Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.

Decision-directed recursive least squares MIMO channels tracking

Abstract: A new approach for joint data estimation and channel tracking for multiple-input multiple-output (MIMO) channels is proposed based on the decision-directed recursive least squares (DD-RLS) algorithm. RLS algorithm is commonly used for equalization and its application in channel estimation is a novel idea. In this paper, after defining the weighted least squares cost function it is minimized and eventually the RLS MIMO channel estimation algorithm is derived. The proposed algorithm combined with the decision-directed algorithm (DDA) is then extended for the blind mode operation. From the computational complexity point of view being O(3) versus the number of transmitter and receiver antennas, the proposed algorithm is very efficient. Through various simulations, the mean square error (MSE) of the tracking of the proposed algorithm for different joint detection algorithms is compared with Kalman filtering approach which is one of the most well-known channel tracking algorithms. It is shown that the performance of the proposed algorithm is very close to Kalman estimator and that in the blind mode operation it presents a better performance with much lower complexity irrespective of the need to know the channel model.

Unified approach to adaptive filters and their performance

Abstract: A streamlined theory is presented for adaptive filters within which the major adaptive filter algorithms can be seen as special cases. The algorithm development part of the theory involves three ingredients: a preconditioned Wiener Hopf equation, its simplest possible iterative solution through the Richardson iteration, and an estimation strategy for the autocorrelation matrix, the cross-correlation vector and a preconditioning matrix. This results in a generalised adaptive filter in which intuitively plausible parameter selections give the major adaptive filter algorithms as special cases. This provides a setting where the similarities and differences between the many different adaptive filter algorithms are clearly and explicitly exposed. Based on the authors' generalised adaptive filter, expressions for the learning curve, the excess mean square error and the mean square coefficient deviation are developed. These are general performance results that are directly applicable to the major families of adaptive filter algorithms through the selection of a few parameters. Finally, the authors demonstrate through simulations that these results are useful in predicting adaptive filter performance.

On-line identification and control of pneumatic servo drives via a mixed-reality environment

Abstract: This paper presents a method to identify and control electro-pneumatic servo drives in a real-time environment. Acquiring the system’s transfer function accurately can be difficult for nonlinear systems. This causes a great difficulty in servo-pneumatic system modeling and control. In order to avoid the complexity associated with nonlinear system modeling, a mixed-reality environment (MRE) is employed to identify the transfer function of the system using a recursive least squares (RLS) algorithm based on the auto-regressive moving-average (ARMA) model. On-line system identification can be conducted effectively and efficiently using the proposed method. The advantages of the proposed method include high accuracy in the identified system, low cost, and time reduction in tuning the controller parameters. Furthermore, the proposed method allows for on-line system control using different control schemes. The results obtained from the on-line experimental measured data are used to determine a discrete transfer function of the system. The best performance results are obtained using a fourth-order model with one-step prediction.

Echo Cancellation of Voiceband Data Signals Using Recursive Least Squares and Stochastic Gradient Algorithms

Abstract: The convergence properties of adaptive least squares (LS) and stochastic gradient (SG) algorithms are studied in the context of echo cancellation of voiceband data signals. The algorithms considered are the SG transversal, SG lattice, LS transversal (fast Kalman), and LS lattice. It is shown that for the channel estimation problem considered here, LS algorithms converge in approximately 2N iterations where N is the order of the filter. In contrast, both SG algorithms display inferior convergence properties due to their reliance upon statistical averages. Simulations are presented to verify this result, and indicate that the fast Kalman algorithm frequently displays numerical instability which can be circumvented by using the lattice structure. Finally, the equivalence between an LS algorithm and a fast converging modified SG algorithm which uses a maximum length input data sequence is shown.