Adaptive Robust Efficient Methods for Periodic Signal Processing Observed with Colours Noises

Pchelintsev, Evgeny; Pergamenshchikov, Serguei; Marcokova, Mariana

doi:10.15598/aeee.v17i3.3132

Cited by 3 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We call such estimation problem as a problem of estimating the number of modulated sinusoids with unknown amplitudes, phases, and partially unknown frequencies. Now, using ( 2), (25) we can obtain the algorithms (4), ( 5), ( 7), (8) of estimating the number of modulated sinusoids within the ML and QL design approaches.…”

Section: Maximum Likelihood and Quasilikelihood Approachesmentioning

confidence: 99%

“…VII. CONSISTENCY Now let us find the ranges of the values of the tuning parameters κ IR and κ I which are provide the consistency of MOS algorithms (7) and (8), respectively. In this section we consider the case of known frequencies only, i.e., we assume that in (31) ω i = ω 0i for all i.…”

Section: Properties Of Likelihood Functionsmentioning

confidence: 99%

“…for algorithm (8), respectively. In (41) and (42) the number 1 ≤ k ≤ N − ν 0 for the left hand inequalities, and 1 ≤ k ≤ ν 0 − 1 for the right hand inequalities, respectively.…”

Section: Properties Of Likelihood Functionsmentioning

confidence: 99%

“…Let us calculate abridged error probability (11) for the algorithms ( 5), ( 7), (8). For brevity, we shall assume that…”

Section: Theoretical Calculation Of the Abridged Error Probabilitymentioning

confidence: 99%

“…The reported study was funded by RFBR and CNRS, project number 20-51-15001 Minimum Description Length (MDL) criterion [3], the Exponentially embedded families (EEF) criterion [4], the Penalizing adaptively likelihood (PAL) criterion [5], etc. Good surveys of the MOS criteria may be found, for instance, in [6] and [7] (also, there are some lesser known approaches, see, for example, nonparametric MOS procedure in [8]). The MOS criteria define the structure of the corresponding algorithms, more specifically these criteria define the penalty terms of the corresponding algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Model Selection for Signal Processing: a Minimum Error Approach and a General Performance Analysis

Kharin¹

2020

Preprint

View full text Add to dashboard Cite

Estimation of the number of signals in the presence of noise is an important problem in several areas of statistical signal processing. There are a lot of modern works on the design of an optimal solution to this problem in terms of some criteria. Each criterion generates a model order selection (MOS) algorithm.However, the minimum error probability criterion has not received significant attention, despite the fact that errors in the estimation of the number of signals might directly affect the performance of the signal processing system as a whole. In this paper, we propose a new approach to the design of MOS algorithms partially based on the minimum error probability criterion.Also, we pay a lot of attention to the performance and consistency analysis of the MOS algorithms. In this study, an abridged error probability is used as a universal performance measure of the MOS algorithms. We propose a theoretical framework that allows to obtain closed-form expressions for the abridged error probabilities of a wide range of MOS algorithms. Moreover, a parametric consistency analysis of the presented MOS algorithms is provided.Next, we use the obtained results to provide a parametric optimization of the presented MOS algorithms.Finally, we study a quasilikelihood (QL) approach to the design and analysis of the MOS algorithms. The proposed theoretical framework is used to obtain the abridged error probabilities as functions of the unknown signal parameter. These functions, in its turn, allow us to find the scope of the QL approach.

show abstract

Section: Maximum Likelihood and Quasilikelihood Approachesmentioning

confidence: 99%

Section: Properties Of Likelihood Functionsmentioning

confidence: 99%

Section: Properties Of Likelihood Functionsmentioning

confidence: 99%

“…Let us calculate abridged error probability (11) for the algorithms ( 5), ( 7), (8). For brevity, we shall assume that…”

Section: Theoretical Calculation Of the Abridged Error Probabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model Selection for Signal Processing: a Minimum Error Approach and a General Performance Analysis

Kharin¹

2020

Preprint

View full text Add to dashboard Cite

show abstract

Improved signal processing observed with semi-Markov noises

Pchelintsev

Perelevskiy

Povzun

2020

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper considers the problem of improved adaptive estimating a periodic signal observed in the transmission channel with the dependent noise defined by semi-Markov processes with unknown probabilities and spectral characteristics. Improved adaptive model selection procedure is proposed. The comparison between improved and least squares methods is studied. Sharp oracle inequality for the procedure risk is obtained. The efficiency of the proposed procedure is established. The Monte Carlo simulation results are given.

show abstract

Estimating the number of signals with unknown parameters under Gaussian noises

Kharin

Pergamenchtchikov

2022

2022 International Telecommunications Conference (ITC-Egypt)

View full text Add to dashboard Cite

Adaptive Robust Efficient Methods for Periodic Signal Processing Observed with Colours Noises

Cited by 3 publications

References 0 publications

Model Selection for Signal Processing: a Minimum Error Approach and a General Performance Analysis

Model Selection for Signal Processing: a Minimum Error Approach and a General Performance Analysis

Improved signal processing observed with semi-Markov noises

Estimating the number of signals with unknown parameters under Gaussian noises

Contact Info

Product

Resources

About