Clutter background suppression is always a difficulty of infrared (IR) dim and point target detection.Background suppression is divided into background estimation and difference filtering.Aiming at the nonlinear distribution of strong clutter background

a spares kernel recursive least squares (KRLS) based nonlinear background suppression algorithm is proposed.This method uses sequence images as training sample in supervised learning model.The complexity of learned function is controlled

and the redundant information is discarded by sparsification.In this way

the generalization of learning machine can be enhanced;moreover

the computational burden can be reduced.In the experiments

real IR images are used to test the algorithm

and the parameters are analyzed.Experimental results show that different kinds of strong clutter background can be estimated

and then be suppressed.