A version of recursive least-squares (RLS) adaptive algorithms, correlation-domain recursive least-squares (CDRLS) algorithms, is presented for identification applications using two correlation vectors to track the information. It is shown that in the CDRLS, various data windows can be manipulated simply by adjusting the correlation vectors in different ways, leading to the flexibility in exact initialization and real-time variation of the window length. In addition, the CDRLS is block-processing applicable and numerically stable. Two typical identification illustrations are provided. >

The correlation-domain recursive least-squares algorithms for identification

The authors present a method to efficiently implement the recursive instrumental variable (RIV) algorithm, which is often encountered in many practical applications. A sequential block-processing algorithm is derived, and its fast Fourier transform (FFT) implementation is discussed. Under several easily met conditions, this method can offer a remarkable computational advantage over the previous fast-RIV. To accommodate the slowly time-varying input situations, an exponentially windowed extension of the result is also given. >

http://ieeexplore.ieee.org/iel2/844/1812/00048618.pdf

Xiao-Hu Yu

Papers

A fast sequential block-processing instrumental variable algorithm and its FFT implementation

The correlation-domain recursive least-squares algorithms for identification