Experiments in Adaptive Estimation of Unknown Binary Waveforms

A special-purpose adaptive machine is described which carries out estimation in real time of an unknown binary waveform which is perturbed with additive Gaussian noise. Unknown waveforms of over 103 samples in duration can be recovered. The unknown waveforms are of unknown epoch and can reappear at either random or periodic time intervals. The observed signal is received at moderate or low signal-to-noise ratios so that a single observation of the received data (even if one knew the precise signal arrival time) is not sufficient to provide a good estimate of the signal waveshape. Experimental results are described which show transient behavior waveform estimate. The transient behavior is expressed as the number of errors in the current estimate of the signal plotted vs. time. In a noisy environment, each ``learning' transient is a random time function. These learning transients are shown for several different signal-to-noise ratios and indicate the threshold noise levels for various types of initial states of the machine memory.     

The Time-of-Flight Energy,Angle, Mass Spectrograph (Teams) Experiment for Fast

The Time-of-flight Energy Angle Mass Spectrograph (TEAMS) is being flown on the FAST Small Explorer mission to measure the 3-dimensional distribution function of the major ion species present in the lower magnetosphere. The instrument is similar to time-of-flight plasma analyzer systems that have been designed and planned for flight as CODIF (COmposition and DIstribution Function analyzer) on the four European Space Agency Cluster-II spacecraft and, as ESIC (Equator-S Ion Composition instrument) on Equator-S. This instrument allows the 3-dimensional distribution functions of individual ion species to be determined within spin period (2.5 s). Two-dimensional distributions are measured in 80 ms. These capabilities are crucial for the study of selective energization processes in the auroral regions of the magnetosphere. The design, operational characteristics, and test and calibration results for this instrument are presented. The sensor consists of a toroidal top-hat electrostatic analyzer with instantaneous acceptance of ions over 360° in polar angle. After post-acceleration of the incoming ions by up to 25 kV, a time-of-flight mass spectrograph discriminates the individual species. It has been demonstrated through calibration that the instrument can easily separate H⁺, He²⁺, He⁺, O⁺ and, for energies after post-acceleration of > 20 keV, even O₂ ⁺ molecules. On-board mass discrimination and the internal accumulation of several distinct data quantities combined with the spacecraft's flexible telemetry formatting allow for instrument data rates from 7.8 kb s^–1 to 315 kb s^–1 to be telemetered to ground through the FAST centralized Instrument Data Processor.     

The FAST Satellite Fields Instrument

We describe the electric field sensors and electric and magnetic field signal processing on the FAST (Fast Auroral SnapshoT) satellite. The FAST satellite was designed to make high time resolution observations of particles and electromagnetic fields in the auroral zone to study small-scale plasma interactions in the auroral acceleration region. The DC and AC electric fields are measured with three-axis dipole antennas with 56 m, 8 m, and 5 m baselines. A three-axis flux-gate magnetometer measures the DC magnetic field and a three-axis search coil measures the AC magnetic field. A central signal processing system receives all signals from the electric and magnetic field sensors. Spectral coverage is from DC to 4 MHz. There are several types of processed data. Survey data are continuous over the auroral zone and have full-orbit coverage for fluxgate magnetometer data. Burst data include a few minutes of a selected region of the auroral zone at the highest time resolution. A subset of the burst data, high speed burst memory data, are waveform data at 2×10⁶ sample s^–1. Electric field and magnetic field data are primarily waveforms and power spectral density as a function of frequency and time. There are also various types of focused data processing, including cross-spectral analysis, fine-frequency plasma wave tracking, high-frequency polarity measurement, and wave-particle correlations.     

Ring Currents and Internal Plasma Sources

The discovery of terrestrial O⁺ and other heavy ions in magnetospheric hot plasmas, combined with the association of energetic ionospheric outflows with geomagnetic activity, led to the conclusion that increasing geomagnetic activity is responsible for filling the magnetosphere with ionospheric plasma. Recently it has been discovered that a major source of ionospheric heavy ion plasma outflow is responsive to the earliest impact of coronal mass ejecta upon the dayside ionosphere. Thus a large increase in ionospheric outflows begins promptly during the initial phase of geomagnetic storms, and is already present during the main phase development of such storms. We hypothesize that enhancement of the internal source of plasma actually supports the transition from substorm enhancements of aurora to storm-time ring current development in the inner magnetosphere. Other planets known to have ring current-like plasmas also have substantial internal sources of plasma, notably Jupiter and Saturn. One planet having a small magnetosphere, but very little internal source of plasma, is Mercury. Observations suggest that Mercury has substorms, but are ambiguous with regard to the possibility of magnetic storms of the planet. The Messenger mission to Mercury should provide an interesting test of our hypothesis. Mercury should support at most a modest ring current if its internal plasma source is as small as is currently believed. If substantiated, this hypothesis would support a general conclusion that the magnetospheric inflationary response is a characteristic of magnetospheres with substantial internal plasma sources. We quantitatively define this hypothesis and pose it as a problem in comparative magnetospheres.     

SAR ATR performance using a conditionally Gaussian model

A family of conditionally Gaussian signal models for synthetic aperture radar (SAR) imagery is presented, extending a related class of models developed for high resolution radar range profiles. This signal model is robust with respect to the variations of the complex-valued radar signals due to the coherent combination of returns from scatterers as those scatterers move through relative distances on the order of a wavelength of the transmitted signal (target speckle). The target type and the relative orientations of the sensor, target, and ground plane parameterize the conditionally Gaussian model. Based upon this model, algorithms to jointly estimate both the target type and pose are developed. Performance results for both target pose estimation and target recognition are presented for publicly released data from the MSTAR program     

Radio observations of Venus and the interpretations

The radio observations of Venus are reviewed and compared with theoretical microwave spectra computed for a variety of models of the Venusian environment. The models considered are (a) a CO₂-N₂ atmosphere, (b) an atmosphere of dust (the aeolosphere model), and (c) a cloud model with various loss mechanisms in the cloud. The effect of polarization on the surface emissivity has been included in all the computations. It is shown how the radio observations place limits upon the acceptable models, for example, the density and size of dust particles required in the aeolosphere model. It is shown how some models place severe restrictions on radar observations at short centimeter wavelengths, thereby emphasizing the importance of such experiments. These same models show that the Mariner II observations can not be interpreted in terms of surface phenomena and provide a new interpretation for the microwave phase effect.This work was supported in part by the U.S. Army, Navy and Air Force under Contract DA36-039-AMC-03200(E); and in part by the National Aeronautics and Space Administration (Grants NsG-250-62 and NsG-419).     

Modified Generalized Sign Test Processor for 2-D Radar

The modified generalized sign test processor is a nonparametric, adaptive detector for 2-D search radars. The detector ranks a sample under test with its neighboring samples and integrates (on a pulse-to-pulse basis) the ranks with a two-pole filter. A target is declared when the integrated output exceeds two thresholds. The first threshold is fixed and yields a 10-6 probability of false alarm when the neighboring samples are independent and identically distributed. The second threshold is adaptive and maintains a low false-alarm rate when the integrated neighboring samples are correlated and when there are nonhomogeneities, such as extraneous targets, in the neighboring cells. Using Monte Carlo techniques, probability of false-alarm results, probability of detection curves, and angular accuracy curves have been generated for this detector. The detector was built and PPI photographs are used to indicate the detector's performance when the radar is operated over land clutter.     

Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own.