Nonlinear least square approach for range estimation based on attenuation of EM waves in seawater using world ocean data from 1955 to 2012

• Published in: International Journal of Communication Systems
• Main Authors: Tahir, Muhammad, Yan, Piao, Jafri, Mohsin Raza
• Other Authors: Ministry of Science and Technology of the People's Republic of China
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2019
• Subjects: Arctic; Pacific; Indian
• Online Access: http://dx.doi.org/10.1002/dac.4117; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fdac.4117; https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.4117

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.