Atmospheric Refractivity Estimation Research Articles

Atmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networks

Atmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networks

Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

Abstract This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

Estimation of surface-based duct parameters from automatic identification system using the Levy flight quantum-behaved particle swarm optimization algorithm

Atmospheric duct propagation has a significant influence on the performance of wireless communication and radar systems in the maritime environment that makes it essential to master the detailed knowledge of the atmospheric refractivity profile. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and the propagation path of AIS signals will be influenced by different atmospheric conditions. In this paper, a new refractivity estimation method based on the AIS signal level is proposed and the Levy flight quantum-behaved particle swarm optimization (LFQPSO) algorithm is presented and applied in the new estimation method to estimate the surface based duct parameters. Numerical simulations demonstrate that the LFQPSO algorithmhas good robustness and the new refractivity estimation method based on the AIS signal level can provide near-real-time estimation ofatmospheric refractivity.

Deep learning for solving inversion problem of atmospheric refractivity estimation

Atmospheric ducts are typical occurrences in marine environments. They can trap electromagnetic waves in ducts layer and extend propagation ranges significantly. Thus, affecting systems such as radar communications. Because the direct measurement of the atmospheric ducts refractivity has a certain degree of difficulty, the radar sea clutter power, which is easily measured, is used to solve the inversion problem of atmospheric refractivity estimation. In this study, based on the refractivity profile of the evaporation duct and the surface based duct, combined with the deep learning, we established a network mapping model between the sea clutter and the refractivity profile parameters. The model is applied to the inversion problem of atmospheric refractivity estimation, and the inversion results are analyzed to verify the feasibility of deep learning in the inversion problem. We herein report the high-precision inversion results of the atmospheric refractivity estimation.

A novel hybrid model for inversion problem of atmospheric refractivity estimation

Atmospheric refractivity estimation is an important issue for performance evaluation of communication systems and air surveillance radars. A novel hybrid model based on artificial neural networks (ANNs) and genetic algorithms (GAs) for inversion problem of atmospheric refractivity estimation is introduced. In this paper, inversion problem and clutter model problem of refractivity from clutter (RFC) method are separated and only inversion problem is studied. A problem specific ANN structure is designed and an original GA is developed to fulfill atmospheric refractivity estimations. In hybrid method, ANNs make pre-estimation and GAs use these results as a starting population for post-estimation. When the results obtained from the single solutions of ANNs and GAs are compared to the results obtained from hybrid model, a significant improvement in the accuracy of estimated results is observed.

Remote sensing of atmospheric duct parameters using simulated annealing

Simulated annealing is one of the robust optimization schemes. Simulated annealing mimics the annealing process of the slow cooling of a heated metal to reach a stable minimum energy state. In this paper, we adopt simulated annealing to study the problem of the remote sensing of atmospheric duct parameters for two different geometries of propagation measurement. One is from a single emitter to an array of radio receivers (vertical measurements), and the other is from the radar clutter returns (horizontal measurements). Basic principles of simulated annealing and its applications to refractivity estimation are introduced. The performance of this method is validated using numerical experiments and field measurements collected at the East China Sea. The retrieved results demonstrate the feasibility of simulated annealing for near real-time atmospheric refractivity estimation. For comparison, the retrievals of the genetic algorithm are also presented. The comparisons indicate that the convergence speed of simulated annealing is faster than that of the genetic algorithm, while the anti-noise ability of the genetic algorithm is better than that of simulated annealing.

An Adaptive Objective Function for Evaporation Duct Estimations from Radar Sea Echo

In the process of atmospheric refractivity estimation from radar sea echo, the objective function that calculates the match between the predicted and observed field plays an important role. To reduce the effect of noises from long ranges on the objective function, we present a selection method of final ranges for inversion. An adaptive objective function is introduced with a linear distance weight added to the least squares error function (LSEF). Through an evaporation duct height (EDH) retrieving process, the performance of the adaptive objective function is evaluated. The result illustrates that the present method performs better than the LSEF in EDH inversions from clutters with different clutter-to-noise ratios.

Refractivity estimations from an angle-of-arrival spectrum

This paper addresses the probability of atmospheric refractivity estimation by using field measurements at an array of radio receivers in terms of angle-of-arrival spectrum. Angle-of-arrival spectrum information is simulated by the ray optics model and refractivity is expressed in the presence of an ideal tri-linear profile. The estimation of the refractivity is organized as an optimization problem and a genetic algorithm is used to search for the optimal solution from various trial refractivity profiles. Theoretical analysis demonstrates the feasibility of this method to retrieve the refractivity parameters. Simulation results indicate that this approach has a fair anti-noise ability and its accuracy performance is mainly dependent on the antenna aperture size and its positions.