Information-theoretic structure of multistatic radar imaging

Using an information theoretical perspective, we explore quantitative methods for exploiting the spatial diversity offered by multiple widely separated antennas for radar imaging applications. While decomposing the operation of multistatic radar into multiple bistatic components, we proceed to characterize relevant conditional mutual information quantities between the underlying channel and bistatic output signals. The target scene is statistically characterized to be imaged as following a GSM (Gaussian Scale Mixture) distribution with respect to a dictionary in which the image is sparse. Under these assumptions we derive a useful upper bound on the conditional mutual information structure of bistatic channels which we then deploy to optimize the transmitted waveform via a convex optimization algorithm. Simulation results demonstrate the utility of our information theoretic characterization of multistatic channels for radar imaging applications.