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.

Adaptive control of linear stochastic evolution systems

Abstract: An adaptive control problem for some linear stochastic evolution systems in Hilbert spaces is formulated and solved in this paper. The solution includes showing the strong consistency of a family of least squares estimates of the unknown parameters and the convergence of the average quadratic costs with a control based on these estimates to the optimal average cost. The unknown parameters in the model appear affinely in the infinitesimal generator of the C 0 semigroup that defines the evolution system. A recursive equation is given for a family of least squares estimates and the bounded linear operator solution of the stationary Riccati equation is shown to be a continuous function of the unknown parameters in the uniform operator topology

MILES: MATLAB package for solving Mixed Integer LEast Squares problems

Abstract: In GNSS, for fixing integer ambiguities and estimating positions, a mixed integer least squares problem has to be solved. The Matlab package MILES provides fast and numerically reliable routines to solve this problem. In the process of solving a mixed integer least squares problem, an ordinary integer least squares problem is solved. Thus this package can also be used to solve an ordinary integer least squares problem alone. An option to compute multiple solutions is provided. This paper gives a description of this package and provides a guide for using it.

An Enhanced Equivalent Circuit Model with Real-Time Parameter Identification for Battery State-of-Charge Estimation

Abstract: This paper introduces an efficient modeling approach based on Wiener structure to reinforce the capacity of the classical Equivalent Circuit Models (ECMs) in capturing the nonlinearities of Lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This paper also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4 Ah 3.6 V NMC-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method.

RLS Algorithm with Variable Fogetting Factor for DecisionFeedback Equalizer over Time-Variant Fading Channels

Abstract: In a high-rate indoor wireless personal communication system, the delay spread due to multipath propagation results in intersymbol interference (ISI) which can significantly increase the transmission bit error rate (BER). Decision feedback equalizer (DFE) is an efficient approach to combating the ISI. Recursive least squares (RLS) algorithm with a constant forgetting factor is often used to update the tap-coefficient vector of the DFE for ISI-free transmission. However, using a constant forgetting factor may not yield the optimal performance in a nonstationary environment. In this paper, an adaptive algorithm is developed to obtain a time-varying forgetting factor. The forgetting factor is used with the RLS algorithm in a DFE for calculating the tap-coefficient vector in order to minimize the squared equalization error due to input noise and due to channel dynamics. The algorithm is derived based on the argument that, for optimal filtering, the equalization errors should be uncorrelated. The adaptive forgetting factor can be obtained based on on-line equalization error measurements. Computer simulation results demonstrate that better transmission performance can be achieved by using the RLS algorithm with the adaptive forgetting factor than that with a constant forgetting factor previously proposed for optimal steady-state performance or a variable forgetting factor for a near deterministic system.

Lms and rls algorithms for smart antennas in a w-cdma mobile communication environment

Abstract: Wireless mobile communication systems will be more sophisticated and wide spread in future. This growth demands not only for capacity but also high quality of service and better coverage without increase in radio frequency spectrum allocated for mobile applications. Wireless systems used fixed antenna systems in the past, but space division multiple access systems use smart antennas. These smart antennas dynamically adapt to changing traffic requirements. Smart antennas are usually employed at the base station and radiate narrow beams to serve different users. The complex weight computations based on different criteria are incorporated in the signal processor in the form of software algorithms. This article focuses on adaptive beam forming approach based on smart antennas and adaptive algorithms used to compute the complex weights like Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms.