A Bayesian Perspective on Sparse Regularization for STAP Post-Processing
Abstract
Traditional Space Time Adaptive Processing (STAP) formulations cast the problem as a detection task which results in an optimal decision statistic for a single target in colored Gaussian noise. In the present work, inspired by recent theoretical and algorithmic advances in the field known as compressed sensing, we impose a Laplacian prior on the targets themselves which encourages sparsity in the resulting reconstruction of the angle/Doppler plane. By casting the problem in a Bayesian framework it becomes readily apparent that sparse regularization can be applied as a post-processing step after the use of a traditional STAP algorithm for clutter estimation. Simulation results demonstrate that this approach allows closely spaced targets to be more easily distinguished.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2010
- Accession Number
- ADA539886
Entities
People
- Jason T. Parker
- Lee C Potter
Organizations
- Air Force Research Laboratory