Estimation and performance — Kalman based approach: A review

Rajender, G.; Anilkumar, T.; Rao, K. Srinivasa

doi:10.1109/iccsp.2016.7754332

Cited by 2 publications

(3 citation statements)

References 68 publications

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“…1, where the MSE performance for the AR(p)-CM+ is presented. Thus, 4 0 1 2 5 10 15 18 Order of autoregressif model p the choice of the order together with the tuning of the model parameters is critical and a trade-off must be found between numerical complexity and performance. The case p = 1 was fully resolved in [28,29], but it leaves room for possible improvement regarding the BCRB.…”

Section: Existing Techniques Limitations and Open Questionsmentioning

confidence: 99%

“…The 2 LMS is a computationally less demanding technique than KF, but at the cost of a slower convergence. The design of KF requires selecting a linear recursive state-space model for the parameter to be tracked [4][5][6][7][8]. As the true CG does not follow such a model, the linear recursive state-space model is considered an approximation.…”

Section: Introductionmentioning

confidence: 99%

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Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Husseini¹,

Simon²,

Ros³

2019

Digital Signal Processing

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Second-order autoregressive modelbased Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation. Digital Signal Processing, Elsevier, 2019, 90, pp. AbstractThis paper treats the estimation of a flat fading Rayleigh channel with Jakes' Doppler spectrum model and slow fading variations. A common method is to use a Kalman filter (KF) based on an auto-regressive model of order p (AR(p)). The parameters of the AR model can be simply tuned by using the correlation matching (CM) criterion. However, the major drawback of this method is that high orders are required to approach the Bayesian Cramer-Rao lower bound. The choice of p together with the tuning of the model parameters is thus critical, and a tradeoff must be found between the numerical complexity and the performance. The reasonable tradeoff arising from setting p = 2 has received much attention in the literature. However, the methods proposed for tuning the model parameters are either based on an extensive grid-search analysis or experimental results, which limits their ap-$ plicability. A general solution for any scenario is simply missing for p = 2 and this paper aims at filling this gap. We propose using a Minimization of Asymptotic Variance (MAV) criterion, for which a general closed-form formula has been derived for the optimal tuning of the model and the mean square error. This provides deeper insight into the behaviour of the KF with respect to the channel state (Doppler frequency and signal to noise ratio).Moreover, the paper interprets the proposed solution, especially the dependence of the shape of the optimal AR(2) spectrum on the channel state.Analytic and numerical comparisons with first-and second-order algorithms in the literature are also performed. Simulation results show that the proposed AR(2)-MAV model performs better than the literature and similarly to AR(p)-CM models with p ≥ 15.

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Section: Existing Techniques Limitations and Open Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Husseini¹,

Simon²,

Ros³

2019

Digital Signal Processing

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show abstract

“…"It is utilized for continuous estimation in sensible estimation dependent on input utilizing squares of information bits. Legitimate gauge was created dependent on a unique square, considering the instance of including the channel rapidly and the condition of divert decent variety in the framework is sketched out in [2].…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation using Kalman Filter for MIMO-OFDM Communication System

2019

IJRTE

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With the advancement of remote correspondence, the confinement of sign estimation under channel variation condition and their belongings were expanding. Different systems were proposed in past for the improvement of sign estimation effectiveness dependent on reference data utilizing versatile, visually impaired or semi visually impaired methodologies. Where visually impaired and semi visually impaired are seen to beat the versatile based methodologies, further upgrades are still on research to improve the productivity with least time union. To accomplish this goal, estimation calculations in time, recurrence and time-recurrence area were created. These methodologies attempt to accomplish the productivity targets by either expanding the estimation recursion or restricting the mistake likelihood. This paper exhibits a methodology for accomplishing improved estimation proficiency with least time assembly and lesser mistake likelihood, in MIMO correspondence framework utilizing the kalman filtration approach. A ghastly estimation rationale dependent on vitality of the sign range is made.

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Estimation and performance — Kalman based approach: A review

Cited by 2 publications

References 68 publications

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Channel Estimation using Kalman Filter for MIMO-OFDM Communication System

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