Paper
4 August 2009 Infrared point target detection based on exponentially weighted RLS algorithm and dual solution improvement
Bin Zhu, Xiang Fan, Dong-hui Ma, Zheng-dong Cheng
Author Affiliations +
Abstract
The desire to maximize target detection range focuses attention on algorithms for detecting and tracking point targets. However, point target detection and tracking is a challenging task for two difficulties: the one is targets occupying only a few pixels or less in the complex noise and background clutter; the other is the requirement of computational load for real-time applications. Temporal signal processing algorithms offer superior clutter rejection to that of the standard spatial processing approaches. In this paper, the traditional single frame algorithm based on the background prediction is improved to consecutive multi-frames exponentially weighted recursive least squared (EWRLS) algorithm. Farther, the dual solution of EWRLS (DEWLS) is deduced to reduce the computational burden. DEWLS algorithm only uses the inner product of the points pair in training set. The predict result is given directly without compute any middle variable. Experimental results show that the RLS filter can largely increase the signal to noise ratio (SNR) of images; it has the best detection performance than other mentioned algorithms; moving targets can be detected within 2 or 3 frames with lower false alarm. Moreover, whit the dual solution improvement, the computational efficiency is enhanced over 41% to the EWRLS algorithm.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bin Zhu, Xiang Fan, Dong-hui Ma, and Zheng-dong Cheng "Infrared point target detection based on exponentially weighted RLS algorithm and dual solution improvement", Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 73830W (4 August 2009); https://doi.org/10.1117/12.834694
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Cited by 1 scholarly publication.
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KEYWORDS
Detection and tracking algorithms

Target detection

Signal to noise ratio

Image filtering

Infrared imaging

Image processing

Filtering (signal processing)

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