Cemil B. Erol

Statistical characterization of time variability in midlatitude single‐tone HF channel response

In this paper, a statistical analysis approach is proposed to characterize the variability of HF channel response to single-tone signals by using only the amplitude information of the received signal. By the proposed methodology, robust estimates of the time varying mean and variance of the channel response can be obtained. For this purpose, we use sliding window statistics of the available data. On the basis of the estimated variance of the obtained results, a detailed justification of the proper window size is given. In order to obtain more reliable estimates, the data are median filtered prior to statistical analysis. A robust way of choosing the length of the median filter is presented. We applied the statistical analysis approach to a set of available data obtained from a measurement campaign between England and Turkey conducted from April 1992 to February 1993. The results of the statistical analysis confirmed the expectations of the physical behavior of the ionospheric channel. It was found that the midlatitude single-frequency channel is slowly time varying and locally stationary in a sliding window of 22 s. Also, it was observed that the amplitude of the received signal exhibits a significant diurnal variation. In addition, during early morning hours and night hours, the channel is considerably more stable for communication purposes compared with day and early evening hours.

3-D Computerized ionospheric tomography with random field priors

Computerized Ionospheric Tomography (CIT) is a method to reconstruct ionospheric electron density image by computing Total Electron Content (TEC) values from the recorded GPS signals. Due to the multi-scale variability of the ionosphere and inherent biases and errors in the computation of TEC, CIT constitutes an underdetermined ill-posed inverse problem. In this study, CIT is performed by using a Bayesian approach with Gaussian random field priors. The 3-D mean and the covariance of the assumed Gaussian random field priors can either be obtained from ionospheric models such as IRI or they can be estimated by an iterative algorithm from the GPS measurements. Given sparse and non-uniform TEC measurements, the electron field is obtained from mean square estimation where the Gaussian random field structure provides regularization. Geographical and temporal variations of ionosphere can be observed by obtaining tomographic reconstructions of electron density distribution from Earth-based GPS stations for both quiet and disturbed days of ionosphere. 2-D slices that will be obtained from 3-D reconstructions can be compared with the model based reconstructions or with the available Global Ionospheric Maps from IGS centers.

Validation of refractive index structure parameter estimation for certain infrared bands

Variation of the atmospheric refraction index due to turbulent fluctuations is one of the key factors that affect the performance of electro-optical and infrared systems and sensors. Therefore, any prior knowledge about the degree of variation in the refractive index is critical in the success of field studies such as search and rescue missions, military applications, and remote sensing studies where these systems are used frequently. There are many studies in the literature in which the optical turbulence effects are modeled by estimation of the refractive index structure parameter, C(n)(2), from meteorological data for all levels of the atmosphere. This paper presents a modified approach for bulk-method-based C(n)(2) estimation. According to this approach, conventional wind speed, humidity, and temperature values above the surface by at least two levels are used as input data for Monin-Obukhov similarity theory in the estimation of similarity scaling constants with a finite difference approximation and a bulk-method-based C(n)(2) estimation. Compared with the bulk method, this approach provides the potential for using more than two levels of standard meteorological data, application of the scintillation effects of estimated C(n)(2) on the images, and a much simpler solution than traditional ones due to elimination of the roughness parameters, which are difficult to obtain and which increase the complexity, the execution time, and the number of additional input parameters of the algorithm. As a result of these studies, Atmospheric Turbulence Model Software is developed and the results are validated in comparison to the C(n)(2) model presented by Tunick.

DAMA—Infrared Sea Modeling and Analysis Software

The performance of infrared (IR) surveillance systems is proportional to the ability of distinguishing the radiance of target from the background. In determining sea radiance as the background, experimental studies are incomplete and very expensive. The few simulation software packages that are available are limited to certain parameters and do not include all aspects of sea radiance. In this paper, simulation and analysis software, namely, InfrareD SeA Modeling and Analysis (DAMA), is developed to calculate the total radiance and its components seen by the observer. The DAMA software allows the user to define all possible parameters related to atmospheric conditions, sea surface conditions, and date and time of observation. The developed software includes all of the three major sea surface models in the IR band provided in the literature, namely, Cox and Munk, Mermelstein, and Shaw and Churnside models. In this sense, the DAMA software is unique in combining all possible atmospheric and surface parameters to provide components of total radiance. The DAMA software can be operated by a user-friendly graphical user interface to facilitate simulations and to analyze the outputs. For this paper, the software has been run for approximately 10 000 simulations to understand the behavior of maritime background in IR. The software is validated by the SEARAD software and measurement results. By this way, for the first time in the open literature, DAMA allows one to observe the behavior of total radiance and its components with respect to the variation of all possible input parameters.

