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.

Performance advantage of complex LMS for controlling narrow-band adaptive arrays

Abstract: In narrow-band adaptive-array applications, the mean-square convergence of the discrete-time real least mean-square (LMS) algorithm is slowed by image-frequency noises generated in the LMS loops. The complex LMS algorithm proposed by Widrow et aL is shown to eliminate these noises, yielding convergence of the mean-squared error (MSE) at slightly over twice the rate. This paper includes a comprehensive analysis of the MSE of adaptation for LMS. The analysis is based upon the method developed in the 1968 dissertation by K. D. Senne, and it represents the most complete treatment of the subject published to date.

A Method for Recursive Least Squares Filtering Based Upon an Inverse QR Decomposition

Abstract: A new computationally efficient algorithm for re- cursive least squares filtering is derived, which is based upon an inverse QR decomposition. The method solves directly for the time-recursive least squares filter vector, while avoiding the highly serial backsubstitution step required in previously de- rived direct QR approaches. Furthermore, the method employs orthogonal rotation operations to recursively update the filter, and thus preserves the inherent stability properties of QR ap- proaches to recursive least squares filtering. The results of sim- ulations over extremely long data sets are also presented, which suggest stability of the new time-recursive algorithm. Finally, parallel implementation of the resulting method is briefly dis- cussed, and computational wavefronts are displayed.

Multipath Mitigation of Continuous GPS Measurements Using an Adaptive Filter

Abstract: Though state-of-the-art dual-frequency receivers are employed in the continuous Global Positioning System (CGPS) arrays, the CGPS coordinate time series are typically very noisy due to the effects of atmospheric biases, multipath, receiver noise, and so on, with multipath generally being considered the major noise contributor. An adaptive finite-duration impulse response filter, based on a least-mean-square algorithm, has been developed to derive a relatively noise-free time series from the CGPS results. Furthermore, this algorithm is suitable for real-time applications. Numerical simulation studies indicate that the adaptive filters is a powerful signal decomposer, which can significantly mitigate multipath effects. By applying the filter to both pseudorange and carrier phase multipath sequences derived from some experimental GPS data, multipath models have been reliably derived. It is found that the best multipath mitigation strategy is forward filtering using data on two adjacent days, which reduces the standard deviations of the pseudorange multipath time series to about one fourth its magnitude before correction and to about half in the case of carrier phase. The filter has been successfully applied to the pseudorange multipath sequences derived from CGPS data. The benefit of this techniques is that the affected observable sequences can be corrected, and then these corrected observables can be used to improve the quality of the GPS coordinate results. © 2000 John Wiley & Sons, Inc.

A Discrete-Time Model of Electrcally Stimulated Muscle

Abstract: A model describing the input/output properties of electrically stimulated isometric muscle is developed and experimentally tested. A discrete-time model gives the force output at the times of stimulation during pulse width modulation of recruitment at fixed stimulus amplitudes and periods. Two elements are necessary in the model: a static nonlinear element followed by a linear dynamic element. The static nonlinearity describes the relationship between pulse width and steady-state force. The dynamic properties are described with less than 10 percent error by a second-order discrete-time deterministic autoregressive moving average (DARMA) model. Exponentially weighted recursive least squares methods allow efficient parameter estimation. Model parameters are found to vary systematically with muscle length and stimulus frequency. Tests comparing actual and simulated feedback control of electrically stimulated muscle indicate that the model is adequate for digital controller design for applications in functional electrical stimulation.