Accurate estimation of common sinusoidal parameters in multiple channels

Chan, Frankie K. W.; So, Hing Cheung; Sun, Weize

doi:10.1016/j.sigpro.2012.09.016

Cited by 10 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, , , are assumed not all to be zero, and is the additive noise source in the -th channel, , which is assumed to be uncorrelated white complex Gaussian process with mean zero and variance . According to [2]- [5], [27], the sinusoidal poles , from different channels indexed , may be shared by or less channels, which means that from the channels are identical. Such sinusoidal poles correspond to the common mode information.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Let be the covariance function of . Then asymptotically in , (27) where is given by (28) Proof: see [31]. To make use of the above theorem, it is essential to analyze the statistical property of the residual for the WLP estimates of the order from the -th channel data:…”

Section: Sinusoidal Model Selection For Multiple Channelsmentioning

confidence: 99%

“…In Section III-B, we develop the MC-WLP estimator to conduct optimum parameter estimation. In many cases, the sinusoidal parameters of the fully-common sinusoidal poles, which are shared by all the channels, are of much interest [4], [27]. Therefore, for the sake of simplicity, here we adopt the average MSEs of the parameter estimates of the fully-common poles for performance evaluation.…”

Section: B Frequency and Damping Factor Estimationmentioning

confidence: 99%

“…Therefore, for the sake of simplicity, here we adopt the average MSEs of the parameter estimates of the fully-common poles for performance evaluation. Apart from the CRLB [27], comparison is made with the subspace-based method [2], [4] and the average of the single-channel WLP (SC-WLP) [26] method for all channels. To observe the impact of the detection of distinct multi-channel sinusoidal poles to estimation, here we use the model order estimate in the MC-MOE, , to conduct MC-WLP parameter estimation.…”

Section: B Frequency and Damping Factor Estimationmentioning

confidence: 99%

See 3 more Smart Citations

Parametric Modeling for Damped Sinusoids From Multiple Channels

Zhou

Christensen

2013

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

The problem of parametric modeling for noisy damped sinusoidal signals from multiple channels is addressed. Utilizing the shift invariance property of the signal subspace, the number of distinct sinusoidal poles in the multiple channels is first determined. With the estimated number, the distinct frequencies and damping factors are then computed with the multi-channel weighted linear prediction method. The estimated sinusoidal poles are then matched to each channel according to the extreme value theory of distribution of random fields. Simulations are performed to show the performance advantages of the proposed multi-channel sinusoidal modeling methodology compared with existing methods.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Section: Sinusoidal Model Selection For Multiple Channelsmentioning

confidence: 99%

Section: B Frequency and Damping Factor Estimationmentioning

confidence: 99%

Section: B Frequency and Damping Factor Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Parametric Modeling for Damped Sinusoids From Multiple Channels

Zhou

Christensen

2013

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…As a related work, in reference [25], the authors exploited linear prediction (LP) and weighted least squares technique to estimate the common sinusoidal parameters in multiple channels, including the frequency. They used several data sets in multiple channels for the estimation, which is different from the conventional estimation using only one data set.…”

Section: Introductionmentioning

confidence: 99%

Distributed Frequency Estimation Over Sensor Network

Wang

2015

IEEE Sensors J.

View full text Add to dashboard Cite

Frequency estimation is a common problem in a variety of applications. In recent years, adaptive notch filtering methods have been widely adopted for solving frequency estimation problems. For frequency estimation performed by a single node, the sampling time may not be long enough, the sampling rate may not be high enough, and the noise effect may be serious. In such situations, most existing algorithms for frequency estimation can not produce accurate enough results. Here, we propose using wireless sensor networks for sampling data distributedly, and using distributed notch filtering method for dealing with this problem. Specifically, we propose two distributed algorithms over sensor network, a LMS based distributed notch filtering (dNF-LMS) algorithm and a TLS based distributed notch filtering (dNF-TLS) algorithm. The communication cost of the new proposed algorithms is low, as each node exchanges only with its neighbors the estimates other than the original data. The proposed algorithms are applied to both synthetic and real examples. Simulation results demonstrate the effectiveness of the new proposed algorithms.

show abstract

Estimating Parameters in Multichannel Sinusoidal Model

Nandi

Kundu

2022

Circuits Syst Signal Process

View full text Add to dashboard Cite

Accurate estimation of common sinusoidal parameters in multiple channels

Cited by 10 publications

References 16 publications

Parametric Modeling for Damped Sinusoids From Multiple Channels

Parametric Modeling for Damped Sinusoids From Multiple Channels

Distributed Frequency Estimation Over Sensor Network

Estimating Parameters in Multichannel Sinusoidal Model

Contact Info

Product

Resources

About