Search

The objective of CASPER is to improve our capability to characterize the propagation of radio frequency (RF) signals through the marine atmosphere with coordinated efforts in data collection, data analyses, and modeling of the air–sea interaction processes, refractive environment, and RF propagation.

In many maritime regions of the world, such as the Mediterranean, Persian Gulf, East China Sea, and the Californian Coast, atmospheric ducts are common occurrences. They result in various anomalies such as significant variations in the maximum operational radar range, creation of regions where the radar is practically blind (radar holes) and increased sea clutter. Therefore, it is important to predict the real-time 3-D environment in which the radar is operating so that the radar operator will at least know the true system limitations and in some cases even compensate for them. This dissertation addresses the estimation and tracking of the lower atmospheric radio refractivity under non-standard propagation conditions frequently encountered in low altitude maritime radar applications. This is done by statistically estimating the duct strength (range and height-dependent atmospheric index of refraction) from the sea-surface reflected radar clutter. Therefore, such methods are called Refractivity From Clutter (RFC) techniques. These environmental statistics can then be used to predict the radar performance. The electromagnetic propagation in these complex environments is simulated using a split-step fast Fourier transform (FFT) based parabolic equation (PE) approximation to the wave equation. The first part of this thesis discusses various algorithms such as genetic algorithms (GA), Markov chain Monte Carlo samplers (MCMC) and the hybrid GA-MCMC samplers that are used to estimate atmospheric radio refractivity for a given azimuth direction and time. The results show that radar clutter can be a rich source of information about the environment and the techniques mentioned above are used successfully as near real-time estimators for the data collected during the Wallops'98 experiment conducted by the Naval Surface Warfare Center. The second part of this dissertation focuses on both spatial and temporal tracking of the 3-D environment. Techniques such as the extended (EKF)

[1] This paper addresses the problem of estimating the lower atmospheric refractivity ( M profile) under nonstandard propagation conditions frequently encountered in low-altitude maritime radar applications. This is done by statistically estimating the duct strength (range- and height-dependent atmospheric index of refraction) from the sea surface reflected radar clutter. These environmental statistics can then be used to predict the radar performance. In previous work, genetic algorithms (GA) and Markov chain Monte Carlo (MCMC) samplers were used to calculate the atmospheric refractivity from returned radar clutter. Although GA is fast and estimates the maximum a posteriori ( MAP) solution well, it poorly calculates the multidimensional integrals required to obtain the means, variances, and underlying posterior probability distribution functions of the estimated parameters. More accurate distributions and integral calculations can be obtained using MCMC samplers, such as the Metropolis-Hastings and Gibbs sampling (GS) algorithms. Their drawback is that they require a large number of samples relative to the global optimization techniques such as GA and become impractical with an increasing number of unknowns. A hybrid GA-MCMC method based on the nearest neighborhood algorithm is implemented in this paper. It is an improved GA method which improves integral calculation accuracy through hybridization with a MCMC sampler. Since the number of forward models is determined by GA, it requires fewer forward model samples than a MCMC, enabling inversion of atmospheric models with a larger number of unknowns.

[1] This paper addresses the problem of estimating the lower atmospheric refractivity ( M profile) under nonstandard propagation conditions frequently encountered in low-altitude maritime radar applications. This is done by statistically estimating the duct strength (range- and height-dependent atmospheric index of refraction) from the sea surface reflected radar clutter. These environmental statistics can then be used to predict the radar performance. In previous work, genetic algorithms (GA) and Markov chain Monte Carlo (MCMC) samplers were used to calculate the atmospheric refractivity from returned radar clutter. Although GA is fast and estimates the maximum a posteriori ( MAP) solution well, it poorly calculates the multidimensional integrals required to obtain the means, variances, and underlying posterior probability distribution functions of the estimated parameters. More accurate distributions and integral calculations can be obtained using MCMC samplers, such as the Metropolis-Hastings and Gibbs sampling (GS) algorithms. Their drawback is that they require a large number of samples relative to the global optimization techniques such as GA and become impractical with an increasing number of unknowns. A hybrid GA-MCMC method based on the nearest neighborhood algorithm is implemented in this paper. It is an improved GA method which improves integral calculation accuracy through hybridization with a MCMC sampler. Since the number of forward models is determined by GA, it requires fewer forward model samples than a MCMC, enabling inversion of atmospheric models with a larger number of unknowns.

