Abstract

In the article, we propose a dictionary learning algorithm for denoising Chirp signal. Under the influence of additive white Gaussian, some dectionary learning algorithms, such as K-SVD and RLS-DLA, cannot effectively remove the noise of Chirp signal. Therefore we present a novel dictionary learning algorithm for denoising Chirp signal. Our principal contribution is modifying the dictionary update stage of the RLS-DLA algorithm by using non-linear least squares in the algorithm. Signal to noise ratio (SNR) obtained by using the novel dictionary learning algorithm is obviously higher than other algorithms, and mean squares error (MSE) obtained by using the novel dictionary learning algorithm is obviously lower than other algorithms. Therefore there is obviously denoising performance for using the dictionary learned by the algorithm to sparsely represent Chirp signal.

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