Galileo trajectory design

The Galileo spacecraft was launched by the Space Shuttle Atlantis on October 18, 1989. A two-stage Inertial Upper Stage propelled Galileo out of Earth parking orbit to begin its 6-year interplanetary transfer to Jupiter. Galileo has already received two gravity assists: from Venus on February 10, 1990 and from Earth on December 8, 1990. After a second gravity-assist flyby of Earth on December 8, 1992, Galileo will have achieved the energy necessary to reach Jupiter. Galileo's interplanetary trajectory includes a close flyby of asteroid 951-Gaspra on October 29, 1991, and, depending on propellant availability and other factors, there may be a second asteroid flyby of 243-Ida on August 28, 1993. Upon arrival at Jupiter on December 7, 1995, the Galileo Orbiter will relay data back to Earth from an atmospheric Probe which is released five months earlier. For about 75 min, data is transmitted to the Orbiter from the Probe as it descends on a parachute to a pressure depth of 20–30 bars in the Jovian atmosphere. Shortly after the end of Probe relay, the Orbiter ignites its rocket motor to insert into orbit about Jupiter. The orbital phase of the mission, referred to as the satellite tour, lasts nearly two years, during which time Galileo will complete 10 orbits about Jupiter. On each of these orbits, there will be a close encounter with one of the three outermost Galilean satellites (Europa, Ganymede, and Callisto). The gravity assist from each satellite is designed to target the spacecraft to the next encounter with minimal expenditure of propellant. The nominal mission is scheduled to end in October 1997 when the Orbiter enters Jupiter's magnetotail.List of Acronyms ASI Atmospheric Structure Instrument - EPI Energetic Particles Instrument - HGA High Gain Antenna - IUS Inertial Upper Stage - JOI Jupiter Orbit Insertion - JPL Jet Propulsion Laboratory - LRD Lightning and Radio Emissions Detector - NASA National Aeronautics and Space Administration - NEP Nephelometer - NIMS Near-Infrared Mapping Spectrometer - ODM Orbit Deflection Maneuver - OTM Orbit Trim Maneuver - PJR Perijove Raise Maneuver - PM Propellant Margin - PDT Pacific Daylight Time - PST Pacific Standard Time - RPM Retropropulsion Module - RRA Radio Relay Antenna - SSI Solid State Imaging - TCM Trajectory Correction Maneuver - UTC Universal Time Coordinated - UVS Ultraviolet Spectrometer - VEEGA Venus-Earth-Earth Gravity Assist     

Detection in correlated Gaussian plus impulsive noise

A detector which is designed to operate in a correlated Gaussian-plus-impulsive-noise environment is presented. The detector whitens the data robustly and then uses a two-sided threshold test to determine the presence of impulsive samples. The impulsive samples are discarded, and the remaining samples are used to detect the presence or absence of a signal using a matched filter. An approximate analysis is presented, and simulations are used to demonstrate the effectiveness of this approach     

Polish radar technology

Polish radar research and development since 1953 is reviewed, covering the development and production of surveillance radars, height finders, tracking radars, air traffic control (ATC) radars and systems, and marine and Doppler radars. Some current work, including an L-band ATC radar for enroute control, a weather channel for primary surveillance radar, signal detection in non-Gaussian clutter, adaptive MTI filters and postdetection filtering, and a basic approach to radar polarimetry, is examined.<>     

Dynamics of Momentum Biased Spacecraft in a Near-Polar Orbit

The equations of motion of a momentum biased spacecraft are derived in a general form. The spacecraft is assumed to be orbiting in a near-elliptical orbit. An aerodynamic torque model which accounts for the atmospheric superrotation is assumed. The equilibrium attitude angles are obtained in terms of modified Bessel functions. Analytic expressions for the long-term motion of the momentum biased axis are derived for special cases. The analysis is applicable to the Magsat mission.     

A Fast Beamforming Algorithm for Large Arrays

This beamforming algorithm is written specifically for array radars in which the number of array elements K is very large compared with the number of jammers L the radar is designed to suppress. It uses a set of M noise vectors to construct a basis for the jammer component of the antenna output vectors. The component of the quiescent weight vector orthogonal to each basis vector is calculated, renormalized to unit length, and identified as the adapted weight vector. This algorithm is effective in the suppression of many types of jammers. The number of noise samples M required in the construction of the adapted weight vector is approximately equal to L. In the special case of L narrowband noise jammers, for example, a choice of M = L usually reduces the receiver output jammer power to a few dBs above the white noise background. It is permissible to have M相似文献   

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.     

The Effect of a Pulsed Interference Signal on an Adaptive Array

The performance of a least mean square (LMS) adaptive array in the presence of a pulsed interference signal is examined. It is shown that a pulsed interference signal has two effects. First, it causes the array to modulate the desired signal envelope (but not its phase). Second, it causes the array output signal-to-interferenceplus-noise ratio (SINR) to vary with time. The desired signal modulation is evaluated as a function of signal arrival angles, powers and interference pulse-repetition frequency (PRF) and pulsewidth. It is shown that the signal modulation is small except when the interference arrives close to the desired signal. To evaluate the effect of the time-varying SINR, it is assumed that the array is used in a differential phase-shift keyed (DPSK) communication system. It is shown that the SINR variation causes a noticeable but not disastrous increase in the bit error probability.     

Optical identification of X-ray sources in the Einstein observatory medium and deep surveys

Methods are discussed for establishing the optical identification of X ray sources in the medium and deep X-ray surveys of the Einstein Observatory. Of the 63 X-ray sources with a statistical significance of 5 in the medium survey (Maccacaro et al. 1981), optical identification work is summarized for 51, of which identifications have been made with 30 active galactic nuclei. The optical properties of some of these X-ray selected objects are briefly discussed.The Einstein deep survey of Pavo (Griffiths et al. 1981) is used to illustrate the problems and methods used for securing optical identifications for X-ray sources in the deep survey fields. Identifications have been made with 4 QSOs at the bright end of the optical candidate distribution (together with 3 G stars) and it is shown that a further 7 fainter objects are also likely to be QSOs.