Pulsed-Beam Wavelets are exact, causal solutions of the inhomogeneous wave equation or Maxwell's equations whose `wavelet parameters' specify physically relevant attributes of the associated pulsed beams. Their point of emission and launch time, as well as the pulse width, collimation, direction of propagation, and duration. Their time-domain radiation patterns have no sidelobes and can be made arbitrarily well-focused. We compute the source distribution necessary to synthesize such beams. It is a generalized function supported on the disk aperture, consisting of a circular line source concentrated on the rim plus a single and double layer distributed over the disk interior. We speculate that such pulsed beams may be generated physically by realizing their source distributions. If so, they could have important applications in radar, sonar, and secure communications.

A novel close form solution to resolve the directions of arrival (azimuth and elevation) of two sources using a single snapshot (monopulse) is presented, the technique is first formulated for a 2 X 2 array, and then extended to the more common amplitude comparison monopulse channels. Limitations, error analysis, and simulation results are also discussed.

We present an all-optical architecture for a fully adaptive antenna array processor capable of optimally processing the signals from very large arrays in the presence of high frequency and wideband signals. A modified version of the least mean square algorithm is employed using the BEAMTAP (Broadband and Efficient Adaptive Method for True-time-delay Array Processing) architecture. A dynamic photorefractive volume hologram is used for the adaptive weights and two cohered fiber arrays are used as tapped-delay-lines at the output and feedback paths, allowing for the processing of signals at bandwidths exceeding 10 GHz. The optical cohering of the fiber arrays is discussed and simulations are shown which describe the performance of the proposed architecture in the presence of broadband signals and multiple broadband jammers.

In this paper, we study discrete chirp-Fourier transform (DCFT), which is analogous to the Discrete Fourier transform (DFT). Besides the multiple frequency matching similar to the DFT, the DCFT can be used to match the multiple chirp rates in a chirp-type signal with multiple chirp components. We show that the magnitudes of all the sidelobes of the DCFT of a quadratic chirp signal are 1 while the magnitude of the mainlobe of the DCFT is yieldsN, where N is a prime and is the length of the signal. With this result, an upper bound for the number of the detectable chirp components using the DCFT is provided in terms of signal length, signal and noise powers. We also show that the N-point DCFT performance optimally when N is a prime.

Effective theater defense requires rapid target identification with ground sensors. Modern radar performs target recognition and target imaging tasks, in addition to conventional tasks of detection and tracking. New processing techniques, like stepped frequency waveforms and RF hardware are now becoming available and will soon result in lower- cost high resolution rate. Additional feature extraction, namely length and velocity obtained from tracker can be used to design an efficient and a rapid ID after a preliminary recognition is performed. Prior information of these features for critical set of targets can be used to design decision regions for a given SNR value.

In automatic/aided target recognition (ATR) applications using high resolution radar (HRR) range profiles (RP), storing and processing large data sets to make a target decision is faster and less costly when compression is used. In this research we focus on feature development, selection, and minimization that reduce data dimensionality and improve target identifiability using a Bayesian quadratic classifier. We explore the development and application of features based primarily on scattering centers represented in HRR range profiles for each of 5 military aircraft targets. Performance of the ATR system improved as optimum features were added producing a probability of correct classification of greater than 99% for 2 training sets of RP using no more than 22 features. Ninety-six percent or more RP were still correctly classified when the system was trained on one set of data and then classified non-training data. When an unknown target was added to either data set, 90% or greater of the range profiles were correctly declared 27.6% unknown RPs were correctly classified as unknown. Compression reduced data dimensionality by a factor of at least 36 while preserving or improving target detection and classification potential.

In naval electronic environment, pulses emitted by radars are collected by ESM receivers. For most of them the intrapulse signal is modulated by a particular law. To help the classical identification process, a classification and estimation of this modulation law is applied on the intrapulse signal measurements. To estimate with a good accuracy the time-varying frequency of a signal corrupted by an additive noise, one method has been chosen. This method consists on the Wigner distribution calculation, the instantaneous frequency is then estimated by the peak location of the distribution. Bias and variance of the estimator are performed by computed simulations. In a estimated sequence of frequencies, we assume the presence of false and good estimated ones, the hypothesis of Gaussian distribution is made on the errors. A robust non linear regression method, based on the Levenberg-Marquardt algorithm, is thus applied on these estimated frequencies using a Maximum Likelihood Estimator. The performances of the method are tested by using varied modulation laws and different signal to noise ratios.

