Quasilikelihood Estimate of the Number of Radio Signals with Unknown Amplitudes and Phases

Trifonov, A. P.; Kharin, Aleksandr

doi:10.1007/s11141-017-9787-x

Cited by 6 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The representations (10) are useful for the further calculations of the coefficients K * * i,j (5) and K * i,j (9). In this paper, we use an error probability, i.e., Pr(ν = ν 0 ) as a measure of performance of the algorithm for estimating the number of signals and an abridged error probability [10]- [14] as a universal approximation to the error probability. Let us write down a definition of the abridged error probability that can be found in [10]- [14]…”

Section: Theoretical Performance Analysismentioning

confidence: 99%

“…The resulting algorithm is called the quasi-likelihood (QL) one [14] for estimating the number of signals. In other words, the structure of the QL algorithm coincides with the structure of the ML algorithm for estimating the number of signals with the known parameters, except the values of the signal parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In other terms, there is provided the performance analysis of the maximum-likelihood algorithm for estimating the number of signals in case when the signal parameters deviate from their true values. Note that, in our previous research [14] we analyzed a special case when only the amplitudes and phases of modulated sinusoids may deviate from their true values. In this paper a general case is examined: we suppose that all the parameters of the modulated sinusoids may deviate from their true values and we also provide the simulations to confirm our theoretical results.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determining the Number of Sinusoids Measured with Errors

Kharin,

Chernoyarov

2020

Preprint

View full text Add to dashboard Cite

This paper describes how a priori information about the signal parameters can influence the accuracy of estimating the number of these signals. This study considers sinusoidal signals and it is supposed that the parameters (amplitudes, frequencies and phases) of the received signals are known up to a certain error. The error probability of the maximum likelihood estimation of the number of sinusoids is calculated under this condition.

show abstract

Section: Theoretical Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determining the Number of Sinusoids Measured with Errors

Kharin,

Chernoyarov

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This simple estimate of frequencies we call as a blind (BL) estimate. Moreover, if one uses BL estimates instead of the ML ones in some MOS algorithms we call such algorithms as QL algorithms (see, for example, [15], [16]).…”

Section: Maximum Likelihood and Quasilikelihood Approachesmentioning

confidence: 99%

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