GPS/TEC Estimation with IONOLAB Method

Total Electron Content (TEC) is a key variable to measure the ionospheric characteristics and disturbances. The Global Positioning System (GPS) can be used for TEC estimation making use of the recorded signals at the GPS receiver. Reg-Est method that is developed by F. Ankan, C.B. Erol and O. Arikan can be used to estimate high resolution, robust TEC values combining GPS measurements of 30 s resolution obtained from the satellites which are above the 10deg elevation limit. Using this method, it is possible to estimate TEC values for a whole day or a desired time period both for quiet and disturbed days of the ionosphere. Reg-Est provides robust TEC estimates for high-latitude, mid-latitude and equatorial stations. In this study, some important parameters of Reg-Est such as ionospheric thin shell height, weighting function and receiver-satellite biases are investigated. By incorporating the results of the investigation, Reg-Est algorithm is developed into IONOLAB method. Thin shell model height is an important parameter for Single Layer Ionosphere Model (SLIM). In this study, it is shown that IONOLAB provides reliable and robust TEC estimates independent of the choice of the maximum ionization height. Signals from the low elevation satellites are prone to multipath effects. In order to reduce the distortion due to multipath signals, the optimum weighting function is implemented in IONOLAB, minimizing the non-ionospheric noise effects. GPS receivers record both pseudorange and phase data of signals. IONOLAB can input absolute TEC computed from the pseudorange measurements or phase-corrected low-noise TEC. The TEC estimates for both of these inputs are in good accordance with each other. Thus, taking either pseoudorange or phase-corrected measurement data as input, high resolution, robust TEC estimates can be obtained from IONOLAB. Another important parameter for TEC estimation is satellite-receiver instrumental biases. The biases are the frequency dependent delays due to satellite and receiver hardware. In order to compute TEC, satellite and receiver biases should be removed from GPS measurements correctly. However, the proper procedure of how to include them in the TEC computation is generally vaguely defined. IONOLAB suggests a technique for inclusion of the hardware biases obtained from the web for TEC estimates that are consistent with the results from the IGS analysis centers.

Polarization of sea background radiance in the infrared band

The success of infrared surveillance systems is proportional to distinguishing the radiance of the target and the background. In this study, a simulation program, namely, DAMA (InfrareD SeA Modeling and Analysis) software is developed to calculate the total radiance and its components seen by an observer looking towards the sea surface. Total radiance is composed of thermal sea radiance, reflected source (sun or moon) radiance, reflected sky radiance and path radiance seen by the observer. The sea surface models are directly effective on the calculation of reflected radiances. DAMA program includes Cox and Munk, Mermelstein and Shaw-Churnside models and models for six different saltiness levels. The inputs of the software are classified as observer, time, wavelength, medium, model selection and output file parameters. The software developed by modules gives the total radiance and its components as output. The program has been run for approximately 10,000 times to obtain the effects of input parameters on total radiance and its components and the outputs are examined. DAMA is validated by SEARAD software and measurement results. By this way, for the first time in open literature, a simulation program including all sea surface and saltiness models is developed and the behavior of the total radiance and its components are examined with respect to the variation of all input parameters.

EgitGOZv1 : A basic imaging system simulator in 0.2–14µm band for educational purposes

In electro-optical and infrared end-to-end system analysis, the image degrading effects can be modelled by cascaded linear shift invariant filters. These effects are in general listed as atmospheric effects, motion effects, optics, detector, reconstruction and display. The degrading effects simulation is discussed by using a developed basic simulator shortly and important points; sampling effects on restoration and shower curtain effects are pointed out.

Ionospheric Total Electron Content Estimation Using IONOLAB Method

Ionosphere which is an important atmospheric layer for HF and satellite communications, can be investigated through Total Electron Content (TEC). Global Positioning System provides cost-effective means for TEC estimation. Regularized TEC estimation method (D-TEI) is developed to estimate high resolution, robust TEC values. The method combines measurements of GPS satellites above 10° elevation limit and estimates can be obtained with 30 s time resolution. In this paper, parameters that are used in D-TEI method such as ionospheric height, weighting function, and satellite receiver biases are studied. It is found that TEC estimation results of D-TEI method is almost independent of ionospheric height. Different weighting functions are tried and the weighting function that minimizes non-ionospheric effects is selected. By using satellite and receiver biases in the correct form consistent TEC estimation results are obtained with IGS analysis centers. In this paper, the method is improved to include phase measurements. Taking either pseudorange or phase measurements as input, high resolution, robust TEC estimates are obtained using D-TEI method.

Model based comparison of basis functions and reconstruction algorithms in computerized ionospheric tomography

Computerized Ionospheric Tomography (CIT) is a method to investigate ionospheric electron density in two or three dimensions. CIT provides a flexible method for studying ionosphere. In CIT, GPS satellites and Earth based receivers are considered to perform tomographic reconstruction. The received signals are processed to calculate Total Electron Content (TEC). TEC values and the tomographic reconstruction algorithms are used together to obtain tomographic images of electron density of ionosphere. In this study, scenario with one GPS satellite and one receiver is simulated. Receiver error and model error are neglected. In simulation, a set of basis functions and algorithms are used and two dimensional tomographic images of ionospheric electron density in height and latitude is obtained. Comparative reconstruction results are given based on IRI-95 forward ionosphere model. 1. Giris