Radar Signal Processing

Research Highlights of
Information Processing & Algorithms Laboratory


The Information Processing and Algorithms Laboratory (iPAL) is directed by Prof. Vishal Monga. Graduate research in iPAL broadly encompasses signal and image processing theory and applications with a particular focus on capturing practical real-world constraints via convex optimization theory and algorithms.


Statistical Signal Processing and Optimization with Applications to Radar

Estimating the disturbance or clutter covariance is a centrally important problem in radar space-time adpative processing (STAP) since since estimation of the disturbance or interference covariance matrix plays a central role on radar target detection in the presence of clutter, noise and a jammer. Traditional maximum likelihood (ML) estimators are effective when homogeneous (target free) training data is abundant.

Learning methods for single and multi-channel SAR/hyperspectral Automatic Target Recognition

Remote sensed imaging, such as synthetic aperture radar (SAR), infra-red (IR), hyperspectral etc. has seen vast improvements in the last decade due to advances in sensors and capture technology. Likewise, sophisticated image analysis and classification techniques may now be used to mine objects of interests in these images. Among the most promising methods for object, e.g. target, detection in remote sensed imagery, sparse representation based methods have shown an impressive resiliency to noise, blur, and occlusion. However, determining the best features and lack of training data continue to be outstanding open challenges in ATR.


104 Electrical Engineering East,
University Park, PA 16802, USA

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