In many maritime regions of the world, such as the Mediterranean, Persian Gulf, East China Sea, and the Californian Coast, atmospheric ducts are common occurrences. They result in various anomalies such as significant variations in the maximum operational radar range, creation of regions where the radar is practically blind (radar holes) and increased sea clutter. Therefore, it is important to predict the real-time 3-D environment in which the radar is operating so that the radar operator will at least know the true system limitations and in some cases even compensate for them. This dissertation addresses the estimation and tracking of the lower atmospheric radio refractivity under non-standard propagation conditions frequently encountered in low altitude maritime radar applications. This is done by statistically estimating the duct strength (range and height-dependent atmospheric index of refraction) from the sea-surface reflected radar clutter. Therefore, such methods are called Refractivity From Clutter (RFC) techniques. These environmental statistics can then be used to predict the radar performance. The electromagnetic propagation in these complex environments is simulated using a split-step fast Fourier transform (FFT) based parabolic equation (PE) approximation to the wave equation. The first part of this thesis discusses various algorithms such as genetic algorithms (GA), Markov chain Monte Carlo samplers (MCMC) and the hybrid GA-MCMC samplers that are used to estimate atmospheric radio refractivity for a given azimuth direction and time. The results show that radar clutter can be a rich source of information about the environment and the techniques mentioned above are used successfully as near real-time estimators for the data collected during the Wallops'98 experiment conducted by the Naval Surface Warfare Center. The second part of this dissertation focuses on both spatial and temporal tracking of the 3-D environment. Techniques such as the extended (EKF)

The development of new geoacoustic inversion methods, their use in the analysis of shallow water experimental data, and evaluation of geoacoustic model and parameter uncertainties including the mapping of these uncertainties through to system performance uncertainties.

The development of new geoacoustic inversion methods, their use in the analysis of shallow water experimental data, and evaluation of geoacoustic model and parameter uncertainties including the mapping of these uncertainties through to system performance uncertainties. Analysis of geoacoustic inversion data collected from various experiments. Of specific technical interest are: (1) development of methods to track the environmental parameters using sequential filtering, (2) use of ambient noise for estimation of seafloor structure parameters, and (3) the development of new inversion methods for use into the kHz frequency regime. In an ONR Graduate Traineeship Awards we address using Random Matrix Theory in ocean acoustics.

Utilize signals from emitters of opportunity to (a) evaluate sources of propagation predictions (e.g., numerical weather prediction), and (b) provide real-time refractivity information using EM inverse methods.

Long-term Goals: Fuse refractivity inferred from electromagnetic (EM) propagation observations with background fields from numerical weather prediction (NWP) models. Objectives: Develop data fusion method for atmospheric refractivity scheme based on objective analysis. Develop means to map observations of refractivity based on RF propagation measurements into the space utilized for the analysis. Exercise the data fusion scheme on a combination of synthetic and real data to assess performance. Achieve reasonable processing time (on the order of 1-minute) with a representative domain size using a high-end laptop computer., Prepared in collaboration with University of California, San Diego, CA.

The development of new geoacoustic inversion methods, their use in the analysis of shallow water experimental data, and evaluation of geoacoustic model and parameter uncertainties including the mapping of these uncertainties through to system performance uncertainties. Analysis of geoacoustic inversion data collected from various experiments. Of specific technical interest are: (1) development of methods to track the environmental parameters using sequential filtering, (2) use of ambient noise for estimation of seafloor structure parameters, and (3) the development of new inversion methods for use into the kHz frequency regime. In an ONR Graduate Traineeship Awards we address using Random Matrix Theory in ocean acoustics., The original document contains color images.

The development of new geoacoustic inversion methods, their use in the analysis of shallow water experimental data, and evaluation of geoacoustic model and parameter uncertainties including the mapping of these uncertainties through to system performance uncertainties. Analysis of geoacoustic inversion data collected from various experiments. Of specific technical interest are: (1) development of methods to track the environmental parameters using sequential filtering, (2) use of ambient noise for estimation of seafloor structure parameters, and (3) the development of new inversion methods for use into the kHz frequency regime.

Develop electromagnetic propagation models, and refractivity inversion algorithms, that perform equally well over land and sea and in the presence of anomalous propagation conditions for both surface and airborne emitters, for use in operational or engineering propagation assessment systems.

Develop electromagnetic propagation models, and refractivity inversion algorithms, that perform equally well over land and sea and in the presence of anomalous propagation conditions for both surface and airborne emitters, for use in operational or engineering propagation assessment systems. Develop an advanced unified hybrid radio propagation model based on parabolic equation and rayoptics methods for both surface-based and airborne applications. This model is named the Advanced Propagation Model (APM) and is the primary model used in the Advanced Refractive Effects Prediction System (AREPS). The specific technical objectives are to develop algorithms to extract surface clutter only from weather files provided by the Hazardous Weather Detection and Display Capability (HWDDC); and to improve, refine, and optimize the current state-of-the-art Refractivity From Clutter (RFC) algorithms to infer and characterize surface-based ducts (SBD)., Prepared in collaboration with Scripps Institute of Oceanography, San Diego, CA.