Inversion for refractivity parameters from radar sea clutter

Abstract

[1] This paper describes estimation of low-altitude atmospheric refractivity from radar sea clutter observations. The vertical structure of the refractive environment is modeled using five parameters, and the horizontal structure is modeled using six parameters. The refractivity model is implemented with and without an a priori constraint on the duct strength, as might be derived from soundings or numerical weather-prediction models. An electromagnetic propagation model maps the refractivity structure into a replica field. Replica fields are compared to the observed clutter using a squared-error objective function. A global search for the 11 environmental parameters is performed using genetic algorithms. The inversion algorithm is implemented on S-band radar sea-clutter data from Wallops Island, Virginia. Reference data are from range-dependent refractivity profiles obtained with a helicopter. The inversion is assessed (1) by comparing the propagation predicted from the radar-inferred refractivity profiles and from the helicopter profiles, (2) by comparing the refractivity parameters from the helicopter soundings to those estimated, and (3) by examining the fit between observed clutter and optimal replica field. This technique could provide near-real-time estimation of ducting effects. In practical implementations it is unlikely that range-dependent soundings would be available. A single sounding is used for evaluating the radar-inferred environmental parameters. When the unconstrained environmental model is used, the ''refractivity-from-clutter,'' the propagation loss generated and the loss from this single sounding, is close within the duct; however, above the duct they differ. Use of the constraint on the duct strength leads to a better match also above the duct.

Statistical estimations of atmospheric duct over the South China Sea and the tropical eastern Indian Ocean

“…However, this does not affect a rough statistics for this region. To describe an atmospheric duct in detail, some characteristic quantities are defined, such as duct height for evaporation duct, duct height, thickness and magnitude/M-deficit for surface-based and elevated ducts [12]. Figure 3 presents the histogram distributions for the characteristic quantities of the three typical ducts using all the observations from 2010 to 2012.…”

Evaporation Duct Retrieval Using Changes in Radar Sea Clutter Power Versus Receiving Height

“…A conventional objective function used in previous EDH inversion research is the least squares error function (LSEF) [11], defined as…”

“…Various traditional methods have been used to determine the atmospheric refractivity profile; these approaches include "bulk" models with in situ measurements as input, microwave refractometers [7], LIDAR techniques [8], and many numerical weather prediction models [9]. In recent years, a technique referred to as refractivity from clutter (RFC) has been widely used in retrieving the refractivity profile in maritime environments [10][11][12][13][14]. This method can easily be performed without additional instruments apart from the radars installed on ships.…”

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

“…Taking the influence of atmosphere condition into account, the received radar sea clutter power can be obtained from radar equation [4] Figure 1. The modified refractivity profiles of (a) evaporation duct and (b) surface-based duct.…”

“…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.…”