Synthetic aperture radar target identification based on incremental kernel extreme learning machine

Abstract

Batch learning method is usually adopted for traditional SAR target identification, but training data of a system cannot be completely acquired at one time in practical application. When a new training sample is added, the batch training method needs to retrain the whole system. In order to solve this problem, cholesky factorization principle was adopted in this paper to promote extreme learning machine to an incremental learning form and apply it in the classifier training for SAR target identification. Moreover, in allusion to disadvantageous approximation capability of traditional single kernel function, a multi-scale wavelet kernel function was established to improve classification performance thereof. Experiment results show: when new SAR target sample is obtained, this algorithm only needs to update output weight value to update the system, without any retraining; it has extremely fast speed, with identification rate higher than that of traditional kernel extreme learning machine, SVM algorithm, etc., thus becoming a good choice for the online updating of SAR target identification system.