Minimum variance matched‐field source localization with SACLANT Mediterranean sea trial data

1995 ◽  
Vol 97 (5) ◽  
pp. 3291-3291
Author(s):  
Jeffrey L. Krolik
Keyword(s):  
Mediterranean Sea ◽  
Source Localization ◽  
Minimum Variance ◽  
Trial Data ◽  
Field Source ◽  
Sea Trial

Statistical analysis of achievable resolution limit in the near field source localization context

2012 ◽  
Vol 92 (2) ◽  
pp. 547-552 ◽  
Author(s):  
Mohammed Nabil El Korso ◽  
Rémy Boyer ◽  
Alexandre Renaux ◽  
Sylvie Marcos
Keyword(s):  
Statistical Analysis ◽  
Source Localization ◽  
Near Field ◽  
Resolution Limit ◽  
Field Source

scholarly journals The Estimation of Angular Misalignments for Ultra Short Baseline Navigation Systems. Part II: Experimental Results

2013 ◽  
Vol 66 (5) ◽  
pp. 773-787 ◽  
Author(s):  
Hsin-Hung Chen
Keyword(s):  
Trial Data ◽  
Experimental Results ◽  
Experimental Result ◽  
Alignment Error ◽  
Navigation Systems ◽  
Analytical Prediction ◽  
Short Baseline ◽  
Sea Trial ◽  
Alignment Errors ◽  
Cross Track

An algorithm of alignment calibration for Ultra Short Baseline (USBL) navigation systems was presented in the companion work (Part I). In this part (Part II) of the paper, this algorithm is tested on the sea trial data collected from USBL line surveys. In particular, the solutions to two practical problems referred to as heading deviation and cross-track error in the USBL line survey are presented. A field experiment running eight line surveys was conducted to collect USBL positioning data. The numerical results for the sea trial data demonstrated that the proposed algorithm could robustly and effectively estimate the alignment errors. Comparisons of the experimental result with the analytical prediction of roll misalignment estimation in Part I is drawn, showing good agreement. The experimental results also show that an inappropriate estimation of roll alignment error will significantly degrade the quality of estimations of heading and pitch alignment errors.

An Algorithm of Turbulence Wavenumber Spectrum Matching Based on SVM

2013 ◽  
Vol 850-851 ◽  
pp. 880-883
Author(s):  
Yong Fang Wang ◽  
Xin Luan ◽  
Da Lei Song ◽  
Li Ping Chen
Keyword(s):  
Cross Validation ◽  
Learning Algorithm ◽  
Trial Data ◽  
Observation Data ◽  
Matching Accuracy ◽  
Sea Trial ◽  
Optimum Parameters ◽  
Category Labels ◽  
Wavenumber Spectrum ◽  
Spectrum Matching

Considering the problem of invalid data caused mismatch of wavenumber spectrum which contained in turbulence observation data, an algorithm of turbulent wavenumber spectrum matching based on SVM is proposed. Category labels are obtained from pre-processed raw data by cross validation algorithm, and then the optimum parameters of the classifier are got through SVM learning algorithm. Sea trial data validation results indicate that the algorithm has high matching accuracy, and provides a new way to calculate the turbulence wavenumber spectrum matching.

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