@proceedings {1039,
	title = {Uncovering the Complexity of WSPR Daemon Noise Through Kernel Density and Gaussian Mixture Models},
	year = {2026},
	month = {03/2026},
	publisher = {HamSCI},
	address = {Newington, CT},
	abstract = {This study describes and analyzes the multimodal nature of the noise measurements in Weak Signal Propagation Reporter (WSPR) daemon data. We statistically modeled the noise data from several stations using a combination of non-parametric and parametric methods to explore the underlying probability structures. Non-parametric kernel density estimation (KDE) was used to define a smooth, continuous estimate of the empirical probability density function, revealing that the observed noise values do not conform to a simple Gaussian distribution and instead exhibit strong multimodal characteristics. To explore these features further, Gaussian Mixture Models (GMM) were applied to estimate the overall distribution as a weighted sum of multiple Gaussian components. The number of components presented was chosen using both visual analyses, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) considering both goodness of fit and model parsimony. The fitted density curves and component contributions varied widely between stations. Most showed that the WSPR daemon noise profile can be decomposed into several overlapping sub distributions, suggesting multiple contributing noise sources related to propagation conditions, receiver and antenna configuration, and possible anthropogenic interference. },
	author = {Robert Gerzoff}
}
@proceedings {939,
	title = {Distributional Characteristics of Short-Term 2022 20-Meter Sequential Matched WSPRDaemon Noise Observations},
	year = {2025},
	month = {03/2025},
	publisher = {HamSCI},
	address = {Newark, NJ},
	abstract = {<p>Understanding the characteristics and structure of one{\textquoteright}s data is critical for effective data analysis. Currently, WSPRDaemon data are being used in background noise level, HF propagation, Doppler shift, and correlational studies. Here we use Exploratory Data Analysis (EDA) on short-term (2-minute), sequential observations of WSPRDaemon observations to gain insights into the distributional characteristics of the data prior to more advanced modeling or analytical procedures. Our findings uncovered data outliers and puzzling distributional characteristics. \&nbsp;While consistent in their presence, these anomalies were unique from one receiving station to the next. These findings warrant further consideration and explanation before researchers invest time attempting more complex analytic procedures. \&nbsp;They also strongly suggest that a parallel data base of meta-data for the receiving stations would be extremely helpful for future studies.\&nbsp;</p>
},
	author = {Robert Gerzoff and Gwyn Griffiths}
}
