GTD Terrain Reflection Model Applied to ILS Glide Scope

The capability of calculating the reflection of electromagnetic signals from uneven terrain has many applications. One of these is the determination of instrument landing system (ILS) glide slope performance. For this application the wavelength is approximately 1 m, incidence angles are usually near grazing, and the fields are horizontally polarized, so that gross irregularities such as dropoffs and hills are more important than surface roughness. Past approaches used to calculate the ground reflections for this application have been three-dimensional physical optics models which were very cumbersome and time consuming and which neglected important diffraction and shadowing phenomenon; a two-dimensional physical optics model which was faster than the three-dimensional models but ignored many shadowing and transverse terrain variation effects; and a half-plane diffraction model which is applicable only to a specified type of terrain geometry. In this paper a terrain reflection model based on the geometrical theory of diffraction (GTD) is described which can accommodate any piecewise linear terrain profile, requires less computer time than the physical optics models, is capable of including transverse terrain effects, and determines the reflected fields with all important diffraction and blockage effects included.     

An Upper Bound on Detection Probability for Fluctuating Signals

In the theory of signal detectability, the signal-to-noise ratio (SNR), defined as the quotient of the average received signal energy and the spectral density of the white Gaussian noise, is a fundamental parameter. For a signal which is exactly known, or known except for a random phase, this ratio uniquely defines the detection performance which can be achieved with a matched filter receiver. However, when the signal amplitude is a random parameter, the detection performance is changed and must be determined from the probability density function (pdf) of the amplitude. Relative to the case of a constant signal amplitude, such signal amplitude fluctuation usually degrades performance when a high probability of detection (Pd) is required, but improves performance at low values of Pd; the corresponding change in the required SNR is the so-called signal fluctuation loss Lf. Thus, since Lf in some cases represents an improvement in performance for low values of Pd, a question of at least theoretical interest is: how large might this improvement be, when the class of all signal amplitude pdf's is considered. The solution, presented here, results in a lower bound on the signal fluctuation loss Lf as a function of Pd, or equivalently an upper bound on Pd as a function of SNR. The corresponding most favorable pdf was determined using the Lagrange multiplier technique and results of a numerical maximization are included to provide insight into the general properties of the solution.     

Direct Evaluation of Radar Detection Probabilities

A simple and effective procedure for evaluating detectionperformances in radar and sonar detection problems is derived forboth fixed-threshold and adaptive-threshold detection. Using theprocedure, the cumulative probabilities of the test statistic can bedirectly evaluated from the moment generating functions bycalculating residues. The exact formulae for computing the detectionperformances for the chi-square family of fluctuating targets withan integer fluctuation parameter are given in a finite sum formwithout any special functions for both fixed threshold and cellaverageconstant false-alarm rate detection by using the methoddeveloped here.     

Remote data sensing using SAR and harmonic reradiators

In sensor networks distributed over large areas, communication by means of active transmitters on sensor nodes is inherently energy expensive and poses a significant bottleneck to achieve a long battery life. We propose modulated reradiation of radar illumination as a means to transmit information from a group of sensors to an airborne radar. This puts the communications energy burden on the radar transmitter rather than on the sensor nodes, thus increasing their battery lifetimes. To distinguish the sensor return from the clutter return, the modulation on the sensors is done by switching a nonlinear load on the sensor antenna and processing the harmonic reradiation. We present techniques to transmit information from the sensors, which use stripmap mode synthetic aperture radar (SAR) ideas to decode the information and to simultaneously obtain a geographic map of the sensor locations.     

The Solar Chemical Composition

We present our current knowledge of the solar chemical composition based on the recent significant downward revision of the solar photospheric abundances of the most abundant metals. These new solar abundances result from the use of a 3D hydrodynamic model of the solar atmosphere instead of the classical 1D hydrostatic models, accounting for departures from LTE, and improved atomic and molecular data. With these abundances, the new solar metallicity, Z, decreases to Z=0.012, almost a factor of two lower than earlier widely used values. We compare our values with data from other sources and analyse a number of impacts of these new photospheric abundances. While resolving a number of longstanding problems, the new 3D-based solar photospheric composition also poses serious challenges for the standard solar model as judged by helioseismology.     

EUV spectroscopy as a plasma diagnostic

The EUV wavelength regions is rich in emission lines from the transition region and the corona. Spectroscopic techniques have been used extensively to determine the physical conditions in the solar atmosphere for such diverse phenomena as coronal holes, active regions, sunspots, flares, etc. The diagnostics and dynamics of plasmas, both homogeneous and inhomogeneous plasmas, are reviewed. The future projects such as the CDS and SUMER instruments on SOHO have been discussed as they cover EUV wavelength region and will provide a wealth of observational data with excellent spatial, spectral, and temporal resolution.     

Langmuir Turbulence and Solar Radio Bursts

Langmuir waves and turbulence resulting from an electron beam-plasma instability play a fundamental role in the generation of solar radio bursts. We report recent theoretical advances in nonlinear dynamics of Langmuir waves. First, starting from the generalized Zakharov equations, we study the parametric excitation of solar radio bursts at the fundamental plasma frequency driven by a pair of oppositely propagating Langmuir waves with different wave amplitudes. Next, we briefly discuss the emergence of chaos in the Zakharov equations. We point out that chaos can lead to turbulence in the source regions of solar radio emissions. This revised version was published online in August 2006 with corrections to the Cover Date.     

Blind adaptive decision fusion for distributed detection

We consider the problem of decision fusion in a distributed detection system. In this system, each detector makes a binary decision based on its own observation, and then communicates its binary decision to a fusion center. The objective of the fusion center is to optimally fuse the local decisions in order to minimize the final error probability. To implement such an optimal fusion center, the performance parameters of each detector (i.e., its probabilities of false alarm and missed detection) as well as the a priori probabilities of the hypotheses must be known. However, in practical applications these statistics may be unknown or may vary with time. We develop a recursive algorithm that approximates these unknown values on-line. We then use these approximations to adapt the fusion center. Our algorithm is based on an explicit analytic relation between the unknown probabilities and the joint probabilities of the local decisions. Under the assumption that the local observations are conditionally independent, the estimates given by our algorithm are shown to be asymptotically unbiased and converge to their true values at the rate of O(1/k/sup 1/2/) in the rms error sense, where k is the number of iterations. Simulation results indicate that our algorithm is substantially more reliable than two existing (asymptotically biased) algorithms, and performs at least as well as those algorithms when they work.