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     

Gravitation and celestial mechanics investigations with Galileo

The gravitation and celestial mechanics investigations during the cruise phase and Orbiter phase of the Galileo mission depend on Doppler and ranging measurements generated by the Deep Space Network (DSN) at its three spacecraft tracking sites in California, Australia, and Spain. Other investigations which also rely on DSN data, and which like ours fall under the general discipline of spacecraft radio science, are described in a companion paper by Howard et al. (1992). We group our investigations into four broad categories as follows: (1) the determination of the gravity fields of Jupiter and its four major satellites during the orbital tour, (2) a search for gravitational radiation as evidenced by perturbations to the coherent Doppler link between the spacecraft and Earth, (3) the mathematical modeling, and by implication tests, of general relativistic effects on the Doppler and ranging data during both cruise and orbiter phases, and (4) an improvement in the ephemeris of Jupiter by means of spacecraft ranging during the Orbiter phase. The gravity fields are accessible because of their effects on the spacecraft motion, determined primarily from the Doppler data. For the Galilean satellites we will determine second degree and order gravity harmonics that will yield new information on the central condensation and likely composition of material within these giant satellites (Hubbard and Anderson, 1978). The search for gravitational radiation is being conducted in cruise for periods of 40 days centered around solar opposition. During these times the radio link is least affected by scintillations introduced by solar plasma. Our sensitivity to the amplitude of sinusoidal signals approaches 10^-15 in a band of gravitational frequencies between 10^-4 and 10^-3 Hz, by far the best sensitivity obtained in this band to date. In addition to the primary objectives of our investigations, we discuss two secondary objectives: the determination of a range fix on Venus during the flyby on 10 February, 1990, and the determination of the Earth's mass (GM) from the two Earth gravity assists, EGA1 in December 1990 and EGA2 in December 1992.     

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     

Cross-correlation properties of algebraically constructed Costasarrays

The problem of determining the cross-correlation properties of signals based on algebraically constructed Costas arrays is addressed by examining the discrete cross-correlation of the algebraically constructed Costas arrays for a given construction and dimension. Finding two arrays that minimally correlate implies that the signals based on these arrays also minimally correlate. The properties of finite fields are reviewed, and the major algebraic constructions for Costas arrays are presented, i.e. the Welch construction and the Golomb construction. The discrete cross-correlation properties of the Costas arrays are derived for arrays of the same dimension derived from the same construction. The use of Costas arrays in the signal design problem is discussed, and examples are given to show the cross-correlation of the signals based on the algebraically constructed arrays     

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.<>     

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   

An Adaptive Array Signal Processing Algorithm for Communications

An algorithm is described for initial synchronization in a communication system with a digital adaptive array. This algorithm can also be used for message extraction. A set of consecutive complex video samples of the array output is processed to obtain optimum adaptive array weights, based on a least mean square (LMS) error criterion. This computation is performed for each of the possible alternative signals which may be present during an observation interval. The correct synchronization time or message symbol is selected as the one which yields the minimum LMS error. Assuming orthogonality of the alternative codes, a probability distribution for the output of this processor has been derived.     

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.     

Adaptive False Alarmrm Regulation in Double Threshold Radar Detection

In practical situations the false alarm probability in double threshold radar detection, sometimes known as binary integration with sliding window detection, is dependent on the nonstationarity and azimuthal correlation of the clutter which is present. Control of the false alarm probability can be achieved, to a certain extent, by the adjustment of the second threshold in the detection process. In this study two adaptive control techniques which are based on the statistical characteristics of the data are compared. Comparing the results for a technique based on first-order statistics with one based on second-order statistics, it is shown that the second-order, or correlation sensitive, technique can give a reduction of 30 to 45 percent in the false alarm probability with no corresponding loss in the detection probability. An interesting aspect of the results is the fact that the effects of the size of the sample area and the bias in the correlation estimator are clearly evident.