Sheng Zheng

Refractivity estimation from radar sea clutter

This paper addresses the problem of estimating lower atmospheric refractivity under the nonstandard propagation conditions frequently encountered in low altitude maritime radar applications. The vertical structure of the refractive environment is modeled by using a five-parameter model, and the horizontal structure is modeled as range-independent. The electromagnetic propagation in the troposphere is simulated by using a split-step fast Fourier transform based on parabolic approximation to the wave equation. A global search marked as a modified genetic algorithm (MGA) for the 5 environmental parameters is performed by using a genetic algorithm (GA) integrated with a simulated annealing technique. The retrieved results from simulated runs demonstrate the ability of this method to make atmospheric refractivity estimations. A comparison with the classical GA and the Bayesian Markov Chain Monte Carlo (Bayesian-MCMC) technique shows that the MGA can not only shorten the inverse time but also improve the inverse precision. For real data cases, the inversion values do not match the reference data very well. The inverted profile, however, can be used to synoptically describe the real refractive structure.

Ray tracing/correlation approach to estimation of surface-based duct parameters from radar clutter

This paper describes a technique to estimate surface-based duct parameters by using a simple ray tracing/correlation method. The approach is novel in that it incorporates the Spearman rank-order correlation scheme between the observed surface clutter and the surface ray density for a given propagation path. The simulation results and the real data results both demonstrate the ability of this method to estimate surface-based duct parameters. Compared with the results obtained by a modified genetic algorithm combined with the parabolic wave equation, the results retrieved from the ray tracing/correlation scheme show a minor reduction in accuracy but a great improvement on computation time. Therefore the ray tracing/correlation method might be used as a precursor to more sophisticated and slower techniques, such as genetic algorithm and particle filters, by narrowing the parameter search space and providing a comprehensive and more efficient estimation algorithm.

Forecasting nonlinear time series with genetic algorithms genetic algorithms and symbolic form

A genetic algorithm (GA) programmed to approximate the functional relation, in symbolic form, that describes the behavior of a time series. The GA formalism proposed here utilizes the “postfix” representation with a view to reduce the procedural complexities and the “elitist mating” scheme to produce fitter offspring strings. An initial population of potential solutions is subjected to an evolutionary process described by selection, reproduction and mutation processes which are repeated over generations until an optimum individual is finally found. The GA was proved useful in obtaining functional forms describing accurately the evolution of the nonlinear time series.

Refractivity Estimation from Radar Sea Echos

This paper addresses the problem of estimating parameters of the height-dependent index of refraction over the sea surface. The parabolic approximation for numerical solution of the wave equation is used to formuate the forward model. Refractivity estimation is performed by a modified genetic algorithm(MGA). Simulation results are presented which demonstrate the ability of this approach to estimate refractivity parameters with high precision, which is likely to provide an effective method for the estimation of the duct effects from radar sea ehco.