Modeling of time-varying Instanteous Frequency in a finitely correlated environment

Pally, Roshin Kadanna; Beex, A.A.

doi:10.1109/icdsp.2009.5201085

Cited by 3 publications

(1 citation statement)

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“…Adaptive algorithms, such as the least mean square (LMS) and the recursive least square (RLS), use a dynamic model for adapting the TV AR parameters and are capable of tracking time-varying frequency, provided that the variation is slow [5,6]. The basis function method employs an explicit model for the TV AR parameter variation, and model parameters are estimated via a block calculation where parameters are expanded as a summation of the weighted time-functions [7], are capable of tracking both the fast or the slow time varying frequencies. However, the selection of the expansion dimension and the basis function is questionable since there is no fundamental theorem on how to choose them [9].…”

Section: Introductionmentioning

confidence: 99%

Non stationary signal prediction using TVAR model

Reddy¹,

Rao

2014

2014 International Conference on Communication and Signal Processing

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In this paper Time-varying Auto Regressive (TV AR) model based approach for non stationary signal prediction in noisy environment is presented. Covariance method is applied for least square estimation of time-varying autoregressive parameters. In TV AR modeling approach, the time-varying parameters are expressed as a linear combination of constants multiplied by basis functions. In this paper, the TV AR parameters are expanded by a low-order discrete cosine basis. The order determination of TV AR model is addressed by means of the maximum likelihood estimation (MLE) algorithm. The experimental results are presented for prediction of Discrete AM, Discrete FM, Discrete AM-FM signals keywords: Basis function, Discrete Amplitude Modulation, Discrete Frequency Modulation, Discrete Amplitude and Frequency Modulation , Time-Varying Autoregressive model,

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Section: Introductionmentioning

confidence: 99%