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Aimed at the shortcoming that the loss of low-frequency information of alternating current millimeter-wave radiometer signal, relevance vector machine (RVM) algorithm is used to compensate the lost component in discrete cosine transform (DCT) domain, and through inverse discrete cosine transform (IDCT) we can receive the compensated signal. RVM exploits Bayesian learning framework, which has dramatically fewer kernel functions than comparative support vector machine. So that accurate prediction models can be acquired. Experimental results also show that this method can obtain good compensation effect.
Aimed at the shortcoming that the loss of low-frequency information of alternating current millimeter-wave radiometer signal, relevance vector machine (RVM) algorithm is used to compensate the lost component in discrete cosine transform (DCT) domain, and through inverse discrete cosine transform (IDCT) we can receive the compensated signal. RVM exploits Bayesian learning framework, which has significantly fewer kernel functions than comparative support vector machine. So that the accurate prediction models can be acquired. Experimental results also show that this method can obtain good compensation effect.