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Nonlinear least square approach for range estimation based on attenuation of EM waves in seawater using world ocean data from 1955 to 2012
Author(s) -
Tahir Muhammad,
Yan Piao,
Jafri Mohsin Raza
Publication year - 2019
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4117
Subject(s) - attenuation , seawater , longitude , latitude , range (aeronautics) , nonlinear system , computational physics , physics , environmental science , geodesy , geology , statistics , mathematics , optics , materials science , oceanography , quantum mechanics , composite material
Summary In this research paper, we estimated absorption loss ( L α ) and spreading loss ( L o ) based on electromagnetic (EM) waves propagation, considering multiple seawater depth (1101) points from surface to 5500 m distributed uniformly with 5‐m difference. Estimation of parameters aforementioned was done based on world ocean data (WOD13) from the National Center for Environmental Information (NCEI) averaged between 1955 and 2012 along decades for basic parameters of seawater T ( C o ) and S (ppt) up to 5500‐m depth vertically and also across (41 088) latitude/longitude points for all oceans that includes Indian, Pacific, Southern, Atlantic, and Arctic horizontally. We also computed another important factor that contributes in overall path loss ( L U W ), loss due to polarization of EM fields between transmitter T x and receiver R x such as antenna polarization factor ( L ϕ ). L ϕ (dB) along with L α (dB) and L o (dB) by substituting into basic propagation model for EM waves in seawater helps us to predict efficiently accurate achievable range ( R e s t ) using nonlinear least square (NLLS) approximation combined with Lambert transformation considering nonlinear exponential P T (dBm) decay. Moreover, predicted R e s t (m) helps us to minimize mean error ( m e a n ( e ( t ))) by adapting to actual range R (m) using NLLS approach. Simulation tool MATLAB has been used for implementation of NLLS approach and performance analysis.