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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Related Experiment Video

Updated: Aug 27, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Kernel Function-Based Ambiguity Function and Its Application on DOA Estimation in Impulsive Noise.

Yuzi Dou1, Sen Li1

  • 1College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.

Sensors (Basel, Switzerland)
|September 23, 2022
PubMed
Summary

New robust ambiguity functions, correntropy-based ambiguity function (CRAF) and fractional lower order correntropy-based ambiguity function (FLOCRAF), improve direction of arrival (DOA) estimation for linear frequency modulated (LFM) signals in impulsive noise.

Keywords:
DOA estimationLFM signalambiguity functiontime-frequency analysisα-stable distribution

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Statistical Signal Processing

Background:

  • Traditional ambiguity functions struggle with impulsive noise, hindering accurate time-frequency analysis of linear frequency modulated (LFM) signals.
  • Impulsive noise environments degrade the performance of direction of arrival (DOA) estimation algorithms.

Purpose of the Study:

  • To develop robust ambiguity functions for LFM signals in impulsive noise.
  • To propose novel DOA estimation algorithms leveraging these robust ambiguity functions.

Main Methods:

  • Defined correntropy-based ambiguity function (CRAF) and fractional lower order correntropy-based ambiguity function (FLOCRAF) utilizing the noise suppression properties of correntropy.
  • Developed CRAF-ESPRIT and FLOCRAF-ESPRIT algorithms by replacing covariance matrices with spatial CRAF and FLOCRAF matrices.

Main Results:

  • The proposed CRAF and FLOCRAF based algorithms demonstrate superior performance in resolution probability and estimation accuracy compared to traditional methods.
  • FLOCRAF-ESPRIT shows enhanced performance over CRAF-ESPRIT in low signal-to-noise ratio and high impulsive noise conditions.

Conclusions:

  • Correntropy-based ambiguity functions offer effective solutions for LFM signal analysis and DOA estimation in impulsive noise.
  • FLOCRAF-ESPRIT provides a robust and accurate DOA estimation method for challenging noisy environments.