Techniques for efficiently computing digital filters with fixed coefficients, such as distributed arithmetic, achieve great arithmetic savings and are widely used. However, no computationally efficient method of implementing time-varying or LMS adaptive filters exists. We develop two techniques for efficient computation of filters that support time-varying coefficients; these methods are forms of distributed arithmetic that encode the data rather than the filter coefficients. The first approach efficiently computes scalar-vector products, with which a digital filter is easily implemented in a transpose-form structure. This method, based on digit coding, supports time-varying coefficients with no additional overhead. Alternatively, distributed-arithmetic schemes that encode the data stream in sliding blocks support efficient direct-form filter computation with time-varying coefficients. A combination of both of these techniques greatly reduces the computation required to implement LMS adaptive filters.
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering