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.

Networked Strong Tracking Filtering with Multiple Packet Dropouts: Algorithms and Applications

Abstract: This paper focuses on the design problem of a recursive networked strong tracking filter (NSTF) for a class of nonlinear networked systems with parameter perturbations and unknown inputs. The sensors for the networked system are allowed to be spatially distributed in a large geographical area, and signals are transmitted via a shared communication channel with limited capacity. For this kind of system structure, the measurements from different sensors may experience probabilistic data loss with different probabilities. A series of Bernoulli sequences is employed to describe the multiple packet dropout rates. Parameter perturbations and unknown inputs in the system are considered in the filter design process. A recursive networked extended Kalman filter is first derived in the least mean square sense by taking the packet dropout phenomenon into account. Then, a fading factor is introduced in the filter structure in order to cope with the parameter perturbations and unknown system inputs, and a recursive NSTF is derived by developing the so-called networked orthogonal principle. It is shown that the proposed NSTF is capable of providing satisfactory estimation results even in the presence of system parameter perturbations and/or unknown system inputs. A simulation study is carried out on a practical Internet-based three-tank system, and the estimation results show the effectiveness and applicability of the proposed filtering techniques.

Blind Adaptive Constrained Constant-Modulus Reduced-Rank Interference Suppression Algorithms Based on Interpolation and Switched Decimation

Abstract: This work proposes a blind adaptive reduced-rank scheme and constrained constant-modulus (CCM) adaptive algorithms for interference suppression in wireless communications systems. The proposed scheme and algorithms are based on a two-stage processing framework that consists of a transformation matrix that performs dimensionality reduction followed by a reduced-rank estimator. The complex structure of the transformation matrix of existing methods motivates the development of a blind adaptive reduced-rank constrained (BARC) scheme along with a low-complexity reduced-rank decomposition. The proposed BARC scheme and a reduced-rank decomposition based on the concept of joint interpolation, switched decimation and reduced-rank estimation subject to a set of constraints are then detailed. The proposed set of constraints ensures that the multipath components of the channel are combined prior to dimensionality reduction. We develop low-complexity joint interpolation and decimation techniques, stochastic gradient, and recursive least squares reduced-rank estimation algorithms. A model-order selection algorithm for adjusting the length of the estimators is devised along with techniques for determining the required number of switching branches to attain a predefined performance. An analysis of the convergence properties and issues of the proposed optimization and algorithms is carried out, and the key features of the optimization problem are discussed. We consider the application of the proposed algorithms to interference suppression in DS-CDMA systems. The results show that the proposed algorithms outperform the best known reduced-rank schemes, while requiring lower complexity.

Nonlinear model identification and adaptive model predictive control using neural networks

Abstract: This paper presents two new adaptive model predictive control algorithms, both consisting of an on-line process identification part and a predictive control part. Both parts are executed at each sampling instant. The predictive control part of the first algorithm is the Nonlinear Model Predictive Control strategy and the control part of the second algorithm is the Generalized Predictive Control strategy. In the identification parts of both algorithms the process model is approximated by a series-parallel neural network structure which is trained by a recursive least squares (ARLS) method. The two control algorithms have been applied to: 1) the temperature control of a fluidized bed furnace reactor (FBFR) of a pilot plant and 2) the auto-pilot control of an F-16 aircraft. The training and validation data of the neural network are obtained from the open-loop simulation of the FBFR and the nonlinear F-16 aircraft models. The identification and control simulation results show that the first algorithm outperforms the second one at the expense of extra computation time.

Recursive identification of bilinear systems

Abstract: Methods of identifying bilinear systems from recorded input-output data are discussed in this article A short survey of the existing literature on the topic is given ‘Standard’ methods from linear systems identification, such as least squares, extended least squares, recursive prediction error and instrumental variable methods are transferred to bilinear, input-output model structures and tested in simulation Special attention is paid to problems of stabilizing the model predictor, and it is shown how a time-varying Kalman filter and associated parameter estimation algorithm can deal with this problem