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     

Detection performance in K-distributed and correlatedRayleigh clutters

Performance prediction for a detection system employing noncoherent integration is carried out for a chi-square family of fluctuating targets in K-distributed clutter plus noise. The detection performance for Swerling 11 targets in the K-distributed clutter plus noise is compared with that in exponentially correlated Rayleigh clutter. The results show that the performance prediction based on N pulses integrated in clutter plus noise using the K-distributed clutter model may be approximately equivalent to that using the exponentially correlated Rayleigh-distributed clutter model     

Detection loss due to interfering targets in ordered statisticsCFAR

The ordered-statistics (OS) constant false-alarm rate (CFAR) is relatively immune to the presence of interfering targets among the reference cells used to determine the average background. OS CFAR performance in a multitarget environment was previously studied by simulation. The author obtains analytic expressions for the added detection loss, assuming strong interfering targets. The real target is assumed to be a Rayleigh fluctuating target. Numerical examples are included     

Filtering of discontinuous processes arising in marine integratednavigation systems

A refined stochastic model for the errors of the Loran-C radio navigation aid is described, and it is shown how this model can be used to improve the performance of integrated navigation systems. In addition to the usual propagation errors, Loran-C time of arrival measurements are occasionally plagued with sudden intermittent errors of a particular magnitude and caused by receiver cycle selection errors. These result in sudden large jumps in the calculated position solution. The Loran-C error has been modeled as the sum of a diffusion process, representing the normal propagating errors, and a pure jump process of Poisson type, representing the cycle selection errors. A simple integrated navigation system is then described, based on the Loran-C model and the standard dead reckoning (heading and speed) system model. Assuming that the observed process is governed by a linear stochastic difference equation, a recursive linear unbiased minimum variance filter is developed, from which the Loran-C and dead reckoning errors, and hence position and velocity, can be estimated     

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.     

Position and Velocity Estimation Via Bearing Observations

The problem of performing target motion analysis using noisy bearing measurements derived from multiple observation platforms or from a single moving observer is addressed. For the latter case, the properties of an estimator based on a Cartesian model of the process are detailed. Methods of providing estimates both before an observer maneuver, when the process is unobservable, and following an observer maneuver are developed. The results of an experimental study are presented.