Medium PRF radar PRF selection using evolutionary algorithms

Evolutionary algorithms are applied to the optimization of pulse repetition frequency (PRF), for both eight-and nine PRFs, in medium PRF radar while considering the detailed effects of sidelobe clutter and many other technical factors. The algorithm presented also ensures that all the solutions produced are fully decodable and have no blind velocities. The evolutionary algorithm was able to identify near-optimum PRF sets for a realistic radar system with only a modest computational effort.     

Observational Manifestation of Chaos in the Gaseous Disk of the Grand Design Spiral Galaxy NGC 3631

The main goal of the paper is to demonstrate the presence of chaotic trajectories in the gaseous disk of a real spiral galaxy. As an example we have chosen NGC 3631. First, we show the stationarity of the 3-D velocity field restored from the observed line-of-sight velocity field of the gaseous disk. That allows to analyse behaviour of the trajectories of the fluid particles (gas clouds) in the disk, calculating the corresponding observed streamlines. We estimate the Lyapunov characteristic numbers using their independence of the metrics and show the existence of chaotic trajectories outside the vortices which are present in the velocity field, and in the vicinity of the saddle point. Related spectra of the stretching numbers for some trajectories are also calculated.     

Receiver operating characteristics for the coherent UWB randomnoise radar

An ultrawideband (UWB) random-noise radar operating in the 1-2 GHz frequency band has been developed and held-tested at a 200 m range at the University of Nebraska. A unique heterodyne correlation technique based on a delayed transmitted waveform using a photonic delay line has been used to inject coherence within this system. The performance of this radar, assuming a point target, has been investigated from a statistical point of view by developing the theoretical basis for the system's receiver operating characteristics (ROC). Explicit analytical expressions for the joint probability density function (pdf) of the in-phase (I) and quadrature (Q) components of the receiver output have been derived under the assumption that the input signals are partially correlated Gaussian processes. The pdf and the complementary cumulative distribution function (cdf) for the envelope of the receiver output are also derived. These expressions are used to relate the probability of detection (P_d) to the probability of false alarm (P_f ) for different numbers of integrated samples, and the results are analyzed     

Optimum gain control for A/D conversion using digitized I/Q data inquadrature sampling

Calculation of optimum gain for minimum distortion due to A/D (analog-to-digital) conversion requires the estimation of the input signal strength. To use a common AGC (automatic gain control) for both the I/Q (in-phase and quadrature) signals, it is efficient to estimate the input signal strength using the quantized A/D output from both channels. Assuming a Gaussian input, the relationship between σ of the input of the A/D converter and E(|x|+|y|) and E(max(|x|,|y|)+1/2 min (|x|,|y|)) for t quantized I/Q output x and y is derived. Numerical results obtained using the derived expression and the statistical data obtained through simulation show excellent agreement. It is concluded that, because of its simplicity, the cubic equation obtained by fitting the numerical results should be useful     

Comments on `Tracking a maneuvering target using input estimation''

P.L. Boglar (see ibid., vol.AES-23, no.3, p.298-310, May 1987) developed a recursive formulation of the Chan, Hu, and Plant (CHP) algorithm, and the results of the simulation verified the performance improvements. The commenter claims that Bogler obtained an incorrect recursive formulation of input estimation because he incorrectly used the formulation of the Kalman filter. The commenter presents calculations that aim to demonstrate this claim     

Measurement of Distributed Targets with the Random Signal Radar

A model of a distributed target as a collection of independent, Poisson distributed point scatterers or scattering centers in a range-velocity target space is introduced and is characterized by a deterministic function called the ?scatterer density function.? This function is the density of the point scatterers in the range-velocity space and can be estimated in a relatively straightforward manner by any radar having adequate resolution in both range and velocity and no ambiguities in the region occupied by the distributed target. The use of the random signal radar with a correlator receiver is considered here and the statistical properties of the correlator output, when the return signal is from a distributed target, are derived. It is shown that the spectral density is simply related to the scatterer density function. The technique is illustrated by an example in which the target is a tornado modeled as a cylinder with constant angular velocity. The example suggests that is a possible to remotely estimate the radar cross section per unit volume as a function of distance from the center of the tornado.     

Angular Accuracy of a Scanning Radar Employing a Two-Pole Filter

A two-pole filter is proposed as a detector for a scanning radar. The optimum values of the filter coefficients are found and are approximated by a simple expression. The optimum two-pole filter requires a 0.15-dB increase in signal-to-noise ratio in order to provide the same detection capability as the optimum detector, and yields azimuth estimates whose standard deviation are within 15 percent of the Cramér-Rao lower bound. The estimator is simple to implement, avoiding the storage requirements of the moving window detector and the bias complications of the feedback integrator.     

On Optimal Control of Linear Systems in the Presence of Multiplicative Noise

This correspondence considers the problem of optimal regulator design for discrete time linear systems subjected to white state-dependent and control-dependent noise in addition to additive white noise in the input and the observations. A pseudo-deterministic problem is first defined in which multiplicative and additive input disturbances are present, but noise-free measurements of the complete state vector are available. This problem is solved via discrete dynamic programing. Next is formulated the problem in which the number of measurements is less than that of the state variables and the measurements are contaminated with state-dependent noise. The inseparability of control and estimation is brought into focus, and an "enforced separation" solution is obtained via heuristic reasoning in which the control gains are shown to be the same as those in the pseudo-deterministic problem. An optimal linear state estimator is given in order to implement the controller.