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Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
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Operation of the Collaborative Composite Manufacturing (CCM) System

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Published on: October 1, 2019

Robust adaptive feedback canceller based on modified pseudo affine projection algorithm.

Keunsang Lee¹, Yong-hyun Baik, Youngcheol Park

¹Computer & Telecomm. Eng Division, Yonsei University, 1 Yonseidae-gil Wonju, Kwangwon-do, Korea. young00@yonsei.ac.kr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 19, 2012

Summary

This summary is machine-generated.

This study introduces a robust adaptive feedback cancellation (AFC) algorithm. It enhances stability in pseudo affine projection (PAP) algorithms for improved performance in signal processing applications.

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Area of Science:

Digital Signal Processing
Acoustic Echo Cancellation

Background:

Adaptive feedback cancellation (AFC) is crucial for mitigating unwanted signal feedback.
Existing pseudo affine projection (PAP) algorithms offer computational efficiency similar to NLMS but can suffer from instability in AFC due to delayed coefficient estimates.
Linear prediction (LP) coefficient estimation is a key component in PAP-based AFC systems.

Purpose of the Study:

To develop a novel adaptive feedback cancellation (AFC) algorithm with enhanced robustness and stability.
To address the instability issue in PAP-based AFC algorithms caused by delayed linear prediction (LP) coefficient estimation.
To achieve stable and reliable feedback cancellation performance comparable to or better than existing methods.

Main Methods:

The proposed algorithm is based on the pseudo affine projection (PAP) algorithm.
An inverse gain filter (IGF) is utilized before the adaptive filter update to mitigate instability.
Linear prediction (LP) coefficients are estimated directly from the input signal, avoiding estimation delays.

Main Results:

Simulation results demonstrate the robust and stable performance of the proposed AFC algorithm.
The integration of the IGF and delay-free LP coefficient estimation effectively resolves the instability problem.
The algorithm maintains computational complexity comparable to the Normalized Least Mean Squares (NLMS) algorithm.

Conclusions:

The proposed AFC algorithm offers a stable and robust solution for feedback cancellation.
The novel approach using IGF and direct LP coefficient estimation significantly improves PAP algorithm performance in AFC.
This work contributes to the advancement of stable and efficient adaptive filtering techniques.