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Signal parameter estimation using fourth order statistics: multiplicative and additive noise environment

Overview of attention for article published in SpringerPlus, June 2015
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Title
Signal parameter estimation using fourth order statistics: multiplicative and additive noise environment
Published in
SpringerPlus, June 2015
DOI 10.1186/s40064-015-1085-5
Pubmed ID
Authors

Chandrakant J Gaikwad, Hemant K Samdani, Pradip Sircar

Abstract

Parameter estimation of various multi-component stationary and non-stationary signals in multiplicative and additive noise is considered in this paper. It is demonstrated that the parameters of complex sinusoidal signal, complex frequency modulated (FM) sinusoidal signal and complex linear chirp signal in presence of additive and multiplicative noise can be estimated using a new definition of the fourth order cumulant (FOC), and the computed accumulated FOC (AFOC). Analytical expressions for the FOC/AFOC of the above signals are derived. The concept of accumulated cumulant is introduced to handle the case of a non-stationary signal, for which the fourth order cumulant may be a function of both time and lag. Simulation study is carried out for all the three signals. In case of complex sinusoidal signals, the resul ts of parameter estimation show that the proposed method based on the new definition of fourth order cumulant performs better than an existing method based on fourth order statistics. The proposed method can be employed for parameter estimation of non-stationary signals also as mentioned above. For comparison purpose, the Cramer-Rao (CR) bound expressions are derived for all the signals considered for parameter estimation. The simulation results for non-stationary signals are compared with the CR bounds.

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Mendeley readers

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The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 40%
Student > Ph. D. Student 2 40%
Student > Doctoral Student 1 20%
Readers by discipline Count As %
Engineering 4 80%
Unknown 1 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 July 2015.
All research outputs
#15,340,005
of 22,817,213 outputs
Outputs from SpringerPlus
#932
of 1,851 outputs
Outputs of similar age
#154,212
of 264,035 outputs
Outputs of similar age from SpringerPlus
#39
of 85 outputs
Altmetric has tracked 22,817,213 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,851 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,035 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 85 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.