Scalar and vector multistatic radar data models

Abstract

The aim of this thesis is to further the theory for multistatic imaging of moving targets through the development and simulation of scalar and vector radar data models and accompanying imaging operations. In the first part of the thesis we investigate scalar representations of multistatic radar data. We begin by comparing two different approaches for developing a multistatic ambiguity function (MAF), a tool used to assess performance of the waveforms and geometry of a multistatic radar system jointly. One approach is deterministic in nature, originating from the scalar wave equation, and the other is statistical, relying on a Neyman-Pearson defined weighting of received data. Although the two methods are fundamentally different in formulation, they are shown to yield similar results. We then build on the data model for the existing deterministically derived MAF with the inclusion of antenna beam patterns by relating the current density on the radiating and receiving antennas to a far-field spatial weighting factor. From this model we develop an imaging formula in position and velocity that can be interpreted in terms of filtered backprojection or matched filtering and a corresponding ambiguity function or point-spread function. We use the resulting data model and MAF to examine scenarios with various geometries and transmit waveforms and we show that the performance of a multistatic system depends critically on the system geometry and transmitted waveforms.