Infrared point target detection based on exponentially weighted RLS algorithm and dual solution improvement
2009
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.
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