Recursive Bayesian electromagnetic refractivity estimation from radar sea clutter

Abstract

[1] Estimation of the range-and height-dependent index of refraction over the sea surface facilitates prediction of ducted microwave propagation loss. In this paper, refractivity estimation from radar clutter returns is performed using a Markov state space model for microwave propagation. Specifically, the parabolic approximation for numerical solution of the wave equation is used to formulate the refractivity from clutter (RFC) problem within a nonlinear recursive Bayesian state estimation framework. RFC under this nonlinear state space formulation is more efficient than global fitting of refractivity parameters when the total number of range-varying parameters exceeds the number of basis functions required to represent the height-dependent field at a given range. Moreover, the range-recursive nature of the estimator can be easily adapted to situations where the refractivity modeling changes at discrete ranges, such as at a shoreline. A fast range-recursive solution for obtaining range-varying refractivity is achieved by using sequential importance sampling extensions to state estimation techniques, namely, the forward and Viterbi algorithms. Simulation and real data results from radar clutter collected off Wallops Island, Virginia, are presented which demonstrate the ability of this method to produce propagation loss estimates that compare favorably with ground truth refractivity measurements.

Compressive MIMO Beamforming of Data Collected in a Refractive Environment

“…We assume that periods for which the received power is at a maximum are indicative of some form of ducting (Vasudevan et al, 2007). Using the periods between 25-29 January and 5-8 February as examples of ducting events, we analyze the impact of ducting on the reconstructed path angle pairs ofĤ a from the experimental data.…”

Brief review on PE method application to propagation channel modeling in sea environment

“…More complicated is the matter of the reconstruction of the refractivity profiles; this especially refers to the evaporation duct profiles. The importance of this problem has led, in addition to the well known methods for in situ collection of the meteorological parameters, to the development of indirect techniques to derive the refractivity, such as the use of ground-based GPS (Global Positioning System) measurements [102], Raman lidar system based measurements [103], or RFC (Refractivity From Clutter) algorithms that extract the refractivity from the radar clutter return [104,105] (in the latter two works PE (1) with SSF solution is used to create the radar replica signals). The remote sensing techniques have the advantage of providing profiles characteristic for the integrated refractive effects along the entire propagation path.…”

Estimation of the Atmospheric Duct From Radar Sea Clutter Using Artificial Bee Colony Optimization Algorithm

“…Estimation of atmosphere duct using RFC technique is an inverse problem, and the relationship between the forward propagation model and atmosphere duct parameters is a complex nonlinear model. Recently, many researchers dedicated to the study of estimating the atmosphere duct [4][5][6][7][8][9][10][11][12][13][14] with efficient optimization methods, estimation model and analyze the performance of the optimization algorithm, and the detailed estimation steps and the latest research progress about the RFC technique can be founded in [4,5]. Due to the nonlinear relationship between the forward propagation model and atmospheric duct parameters, the intelligent optimization algorithms, such as genetic algorithm (GA) [15], particle swarm optimization (PSO) [16], differential evolution (DE) [17] and ant colony optimization (ACO) [18], are good candidates to estimate the atmospheric duct from radar sea clutter.…”