Jon Mitchell

Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Electrical Engineering


Electrical Engineering

First Advisor

Saibun Tjuatja


Clutter is common in applications of radar imaging and can adversely impact target imaging by contributing scattered energy that is not accounted for in target signal models. One potential source of clutter is moving foliage in the vicinity of the target, such as a target embedded in a forest. ISAR imaging of moving clutter results in an equivalent current image that changes over each imaging sample. The stochastic nature of this clutter equivalent current presents challenges in detecting and imaging a weak embedded target using traditional algorithms. This dissertation proposes a multiscale model and analysis method to characterize the multiscale statistical properties of the clutter equivalent current density. It is hypothesized that clutter scattering phenomenon is related to vegetation structure, and the resulting multiscale properties of the projected clutter equivalent current can be modeled and analyzed to reveal these clutter scattering characteristics. Simulation methods are proposed which use these multiscale characteristics to generate additional representative samples of the ISAR clutter equivalent current. The proposed analysis and simulation methods are validated using known simulation data and applied to physical clutter ISAR measurements. Simulation fidelity of the two proposed simulation methods is evaluated with image similarity measures. Hypotheses relating multiscale characteristics of the multiscale model to scattering phenomenon are proposed and tested. Finally, applications for analysis and simulation are presented, demonstrating the value of the proposed methods in a variety of real problems.


ISAR, Clutter, Modeling, Multiscale analysis, Deep learning, Convolutional neural networks


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington