Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Electrical and Computer Engineering


Simon Haykin


The problem of radar modeling is of critical importance to Electronic Warfare applications such as radar recognition and threat analysis. As modern radar signals become steadily more complex, so do the issues associated with radar modeling and signal processing. Traditional, data-centric approaches to radar signal processing can no longer cope with the increasing complexity of radar signals. The main contribution of this thesis is the novel, model-centric approach to radar signal processing that utilizes methods from the theory of formal languages and syntactic pattern recognition.

In this thesis, we focus our attention on modeling of Multi-Function Radars (MFRs)---the class of radars that currently presents the greatest challenges to the radar signal processing community. The characteristic feature of MFRs is the complex hierarchical signal structure often utilized by these radars. This complexity in MFR signals makes the classic radar signal processing techniques inadequate.

We consider MFRs as stochastic discrete event systems that are "communicating" information using some stochastic formal languages. We then show how these languages can be modeled by grammars that can be derived using a priori information available in the databases of electronic intelligence. We also demonstrate how these grammars can capture the complex MFR signal structures and exploit the relationships between the internal processes within MFRs and signals emitted by these radars. We refer to this MFR modeling approach as "syntactic modeling".

We also take advantage of the hierarchical nature of the MFR signals and develop a layered radar model where processing of related features of radar signals is confined to a certain modeling layer, and only the information relevant to the next layer of radar signal processing in propagated forward. This hierarchical radar modeling approach enables to keep the complexity of MFR models manageable.

We demonstrate the applicability of the developed approach using computer simulations of synthetic MFR signals and provide two complete case studies demonstrating how the principles developed in this thesis can be applied to modeling of real-life MFRs.

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