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.     

A cosmochemical view of cosmic rays and solar particles

The composition of cosmic rays and solar particles is reviewed with emphasis on the question of whether they are representative samples of Galactic and solar matter. The composition of solar particles changes with energy and from flare to flare. A strong excess of heavy elements at energies below a few MeV/nuc decreases with energy, and at energies above 15 MeV/nuc the composition of solar particles resembles that of galactic cosmic rays somewhat better than that of the solar atmosphere. The elements Ne through Pb have remarkably similar abundances in cosmic ray sources and in the matter of the solar system. The lighter elements are depleted in cosmic rays, whereas U and Th may be enriched or not, depending on whether the meteoritic or solar abundance of Th is used. Two prototype sources of cosmic rays are considered: gas with solar system composition but enriched in elements with Z > 8 during acceleration and emission (by analogy with solar particle emission), and highly evolved matter enriched in r-process elements such as U, Th and transuranic elements. The energy-dependence of cosmic ray composition suggests that both sources may contribute at different energies.Miller Institute Professor, 1972–73.     

The Local Bubble and Interstellar Material Near the Sun

The properties of interstellar matter at the Sun are regulated by our location with respect to a void in the local matter distribution, known as the Local Bubble. The Local Bubble (LB) is bounded by associations of massive stars and fossil supernovae that have disrupted dense interstellar matter (ISM), driving low density intermediate velocity ISM into the void. The Sun appears to be located in one of these flows of low density material. This nearby interstellar matter, dubbed the Local Fluff, has a bulk velocity of ∼19 km s⁻¹ in the local standard of rest. The flow is coming from the direction of the gas and dust ring formed where the Loop I supernova remnant merges into the LB. Optical polarization data suggest that the local interstellar magnetic field lines are draped over the heliosphere. A longstanding discrepancy between the high thermal pressure of plasma filling the LB and low thermal pressures in the embedded Local Fluff cloudlets is partially mitigated when the ram pressure component parallel to the cloudlet flow direction is included.     

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.     

Ll-CFAR: a flexible and robust alternative

A new family of constant false alarm rate (CFAR) processors is introduced. An Ll-CFAR forms its noise power estimate by linearly filtering ranked samples from the reference set; the weights of this combination, however, depend not only on the rank, but also on the relative proximity of the sample to the cell under test. From the class of Ll-CFARs may be chosen members which effectively censor spurious targets; members which exhibit impressive control of false alarm in the presence of a clutter edge; and members which are robust against both such inhomogeneities. While the design of such schemes is involved, their implementation is not significantly more burdensome than that of plain ordered statistic CFAR (OS-CFAR). After a discussion of the stochastic training of Ll-CFAR, the performance is thoroughly assessed under the most commonly encountered instances of environmental conditions, and compared with those of classical CFAR techniques     

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.     

Speech Data Rate Reduction Part II: Applicability of Sensitivity and Error Analysis

This paper deals with the application of modern estimation techniques to the problem of speech data rate reduction. It is desirable to adaptively identify and quantitize the parameters of the speech model. These paramaters cannot be identified and quantized exactly; the performance of the predictor is thereby degraded and this could prevent data reduction. In many cases it is desirable to emply a suboptimal predictor in order to simplify the algorithms, and predictor performance is again degraded. This paper develops sensitivity and error analysis as a potential method for determining quantitatively how speech data reduction system performance is degraded by imprecise parameter knowledge or suboptimal filtering. An intended use of the sensitivity and error analysis algorithms is to determine parameter identification and model structure requirements of configuration concepts for adaptive speech digitizers. First, sensitivity and error analysis algorithms are presented that form the basis for the remainder of the work. The algorithms are then used to determine how imprecise knowledge of vocal tract parameters degrades predictor performance in speech. Transversal filters have previously been proposed for this application. The sensitivity analysis algorithms are then used to determine when and by how much the transverse filter is suboptimal to the Kalman filter. In particular, the question of how effectively a higher order of all-pole model approximates a system with zeros is answered, as this question is of considerable importance in speech. Finally, the physical significance of the innovations process in speech data rate reduction is studied.     

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.