GLRT subspace detection of multi-pixel targets with known and unknown spatial parameters in presence of signal-dependent background power. (May 2016)

• Record Type: Journal Article
• Authors: Golikov, Victor; Lebedeva, Olga; Blanco, Marco Rodriguez
• Is Part Of: Computers & electrical engineering. Volume 52(2016)
• Page Start: 38
• Page End: 48
• Publication Date: 2016-05
• Subjects: Multi-pixel subspace detection -- Sequence of optical digital images -- Signal-dependent background clutter; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.04.016
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Two GLRT-based detectors are proposed for multi-frame processing. Performance study of each detector is performed. Experiments with targets on the agitated sea surface are considered. Abstract: The video/infrared images captured at long range usually have low brightness and low contrast objects of interest with respect to surrounding background clutter. In this work, we design and analyze two detectors relying on the general likelihood ratio test (GLRT) to detect weak barely discernible multi-pixel objects with known (the first detector) and unknown shape, size and position (the second detector). The derived algorithms combine the multi-pixel matched subspace detector and multi-pixel background-plus-noise power change detector in a unique scheme. The numerical simulations and real experiments show that the first detector considerably outperforms the second one, especially in real-word situation, when the size, shape and position of the object are unknown. Finally, we compare the performances of the proposed detectors to the performances of the recently proposed modified mean subtraction filter and focused correlation (FC) detector. Graphical abstract: