Track-While-Scan Algorithm in a Clutter Environment

A track-while scan (TWS) algorithm is developed for targets in a clutter environment. The problem has been studied using only the position measurements [1, 5-8], but the simulation results have not been satisfactory. Modern processing techniques (FFT processor) ) in air traffic control and surveillance radar receivers provide both position and radial velocity. The radial velocity measurement may be conveniently used in the target-track correlation process, which will reduce the association ambiguity in the clutter environment. t. In the clear environment the algorithm using the position and radial velocity measurements has been treated in [3, 4]. A TWS algorithm, using both position and radial velocity measurements for targets in a clutter environment, is presented here. The algorithm obtained is nonlinear and adaptive. In order to evaluate the improvement due to radial velocity measurement a simulation has been performed on a digital computer. The algorithm was run with and without radial velocity measurements to compare its performances. An improvement was noted especially when the target path included an accelerated portion.     

Optical Simulation of Radar Ambiguities

Coherent optical systems, because of their basic similarity to coherent radar systems, can be used to simulate many of the characteristics of the latter. This paper discusses the use of a coherent optical system for the simulation of the range and azimuth ambiguities that sometimes occur in radar systems. The optical configurations for implementing these simulations are described in detail, and extensive experimental results are presented.     

Some early results of the keV plasma experiment on GEOS-1

Some preliminary new results are presented of the keV plasma experiment on GEOS-1. Electrons and ions have been observed to stream along the magnetic field lines in the dayside magnetosphere from the ionospheric side of the satellite towards the equatorial plane during magnetic storms, with streaming velocities corresponding to a kinetic energy of the order of a keV. The opposite streaming velocity has also been seen, but primarily in the ions only and with a smaller flux ratio for the two opposite directions along the field lines. The transition between the two opposite streaming directions, as seen by the satellite, has been found to occur even in a fraction of a second.In magnetic storm conditions azimuthal asymmetries in ion fluxes corresponding to electric field intensities of several tens of millivolts per meter have been observed.     

Efficient algorithms for finding the K best paths througha trellis

A set of algorithms is presented for finding the best set of K mutually exclusive paths through a trellis of N nodes, with worst-case computation time bounded by N³log n for a fixed-precision computation. The algorithms are based on a transformation of the K-path trellis problem into an equivalent minimum-cost network flow (MCNF) problem. The approach allows the application of efficient MCNF algorithms, which can obtain optimal solutions orders of magnitude faster than the algorithm proposed by J.K. Wolf et al. (1989). The resulting algorithms extend the practicality of the trellis formulation (in terms of required computations) to multiobject tracking problems with much larger numbers of targets and false alarms. A response by Wolf et al. is included     

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.<>     

Some crucial X-ray observations — One or two SNR(s) in Crux?

A new EXOSAT (LE/CMA) observation of the region in Crux (R.A. 11h 45m, Dec. -62°) where Markert et al. (1981) reported the existence of two x-ray SNR's is presented. After cleaning the CMA field from the point source component, due to the UV emission of the numerous stars in the field, the smoothed x-ray contours are compared to the 408 MHz radio map of Caswell et al. (1983). The existence of two, well-separate x-ray emission regions is confirmed by EXOSAT, and the current x-ray/radio picture is not sufficent to distinguish clearly between the assumption of one or two (possibly interacting) SNR's in the region.     

An Upper Bound on Detection Probability for Fluctuating Signals

In the theory of signal detectability, the signal-to-noise ratio (SNR), defined as the quotient of the average received signal energy and the spectral density of the white Gaussian noise, is a fundamental parameter. For a signal which is exactly known, or known except for a random phase, this ratio uniquely defines the detection performance which can be achieved with a matched filter receiver. However, when the signal amplitude is a random parameter, the detection performance is changed and must be determined from the probability density function (pdf) of the amplitude. Relative to the case of a constant signal amplitude, such signal amplitude fluctuation usually degrades performance when a high probability of detection (Pd) is required, but improves performance at low values of Pd; the corresponding change in the required SNR is the so-called signal fluctuation loss Lf. Thus, since Lf in some cases represents an improvement in performance for low values of Pd, a question of at least theoretical interest is: how large might this improvement be, when the class of all signal amplitude pdf's is considered. The solution, presented here, results in a lower bound on the signal fluctuation loss Lf as a function of Pd, or equivalently an upper bound on Pd as a function of SNR. The corresponding most favorable pdf was determined using the Lagrange multiplier technique and results of a numerical maximization are included to provide insight into the general properties of the solution.