Abstract
Infrared (IR) small target detection in heavy noise has always been a challenging research task. Detection methods based on energy accumulation can effectively suppress noise and improve the signal-to-noise ratio (SNR). Unfortunately, the detection performance of such dim and small target detection methods is limited due to the lack of accurate target motion modeling. In this paper, considering that the target motion is complex and diverse, a high-dimensional motion parameter (HMP) target motion model is studied. The HMP target motion model can cover various target motion forms, including constant velocity (CV) linear motion, constant acceleration (CA) linear motion and constant velocity curvilinear motion (CVC). Then, the SNR gain based on the energy accumulation detection algorithm under HMP is analyzed in detail. Experimental results show that when the target motion is non-linear, the energy accumulation method based on the HMP motion model can more effectively improve the SNR compare to the energy accumulation method based on CA motion model.
Published Version
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