Tutorial: Adaptive and Distributed Radar Short Course
Presented by:
Jason T. Parker
Air Force Research Laboratory, USA
Dr. William L. Melvin
Georgia Tech Research Institute, USA
Course Description:
This short course on Adaptive and Distributed Radar will be divided into four one-hour segments. Each segment is self contained, and subsequent segments build on prior discussion. The connection between adaptive and distributed radar is made explicit.
In the first hour we discuss fundamentals. The following topics will be covered: spatial and temporal beamforming; the basics of space-time adaptive processing (STAP); and, tenets of synthetic aperture radar (SAR) and inverse imaging. In essence, we relate matched and whitening filters to the basic detection and imaging modes prevalent in modern radar.
The second segment will cover STAP in the real-world. We first discuss STAP architectures useful in practice, such as post-Doppler, pre-Doppler, and beamspace methods. We then consider several approaches to train the adaptive filter, including ad hoc selection and data-dependent methods designed for robust operation in certain types of clutter environments. We further define heterogeneous and geometry-induced nonstationary clutter and overview the corresponding impact on STAP-based radar. Key performance metrics and several measured data examples are given in this segment.
The third segment introduces the participant to compressive sensing, tomography, and knowledge-aided STAP. All three topics are timely in an era of increased interest in persistent surveillance. Compressive sensing is useful in distributed sensor environments as a means of extracting critical information with minimal stimuli. Distributed sensor topologies provide a range of angular, or tomographic, views of the target area. Knowledge-aided STAP leverages a priori information to enhance single and distributed site radar detection performance in complex clutter environments.
The final segment of the short course describes three adaptive distributed topologies: multistatic, multipass, and coherent distributed arrays. We describe the phenomenological differences between these three distributed sensor configurations and provide a common detection framework for each. In the multistatic case, we show the adaptive detector as the noncoherently summed outputs of STAP applied at each of the available sensor nodes. The coherent distributed array case occurs when the sensors are “closely” separated such that target and clutter appear correlated; we describe an adaptive MIMO processing architecture to accommodate this situation. We conclude the segment by briefly describing multipass sensor operation. Details of the data collection strategy and factors influencing performance are given.
Who should attend:
This course is intended for practicing radar engineers and program managers interested in obtaining additional insight into adaptive and distributed radar capabilities and issues.