Radar systems transmit an electromagnetic signal into a volume of space containing both objects of interest (targets) and objects not of interest (clutter). The signal reflects from both kinds of objects and these reflections are received by the radar along with signals produced by other sources (interference). The radar must then separate the target returns from the clutter and interference. There are currently effective techniques for adaptively suppressing clutter and interference at the receiver. Salient examples include constant false alarm rate (CFAR) detectors, adaptive antenna beam forming techniques, and space-time adaptive processing (STAP). In theory, clutter and interference suppression could be enhanced if the transmit signal was tailored to the target/clutter/interference environment. However, despite potentially significant performance gains, this kind of transmit adaptivity has not yet become a mature technology. This is principally due to the insufficient computing power and limited RF waveform generation hardware that have rendered adaptive transmit techniques impractical to implement. However, the coupling of Moore’s Law with recent advances in arbitrary waveform generation has dramatically improved the prospects of transmit adaptivity as a viable technology. We say that a system is "transmit adaptive" if it is capable of altering its transmit waveform in response to knowledge about its environment. By "environment" we mean those elements that affect system performance, such as targets, clutter, and radio frequency interference (RFI). Knowledge of the environment could be acquired a priori, estimated online, or both. There are three main reasons to investigate transmit adaptivity in radar systems: (1) performance improvement, (2) resource management, and (3) novel missions. The first reason involves improving performance subject to constraints on the transmit waveform (e.g., peak power, nice autocorrelation); the remaining two reasons focus on maintaining a minimum level of performance while either minimizing resources or performing multiple functions. For example, a transmit-adaptive system might be able to compute a transmit waveform that maximizes the probability of detection in a given RFI environment (Reason 1). Such a system might also be able to maintain the probability of detection at a desired level while using a minimal amount of transmitted power and bandwidth (Reason 2). Efficient spectrum usage afforded by such a system would provide an immense benefit in a spectrally congested environment. Ideally, an ATx system would also be capable of performing two missions simultaneously--e.g., spotlight synthetic aperture radar (SAR) and digital communications--while maintaining a minimum level of performance for each function (Reason 3). The multifunction capability afforded by WARP would be of considerable benefit in realizing one or more attributes of the Layered Sensing paradigm. Research opportunities exist in every aspect of waveform design and optimization. This includes theoretical design of waveforms, optimization algorithms, and efficient computation. Transition opportunities to investigate practical aspects of implementation and proof of concept demonstrations are available through other laboratory resources.

 

key words

Waveform agile radar; Transmit-adaptive radar; Waveform design; Waveform optimization; Simultaneous multifunctional operation;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants