Estimation of surface duct parameters using AIS data based on Bayesian inversion method

Automatic Identification System (AIS) operates within a very high frequency (VHF) maritime mobile band, which could be impacted by surface duct seriously. Utilizing AIS signals for atmospheric duct inversion is a relatively new technique in passive remote sensing. This study employs the Bayesian method to conduct inversion of maritime surface ducts based on AIS signals, categorizing surface ducts into surface-based and elevated-surface ducts. Initially, preliminary data on surface-based duct and elevated-surface duct parameters are acquired from historical sounding data, followed by correlation analysis and probability density distribution fitting of these surface duct parameters. Subsequently, the inversion method steps are provided, and using the same measured AIS data, the likelihood function and posterior probability are calculated by directly employing historical samples and samples generated using Latin Hypercube Sampling (LHS), to accomplish the estimation of surface duct parameters. Directly utilizing historical samples and LHS-generated samples for Bayesian inversion can consistently and accurately yield results with an elevated-surface duct, with the latter being slightly more accurate than the former. The root mean square errors for the AIS path loss, calculated using the inverted elevated-surface duct parameters, are 5.9 dB and 4.4 dB when compared with the measured data. In contrast, the error calculated from the inverted surface-based duct parameters is 21.9 dB.