Radar imaging via random FM correlations

Our goal is to study the properties of a random FM process correlator as applied to radar imaging. The driver of the transmitted FM signal is gaussian, bandlimited random process while the initial phase of the process is uniformly distributed. Thus, the FM process is wide-sense stationary. For wideband modulation, both the autocorrelation and spectrum of the FM process are approximately gaussian as inferred from Woodward's adiabatic principle. Since the half power bandwidth of the process is linearly proportional to the modulation index lambda, the range-delay resolution is inversely proportional to lambda. We make use of Monte Carlo simulations to illustrate the stationary nature of the correlator output. Radar imaging of rotating targets is implemented using a microwave tomography algorithm, which requires data collection for a finite number of viewing angles. We demonstrate that the self-noise power in this type of imagery is controlled by the number of samples processed and the number of signal realizations included in calculating the autocorrelations.