Automatic detection of pulsed radio frequency (RF) targets using sparse representations in undercomplete learned dictionaries

Automatic classification of transitory or pulsed radio frequency (RF) signals is of particular interest in persistent surveillance and remote sensing applications. Such transients are often acquired in noisy, cluttered environments, and may be characterized by complex or unknown analytical models. Conventional representations using orthogonal bases, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular target signal. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Our goal is to detect chirped pulses from a model target emitter in poor signal-to-noise and varying levels of simulated background clutter conditions. This paper builds on our previous RF classification work, and extends it to more complex target and background scenarios. We use a Hebbian rule to learn discriminative RF dictionaries directly from data, without relying on analytical constraints or additional knowledge about the signal characteristics. A pursuit search is used over the learned dictionaries to generate sparse classification features in order to identify time windows containing a target pulse. We demonstrate that learned dictionary techniques are highly suitable for pulsed RF analysis and present results with varying background clutter and noise levels. The target detection decision is obtained in almost real-time via a parallel, vectorized implementation.