The purpose of the research is to study the effects of three wavelet-based denoising techniques on the structure of a radar signal pulse. The radar signal pulse is 50 microsecond(s) ec in duration with 2.0 MHz of Linear Frequency Modulation on Pulse. The Signal-to-Noise Ratio of the signal is fixed at 0.7. The comparison is accomplished in the time-domain and the FFT domain. In addition, the output from a FM Demodulator is examined. The comparisons are performed based upon MSE calculations and a visual inspection of the resulting signals. A comparison between the results outlined above and an ideal bandpass filter is also performed. A final comparison is discussed which compares the wavelet- based results outlined above and the results obtained from a bandpass filter that are offset in center frequency. The wavelet-based techniques can be shown to provide an advantage in visually detecting the radar signal pulse in low SNR environments over the results obtained from a bandpass filter approach in which the ideal filter characteristics are not known. All work is accomplished in MATLAB^TM.

This paper discusses the methods for ground moving target identification (GMTI) in a bistatic ultra-wideband and wide- beam (UWB-WB) SAR system. Simulations of GMTI in UWB-WB SAR system are shown. Bistatic compensation in a time domain SAR processing system is given, and clutter leakage caused by bistatic radar and time domain fast backprojection SAR algorithms is studied. The clutter leakage is investigated both for the scatter and for the sidelobes of the scatter. In the paper we also discuss clutter leakage caused by bistatic scattering. As the scatter size increases the bistatic wave will scatter differently then the monostatic wave. Also the effect of bistatic nadir and antenna configuration is studied.

In the present work it is presented a simple method to detect target moving along runways and taxiways of an airport from images provided by a Surface Movement Radar, even with a very noisy image. The aim of the application is to determine the position of aircraft in Advanced Surface Movement Guidance and Control Systems.

In this work, we introduce a detection scheme that is able to identify regions of interest during the intermediate stages of an image formation process for ultra-wideband (UWB) synthetic aperture radar. Traditional detection methods manipulate the data after image formation. However, this approach wastes computational resources by resolving to completion the entire scene including area dominated by benign clutter. As an alternative, we introduce a multiscale focus of attention (FOA) algorithm that processes intermediate radar data from a quadtree-based backprojection image formation algorithm. As the stages of the quadtree algorithm progress, the FOA thresholds a detection statistic that estimates the signal-to-background ratio for increasingly smaller subpatches. Whenever a subpatch fails a detection, the FOA cues the image formation processor to terminate further processing of that subpatch. We demonstrate that the FOA is able to decrease the overall computational load of the image formation process by a factor of two. We also show that the new FOA method provides fewer false alarms than the two-parameter CFAR FOA over a small database of UWB radar data.

This paper builds on the method of Principal Components Analysis and its use for obtaining from a set of training image vectors a basis in which the members are rank ordered in terms of importance. The particular focus of this research is situations where the training vectors arise from images acquired at one range, call it the base range, and the image under question has been acquired at a different range. A natural question is whether one must train for all possible ranges. It is shown that, under certain assumptions, the eigenvectors for the data corresponding to ranges other than the base range may be approximated by performing a simple transformation on the eigenvectors derived from the training set at the base range. This is an important result, tending to obviate the need for acquisition of additional training patterns or for additional complex computations. It is also shown that under these assumptions the eigenvalues remain approximately constant over the different ranges even though pixel size is changed. Bounds for the errors of approximation introduced by the method are derived.

Traditional methods in image processing have not enjoyed an easy transition to radar data because most of these techniques are phase insensitive and generalizations have often not led to unique results. The desire to develop image reconstruction algorithms which are not `phase blind' has well-recognized resolution and superresolution consequences since these algorithms are typically based upon complex Fourier techniques. In addition, standard radar imaging methods have employed a linear `weak scatterer' target model to make a simple connection between target and scattered field--a model that is not always appropriate and which can cause deleterious image artifacts. Clearly, the accuracy of follow-on model appraisal requires more than simple `resolution' analysis. Recently, several important ideas have been developed which help to bridge the gap between algorithmic image resolution `enhancement' processing and usual radar image appraisal methods. We present a coordinated overview of some of the more promising of the techniques, including nonlinear Backus-Gilbert restoration and complex target modeling.

ISAR-based identification of small ships in a littoral environment must include measurement of the positions and characteristics of individual scatterers. Such measurements require a well focused complex image. Such focusing, as well as interpretability of the measurements, necessitates an adaptive choice of imaging intervals. We demonstrate an interval selection algorithm based on tracking individual scatterers. This approach offers important advantages over selecting imaging intervals on the basis of the target shape in intensity images.

In this paper, we analyze the effect of roll, pitch and yaw rotations on inverse synthetic aperture radar (ISAR) imaging. An ideal ISAR image of a target with regular motion can be derived from the image projection plane and the radar line-of-sight unit vector. Roll, pitch and yaw rotations induce time-varying Doppler shifts that can be analyzed through the rotation matrix and the effective rotation vector. We simulated the process of ISAR imaging of a rotating target. Thus, the effect of rotational motion on ISAR imaging can be observed, and the effect of individual rotation components can be studied separately. For a target with regular motion, perturbations of roll and pitch motions may make image blurring if conventional motion compensation is used. Advanced motion compensation algorithms that compensate the perturbations may improve the image.

Friedlander and Porat recently presented velocity SAR (VSAR) imaging of moving targets. In this correspondence, the VSAR is generalized to multi-frequency VSAR (MFVSAR). The MFVSAR can image both slowly and fast moving targets. Simulation results are presented to illustrate the theory.

The University of Nebraska-Lincoln has developed and field tested a coherent ultrawideband polarimetric random noise radar system that shows great promise in its ability to covertly estimate Doppler and image targets and terrain features. The system uses the technique of heterodyne correlation processing to preserve phase coherence, an essential ingredient in Doppler estimation and imaging applications. Prior work has been presented at past SPIE conferences on this topic. Some recent exciting developments have taken place that further underscore the utility of this system in operational scenarios. These include the accurate estimation of Doppler velocities, and achievement of theoretical slant-range and cross-range resolutions in SAR and ISAR imaging in an outdoor environment at approximately 200 meters range using a photonic delay line provided by SPAWAR. The experimental results are also supported by theoretical modeling and more controlled experimentation. This paper will summarize recent developments and discuss future research directions in this area.

The paper presents the analysis of polarization on the full polarimetric SAR image of San Francisco Bay for a better classification.

An ultrawideband random noise radar operating in 1 - 2 GHz frequency range has been developed at University of Nebraska. A unique heterodyne correlation technique based on delayed transmit waveform using a photonic delay line has been used to field test this system at a target range of 200 meters. In this paper, we investigate the performance of this radar from a statistical point of view, by developing the theoretical basis for the system's receiver operating characteristics. Explicit analytical expressions for the joint probability density function (PDF) of the in-phase and quadrature components of the receiver output have been obtained under the assumption that the input signals are partially correlated bandpass Gaussian processes. The PDF and cumulative distribution function for the envelope of the receiver output are obtained. These expressions are then used to relate the probability of detection and the probability of false alarm for the system for different values of sample integrated, and the results are presented in the form of graphs.

An ultrawideband random noise radar operating in the 1 - 2 GHz frequency range has been developed at the University of Nebraska-Lincoln. A unique signal processing procedure is utilized that preserves the phase of a received signal thus making it possible to use this radar as a coherent receiver. This allows the UWB random noise radar to be configured as an inverse synthetic aperture radar. Turntable experiments are presented here. HH, HV, VH, and VV data is taken giving fully polarimetric images. Image formation is performed with a short-time Fourier transform as well as with the Wigner- Ville distribution.

A coherent ultrawideband random noise radar system operating in the 1 - 2 GHz frequency range has been developed at the University of Nebraska. A unique signal processing procedure based upon heterodyne correlation techniques preserves phase coherence within the system, thereby enabling it to be used for synthetic aperture radar (SAR) imaging. The amplitude and the phase response of the system are used to form the frequency-domain target scattering profile matrix, which is then transformed into a SAR image. The ultrawideband nature of the transmit waveform presents some unique challenges in signal processing. A technique has been developed that achieves the theoretical cross-range resolution, and this method has been validated by field measurements at 200 meters range to target. Controlled close-range SAR experiments at 8 - 10 meters range clearly demonstrate the ability of the system to provide high resolution images of targets located in a cluttered background and to extract the spatial geometry of the scattering center locations. The paper will present theoretical basis for random noise SAR imaging as well as experimental results and discussion.

In this paper, ISAR images generated from measured data are compared to those from computer simulation in order to evaluate the effectiveness of ISAR-based target identification. Three sets of images are generated including: (1) motion compensated images from measured data using a joint time-frequency technique, (2) reference images from measured data and GPS-derived aircraft attitude data, and (3) synthetic images predicted by Xpatch. Visual examination and correlation analysis are undertaken to compare the three sets of images. In addition, two problem areas including JEM line corruption of the measured images and 3D rotation of the target are identified.