Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations

Jamoos, Ali; Grivel, Éric; Shakarneh, N.; Abdel-Nour, Hanna

doi:10.1049/iet-spr.2010.0066

Cited by 18 publications

(17 citation statements)

References 26 publications

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“…ζ k is a linear combination of the states to be estimated. Comparisons between H ∞ filter and Kalman filter (KF) reveals that ω k and n k in H ∞ filter design can be considered as the process and measurement noises with unknown covariance matrices of R and Q, respectively [33,[35][36][37]. In [35][36][37] R and Q are supposed to be adjusted for better performance.…”

Section: H ∞ Filtermentioning

confidence: 99%

Design of adaptiveH_∞filter for implementing on state‐of‐charge estimation based on battery state‐of‐charge‐varying modelling

Charkhgard

Zarif

2015

IET Power Electronics

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This study suggests a new method for modelling lithium-ion battery types and state-of-charge (SOC) estimation using adaptive H ∞ filter (AHF). First, a universal linear model with some free parameters is considered for dynamical behaviour of the battery. The battery voltage and SOC are used as states of the model. Then for every period in the charge/discharge process the free parameters of the model are identified. Each period of process is associated with a specific SOC value, hence the parameters can be regarded as functions of SOC in the entire process. The functions are determined based on polynomial approximation and least squares method. The proposed SOC-varying model is incorporated in AHF for SOC estimation. Moreover, a new method for adjusting the tuning parameters of the filter is suggested. The proposed method is verified by experimental tests on a lithium-ion battery and is compared with adaptive extended Kalman filter and square-root unscented Kalman filter

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Section: H ∞ Filtermentioning

confidence: 99%

Design of adaptiveH_∞filter for implementing on state‐of‐charge estimation based on battery state‐of‐charge‐varying modelling

Charkhgard

Zarif

2015

IET Power Electronics

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“…Another approach to this problem makes use of coupled Kalman filters [ 104 ] or cross-coupled

filters [ 105 , 106 ] has been developed. In essence these move beyond the Kalman filter approach referenced above ([ 21 ], Section 3.3) and operate as follows.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Moon

Gunther

2020

Entropy

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Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation.

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“…Nevertheless, the AR parameters are unknown and should be estimated. To jointly estimate the autoregressive process and its parameters from noisy observations, one of the authors of this paper has recently proposed dual Kalman filters based structure [ 11 , 20 ]. This structure consists of two cross-coupled Kalman filters where the first filter uses the latest estimated AR parameters to estimate the fading process, while the second filter uses the estimated fading process to update the AR parameters.…”

Section: Introductionmentioning

confidence: 99%

Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters

Aldababseh

Jamoos

2014

The Scientific World Journal

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We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method.

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Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations

Cited by 18 publications

References 26 publications

Design of adaptiveH_∞filter for implementing on state‐of‐charge estimation based on battery state‐of‐charge‐varying modelling

Design of adaptiveH_∞filter for implementing on state‐of‐charge estimation based on battery state‐of‐charge‐varying modelling

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters

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Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations

Cited by 18 publications

References 26 publications

Design of adaptiveH∞filter for implementing on state‐of‐charge estimation based on battery state‐of‐charge‐varying modelling

Design of adaptiveH∞filter for implementing on state‐of‐charge estimation based on battery state‐of‐charge‐varying modelling

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters

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Design of adaptiveH_∞filter for implementing on state‐of‐charge estimation based on battery state‐of‐charge‐varying modelling

Design of adaptiveH_∞filter for implementing on state‐of‐charge estimation based on battery state‐of‐charge‐varying modelling