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Affine Projection Algorithm for Censored Regression

Non-Gaussian colored input signal and censored observation are encountered frequently in many practical applications of adaptive signal processing. Conventional adaptive algorithms will face convergence performance degradation in such cases. To address the issue, the affine projection algorithm for censored regression (CR-APA) is proposed in this brief. The censored thresholds and the variance of the background noise are regarded as the prior knowledge to compensate for the bias caused by censored observation. By theoretical analysis, the range of the step size is derived to guarantee the stabilization of the CR-APA. Besides, the method of evolving projection order is employed to improve the convergence performance of the CR-APA. Computer simulations in system identification and echo cancellation applications are performed to demonstrate the better convergence performance of the proposed algorithms in censored data processing over competing algorithms, and to verify the improvement of the CR-APA by the method of evolving projection order. The proposed CR-APA and CR-EAPA can be used in engineering applications to make up for the hardware.