Troposcatter transmission loss prediction based on particle swarm optimisation

Abstract

Tropospheric scatter is a promising method for over‐the‐horizon propagation. Transmission loss caused by the three mainstream troposcatter mechanisms is analysed, namely turbulent incoherent scattering theory, coherent reflection by stable layers theory, and incoherent reflection by irregular layers theory. Then an experiment is conducted to explore the relationships among the three mechanisms. Based on this experiment, the troposcatter transmission loss prediction model is established in different climate zones by a particle swarm optimisation algorithm and experimental data from the global troposcatter databank. The simulation shows that this model is more effective than the existing International Telecommunication Union‐Radiocommunication Sector (ITU‐R) P.617, P.452, and P.2001. Furthermore, by analysing the training parameters' proportion of the new model in different climate zones, the specific composition of three troposcatter mechanisms can be obtained.

Revisiting Enhanced AIS Detection Range under Anomalous Propagation Conditions

“…The troposcatter model [31] is a general purpose model that combines the two previous models. With the development of optimization methods, recently, more elaborated troposcatter models have been proposed, including one based on the genetic algorithm optimization of the problem parameters [32] and another based on particle swarm optimization in [33]. A review of recent studies on tropospheric scatter models may be found in [34].…”

A New Model for Estimating Troposcatter Loss and Delays Based on Ray-Tracing and Beam Splitting With ERA5

“…G t and G r can be obtained by the empirical formula G t,r = 10 lg 4.5(D/λ) 2 (22) where D is the antenna diameter. The beamwidth ω can be determined by antenna diameter and wavelength…”

“…For more information, see https://creativecommons.org/licenses/by/4.0/ the parabolic equation method proposed by Li et al [17], and the ray-based troposcatter channel model proposed by Dinc and Akan [18], [19]. Based on the above models, some progress has also been made in recent years to improve the loss estimation performance using optimization algorithms, such as in 2016, Li et al [20] used genetic algorithm combined with troposcatter link database to optimize the relevant to-be-determined scaling parameters and combined it with waveguide and layer reflection anomaly propagation model to obtain a new improved model, in 2018, Wei et al [21] derived a distribution model of fast and slow fading characteristics of troposcatter transmission loss based on the fast and slow fading characteristics of electric field strength and combined with the ITU-R P.617-3 model and proposed a transmission loss prediction method based on the transmission loss distribution model, and in 2021, Yuan and Chen [22] used the particle swarm algorithm combined with the troposcatter database to obtain the optimal proportional weights of the three propagation mechanisms in different climate regions for the situation that the three propagation mechanisms of troposcatter may exist simultaneously with different weights in different regions, and the loss prediction capability is better than the ITU recommended algorithm.…”