Detection performance in K-distributed and correlatedRayleigh clutters

Performance prediction for a detection system employing noncoherent integration is carried out for a chi-square family of fluctuating targets in K-distributed clutter plus noise. The detection performance for Swerling 11 targets in the K-distributed clutter plus noise is compared with that in exponentially correlated Rayleigh clutter. The results show that the performance prediction based on N pulses integrated in clutter plus noise using the K-distributed clutter model may be approximately equivalent to that using the exponentially correlated Rayleigh-distributed clutter model     

Detection loss due to interfering targets in ordered statisticsCFAR

The ordered-statistics (OS) constant false-alarm rate (CFAR) is relatively immune to the presence of interfering targets among the reference cells used to determine the average background. OS CFAR performance in a multitarget environment was previously studied by simulation. The author obtains analytic expressions for the added detection loss, assuming strong interfering targets. The real target is assumed to be a Rayleigh fluctuating target. Numerical examples are included     

Filtering of discontinuous processes arising in marine integratednavigation systems

A refined stochastic model for the errors of the Loran-C radio navigation aid is described, and it is shown how this model can be used to improve the performance of integrated navigation systems. In addition to the usual propagation errors, Loran-C time of arrival measurements are occasionally plagued with sudden intermittent errors of a particular magnitude and caused by receiver cycle selection errors. These result in sudden large jumps in the calculated position solution. The Loran-C error has been modeled as the sum of a diffusion process, representing the normal propagating errors, and a pure jump process of Poisson type, representing the cycle selection errors. A simple integrated navigation system is then described, based on the Loran-C model and the standard dead reckoning (heading and speed) system model. Assuming that the observed process is governed by a linear stochastic difference equation, a recursive linear unbiased minimum variance filter is developed, from which the Loran-C and dead reckoning errors, and hence position and velocity, can be estimated     

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.     

MESSENGER: Exploring Mercury’s Magnetosphere

The MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) mission to Mercury offers our first opportunity to explore this planet’s miniature magnetosphere since the brief flybys of Mariner 10. Mercury’s magnetosphere is unique in many respects. The magnetosphere of Mercury is among the smallest in the solar system; its magnetic field typically stands off the solar wind only ∼1000 to 2000 km above the surface. For this reason there are no closed drift paths for energetic particles and, hence, no radiation belts. Magnetic reconnection at the dayside magnetopause may erode the subsolar magnetosphere, allowing solar wind ions to impact directly the regolith. Inductive currents in Mercury’s interior may act to modify the solar wind interaction by resisting changes due to solar wind pressure variations. Indeed, observations of these induction effects may be an important source of information on the state of Mercury’s interior. In addition, Mercury’s magnetosphere is the only one with its defining magnetic flux tubes rooted beneath the solid surface as opposed to an atmosphere with a conductive ionospheric layer. This lack of an ionosphere is probably the underlying reason for the brevity of the very intense, but short-lived, ∼1–2 min, substorm-like energetic particle events observed by Mariner 10 during its first traversal of Mercury’s magnetic tail. Because of Mercury’s proximity to the sun, 0.3–0.5 AU, this magnetosphere experiences the most extreme driving forces in the solar system. All of these factors are expected to produce complicated interactions involving the exchange and recycling of neutrals and ions among the solar wind, magnetosphere, and regolith. The electrodynamics of Mercury’s magnetosphere are expected to be equally complex, with strong forcing by the solar wind, magnetic reconnection, and pick-up of planetary ions all playing roles in the generation of field-aligned electric currents. However, these field-aligned currents do not close in an ionosphere, but in some other manner. In addition to the insights into magnetospheric physics offered by study of the solar wind–Mercury system, quantitative specification of the “external” magnetic field generated by magnetospheric currents is necessary for accurate determination of the strength and multi-polar decomposition of Mercury’s intrinsic magnetic field. MESSENGER’s highly capable instrumentation and broad orbital coverage will greatly advance our understanding of both the origin of Mercury’s magnetic field and the acceleration of charged particles in small magnetospheres. In this article, we review what is known about Mercury’s magnetosphere and describe the MESSENGER science team’s strategy for obtaining answers to the outstanding science questions surrounding the interaction of the solar wind with Mercury and its small, but dynamic, magnetosphere.     

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.     

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.