Rapid waveform adaptation for nearly optimal detection in colored interference

A cognitive radar can dynamically design its transmit waveform in response to changing environmental knowledge, which may be obtained a priori, estimated online, or both. We consider the detection of targets in wide-sense stationary additive colored Gaussian noise. Cognitive radar has been shown to provide potentially significant improvements for this problem. However, existing algorithms may be too computationally demanding for some scenarios. We present an approach that can be implemented in the most demanding scenarios. This approach trades optimality for reduced computational complexity by computing a large library of nearly optimal waveforms before operation, and retrieving them rapidly at runtime.