Single frequency pulse, linear frequency pulse, and stepped frequency pulse are a few of the radar pulse waveforms used to obtain target range information. Using a basic single frequency pulse limits the radar's range resolution by the pulse width, has excessive energy requirements, and is more vulnerable to jamming. With the use of frequency modulation the radar's range resolution can be greatly enhanced. This report deals with some of the issues involved in using stepped frequency pulse trains to obtain high resolution target range and range autocorrelation profiles. Radar returns from stepped frequency pulse trains may be coherently processed to obtain the range profile, or noncoherently processed to obtain the range autocorrelation (or separation) profile. The inverse discrete Fourier transform (DFT-1) of N coherently detected pulse returns from each range cell (where N is the number of pulses in the train) gives the high resolution range profile within that range cell. The DFT of the squared magnitudes of the pulse returns yields the autocorrelation of the range profile (the separation profile). Range resolution is determined by the total bandwidth of the pulse train. Using a Fast Fourier Transform (FFT) algorithm for the DFT a simulation of the radar system is implemented on the University of Central Florida, College of Engineering Research VAX 11/750 computer. This simulation computes and plots the range and separation profiles.

Thesis (MSc (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2006. / In this work we present a novel method to do measurement correlation between target observations made by two ormore radar systems. Some of the most common radar sensors available are those measuring only range (distance to the target) and bearing (azimuth angle). We use these measurements to determine the correlation between two di¤erent sensors observing the same target. As a by-product of the correlation algorithm, we nd a way to estimate the target height for a target observed by at least two radar sensors. The correlation method is expounded upon, where we discuss measurement correlation for moving targets. Targets are tracked using a Kalman Filter, and correlation is done between new observations and existing target tracks. Finally, the correlation algorithm is implemented in an interactive 3D computer simulation. Results obtained indicate a high success rate, with false correlations only obtained where sensor accuracy is the limiting factor.