Nonstochastic adaptive decision fusion in distributed-detection systems

Implementing the optimal Neyman-Pearson (NP) fusion rule in distributed detection systems requires the sensor error probabilities to be a priori known and constant during the system operation. Such a requirement is practically impossible to fulfil for every resolution cell in a multiflying target multisensor environment. The true performance of the fusion center is often worse than expected due to fluctuations of the observed environment and instabilities of sensor thresholds. This work considers a nonparametric data fusion situation in which the fusion center knows only the number of the sensors, but ignores their error probabilities and cannot control their thresholds. A data adaptive approach to the problem is adopted, and combining P reports from Q independent distributed sensors through a least squares (LS) formulation to make a global decision is investigated. Such a fusion scheme does not entail strict stationarity of the noise environment nor strict invariance of the sensor error probabilities as is required in the NP formulation. The LS fusion scheme is analyzed in detail to simplify its form and determine its asymptotic behavior. Conditions of performance improvement as P increases and of quickness of such improvement are found. These conditions are usually valid in netted radar surveillance systems.     

Electronically Tracking Antenna System for Satellite Reception

This correspondence describes a novel electronically tracking antenna system for satellite reception in the VHF range. The "hedgehog" antenna consists of several antennas directed in different directions to cover the whole sky. An electronic switch, controlled by the satellite receiver, connects the receiver to the antenna from which the desired satellite signal is obtained. This system is especially suitable for unmanned reliable receiving stations if an antenna gain of the order of 10 dB is enough.     

Adaptive cancellation method for geometry-induced nonstationary bistatic clutter environments

This paper describes and characterizes a new bistatic space-time adaptive processing (STAP) clutter mitigation method. The approach involves estimating and compensating aspects of the spatially varying bistatic clutter response in both angle and Doppler prior to adaptive clutter suppression. An important feature of the proposed method is its ability to extract requisite implementation information from the data itself, rather than rely on ancillary - and possibly erroneous or missing - system measurements. We justify the essence of the proposed method by showing its ability to align the dominant clutter subspaces of each range realization relative to a suitably chosen reference point as a means of homogenizing the space-time data set. Moreover, we numerically characterize performance using synthetic bistatic clutter data. For the examples considered herein, the proposed bistatic STAP method leads to maximum performance improvements between 17.25 dB and 20.75 dB relative to traditional STAP application, with average improvements of 6 dB to 10 dB.     

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.     

Exploiting GNSS signal structure to enhance observability

There are a number of different error sources, such as multipath and thermal noise, which corrupt satellite navigation waveforms from their theoretical structure. However, even under ideal conditions the broadcast signals have some degree of deformation as a result of the practical individual hardware implementation. For the most demanding users of satellite navigation, such as aircraft navigation and landing systems, it is important to characterize the nominal signal structure in order to detect minimal variations resulting from hardware-based errors. Thus far such precorrelation Global Navigation Satellite System (GNSS) signal quality monitoring has been performed through high gain antennas, which allow for raising the GNSS spectrum above the thermal noise floor and observing the structure of the signal directly at the front end output. This paper describes a new approach to achieve such observability based on signal processing techniques, such as dithering and averaging, which leverage the repetitive nature of the GNSS signal. The paper presents how these techniques can drastically improve the signal-to-noise ratio (SNR) in postprocessing, allowing for the direct analysis of GNSS signals using traditional front end designs and conventional antennas. Results are predicted using the appropriate theory and validated using data collected from the Global Positioning System (GPS).     

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     

Investigation of corona initiation voltage at reduced pressures

The results of an experimental study of the 60 Hz ac corona inception voltage at reduced pressures for a cylindrical conductor and a parallel ground plane are presented. Several spacings and bare and insulated conductors of several diameters are considered. Empirical expressions from which a reasonably accurate prediction of the corona inception voltage (CIV) may be made have been inferred from experimental data     

Modulation of Cosmic Rays in the Heliosphere From Solar Minimum to Maximum: a Theoretical Perspective

The modulation of galactic cosmic rays in the heliosphere seems to be dominated by four major mechanisms: convection, diffusion, drifts (gradient, curvature and current sheet), and adiabatic energy losses. In this regard the global structure of the solar wind, the heliospheric magnetic field (HMF), the current sheet (HCS), and that of the heliosphere itself play major roles. Individually, the four mechanisms are well understood, but in combination, the complexity increases significantly especially their evolvement with time - as a function of solar activity. The Ulysses observations contributed significantly during the past solar minimum modulation period to establish the relative importance of these major mechanisms, leading to renewed interest in developing more sophisticated numerical models, and in the underlying physics, e.g., what determines the diffusion tensor. With increased solar activity, the relative contributions of the mentioned mechanisms change, but how they change and what causes these changes over an 11-year solar cycle is not well understood. It can therefore be expected that present and forthcoming observations during solar maximum activity will again produce very important insights into the causes of long-term modulation. In this paper the basic theory of solar modulation is reviewed for galactic cosmic rays. The influence of the Ulysses observations on the development of the basic theory and numerical models are discussed, especially those that have challenged the theory and models. Model-based predictions are shown for what might be encountered during the next solar minimum. Lastly, modulation theory and modelling are discussed for periods of maximum solar activity when a global reorganization of the HMF, and the HCS, occurs. This revised version was published online in August 2006 with corrections to the Cover Date.