Joint Optimization of Cooperative Spectrum Sensing and Resource Allocation in Multi-channel Cognitive Radio Sensor Networks

Intl J Robust & Nonlinear

2019

121

Summary This paper is concerned with the joint estimation of states and parameters of a special class of nonlinear systems, ie, bilinear systems. The key is to investigate new estimation methods for interactive state and parameter estimation of the considered system based on the interactive estimation theory. Because the system states are unknown, a bilinear state observer is established based on the Kalman filtering principle. Then, the unavailable states are updated by the state observer outputs recursively. Once the state estimates are obtained, the bilinear state observer–based hierarchical stochastic gradient algorithm is developed by using the gradient search. For the purpose of improving the convergence rate and the parameter estimation accuracy, a bilinear state observer–based hierarchical multi‐innovation stochastic gradient algorithm is proposed by expanding a scalar innovation to an innovation vector. The convergence analysis indicates that the parameter estimates can converge to their true values. The numerical example illustrates the effectiveness of the proposed algorithms.

Section: Discussionmentioning

confidence: 99%

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

Xiao

Intl J Robust & Nonlinear

2019

121

“…There are still some interesting topics that are worth discussing. The proposed methods proposed in this paper can be used for modeling and prediction [61][62][63][64][65] and can be extended to study the parameter estimation problems of different systems with colored noises [66][67][68][69][70] and can be applied to other literatures [71][72][73][74][75] such as information and networked communication systems. [76][77][78][79][80][81][82]…”

Section: Discussionmentioning

confidence: 99%

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

Liu

Intl J Robust & Nonlinear

Hayat

2020

Summary The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.

“…The proposed methods proposed in this paper can be extended to study the parameter estimation problems of different systems with colored noises such as signal modeling and communication networked systems. 11,[55][56][57][58][59][60][61][62][63][64]…”

Section: Discussionmentioning

confidence: 99%

The innovation algorithms for multivariable state‐space models

Adaptive Control & Signal

Xiao

2019

116

This paper derives the input-output representation of the dynamical system described by a linear multivariable state-space model and the corresponding multivariate linear regressive model (ie, multivariate equation-error model). A projection identification algorithm, a multivariate stochastic gradient identification algorithm, and a multi-innovation stochastic gradient (MISG) identification algorithm are proposed for multivariate equation-error systems by using the negative gradient search and the multi-innovation identification theory. The convergence analysis of the MISG algorithm indicates that the parameter estimation errors converge to zero under the persistent excitation condition. Finally, a numerical example illustrates the effectiveness of the proposed algorithms.