Fast Detection of Compressively Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters

Brian Millikan, Aritra Dutta, Qiyu Sun, Hassan Foroosh

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Target detection of potential threats at night can be deployed on a costly infrared focal plane array with high resolution. Due to the compressibility of infrared image patches, the high resolution requirement could be reduced with target detection capability preserved. For this reason, a compressive midwave infrared imager (MWIR) with a low-resolution focal plane array has been developed. As the most probable coefficient indices of the support set of the infrared image patches could be learned from the training data, we develop stochastically trained least squares (STLS) for MWIR image reconstruction. Quadratic correlation filters (QCF) have been shown to be effective for target detection and there are several methods for designing a filter. Using the same measurement matrix as in STLS, we construct a compressed quadratic correlation filter (CQCF) employing filter designs for compressed infrared target detection. We apply CQCF to the U.S. Army Night Vision and Electronic Sensors Directorate dataset. Numerical simulations show that the recognition performance of our algorithm matches that of the standard full reconstruction methods, but at a fraction of the execution time.
Original languageEnglish (US)
Pages (from-to)2449-2461
Number of pages13
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume53
Issue number5
DOIs
StatePublished - May 2 2017
Externally publishedYes

Fingerprint Dive into the research topics of 'Fast Detection of Compressively Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters'. Together they form a unique fingerprint.

Cite this