sea surface, whilst the second describes an anticyclonic/cyclonic surface circulation. The results obtained demonstrate that the local winds play an important role in driving the upper water circulation. The wind-current interaction time-scales are mainly related to diurnal breezes and synoptic variability. In particular, the breezes force diurnal currents in the continental shelf and slope of the Southeastern Bay. It is concluded that the breezes may force diurnal currents over considerably wider areas than that covered by the HF radar, considering that the northern and southern continental shelves of the Bay exhibit stronger diurnal than annual wind amplitudes.

scholarly journals An Analytical EM Algorithm for Sub-Gaussian Vectors

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 945
Author(s):  
Audrius Kabašinskas ◽  
Leonidas Sakalauskas ◽  
Ingrida Vaičiulytė
Keyword(s):  
Em Algorithm ◽  
Likelihood Ratio ◽  
Goodness Of Fit ◽  
Stable Distribution ◽  
Likelihood Estimation ◽  
Estimation Methods ◽  
Normal Vector ◽  
Ratio Test ◽  
Estimation Of Parameters ◽  
Goodness Of Fit Test

The area in which a multivariate α-stable distribution could be applied is vast; however, a lack of parameter estimation methods and theoretical limitations diminish its potential. Traditionally, the maximum likelihood estimation of parameters has been considered using a representation of the multivariate stable vector through a multivariate normal vector and an α-stable subordinator. This paper introduces an analytical expectation maximization (EM) algorithm for the estimation of parameters of symmetric multivariate α-stable random variables. Our numerical results show that the convergence of the proposed algorithm is much faster than that of existing algorithms. Moreover, the likelihood ratio (goodness-of-fit) test for a multivariate α-stable distribution was implemented. Empirical examples with simulated and real world (stocks, AIS and cryptocurrencies) data showed that the likelihood ratio test can be useful for assessing goodness-of-fit.

scholarly journals NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

2021 ◽  
Author(s):  
Anne Krogh Nøhr ◽  
Kristian Hanghøj ◽  
Genis Garcia Erill ◽  
Zilong Li ◽  
Ida Moltke ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Research ◽  
Likelihood Estimation ◽  
Software Tool ◽  
Estimation Methods ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

scholarly journals A New Inverted Topp-Leone Distribution: Applications to the COVID-19 Mortality Rate in Two Different Countries

Axioms ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 25 ◽  
Author(s):  
Ehab Almetwally ◽  
Randa Alharbi ◽  
Dalia Alnagar ◽  
Eslam Hafez
Keyword(s):  
Statistical Model ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Likelihood Estimation ◽  
Linear Representation ◽  
Rate Function ◽  
Estimation Methods ◽  
Unknown Parameters ◽  
Least Squares Estimators ◽  
New Distribution

This paper aims to find a statistical model for the COVID-19 spread in the United Kingdom and Canada. We used an efficient and superior model for fitting the COVID 19 mortality rates in these countries by specifying an optimal statistical model. A new lifetime distribution with two-parameter is introduced by a combination of inverted Topp-Leone distribution and modified Kies family to produce the modified Kies inverted Topp-Leone (MKITL) distribution, which covers a lot of application that both the traditional inverted Topp-Leone and the modified Kies provide poor fitting for them. This new distribution has many valuable properties as simple linear representation, hazard rate function, and moment function. We made several methods of estimations as maximum likelihood estimation, least squares estimators, weighted least-squares estimators, maximum product spacing, Crame´r-von Mises estimators, and Anderson-Darling estimators methods are applied to estimate the unknown parameters of MKITL distribution. A numerical result of the Monte Carlo simulation is obtained to assess the use of estimation methods. also, we applied different data sets to the new distribution to assess its performance in modeling data.

Spatio-Temporal Deep Learning for Ocean Current Prediction Based on HF Radar Data

2019 ◽  
Author(s):  
Nathachai Thongniran ◽  
Peerapon Vateekul ◽  
Kulsawasd Jitkajornwanich ◽  
Siam Lawawirojwong ◽  
Panu Srestasathiern
Keyword(s):  
Deep Learning ◽  
Radar Data ◽  
Hf Radar ◽  
Ocean Current ◽  
Spatio Temporal

scholarly journals A Distribution for Instantaneous Failures

Stats ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 247-258 ◽  
Author(s):  
Pedro L. Ramos ◽  
Francisco Louzada
Keyword(s):  
Residual Life ◽  
Likelihood Estimation ◽  
Estimation Methods ◽  
Mean Residual Life ◽  
Parameter Distribution ◽  
Coverage Probabilities ◽  
Instantaneous Failures ◽  
The Relationship ◽  
Mean Variance ◽  
New Distribution

A new one-parameter distribution is proposed in this paper. The new distribution allows for the occurrence of instantaneous failures (inliers) that are natural in many areas. Closed-form expressions are obtained for the moments, mean, variance, a coefficient of variation, skewness, kurtosis, and mean residual life. The relationship between the new distribution with the exponential and Lindley distributions is presented. The new distribution can be viewed as a combination of a reparametrized version of the Zakerzadeh and Dolati distribution with a particular case of the gamma model and the occurrence of zero value. The parameter estimation is discussed under the method of moments and the maximum likelihood estimation. A simulation study is performed to verify the efficiency of both estimation methods by computing the bias, mean squared errors, and coverage probabilities. The superiority of the proposed distribution and some of its concurrent distributions are tested by analyzing four real lifetime datasets.

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