Conference Paper

An automated measurement system for cosite interference analysis

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Abstract

Cosite electromagnetic interference can be accurately simulated when measured data characterizing the broadband behavior of individual RF systems operating in the cosite environment is available. In this paper, a measurement system is presented that performs the broadband transmitter and receiver characterizations necessary for analyzing the cosite problem. An overview of the system is provided along with resulting measurements and observations from a validation exercise.

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... When available however, wideband measured emissions data such as shown inFigure 4 can be used for the simulations. Obtaining such data with enough dynamic range for reliable cosite characterization presents its own challenges and requires specialized measurement systems [5] (dBm) In practice, the prediction process often begins with limited data and so it is important that the analysis framework supports the initial simple parametric model and permits refining that simple model as more information becomes available. The ability to adjust harmonic levels and to add non-harmonic spurs to the model allows the fidelity of the predictions to be refined as the additional information becomes available. ...
... For EMI however, the situation becomes less clear as we need to characterize the Rx response to undesired signals that may have very different characteristics (e.g., modulation) than the intended signal. In this case, the sensitivity can be based on a power spectral density within the Rx IF channel bandwidth in order to detect the presence of the unwanted signal regardless of its modulation or other characteristics [5]. A potentially more useful approach for EMI is to specify the wideband susceptibility of the Rx. ...
... In this case the sensitivity can be a measure of the difference in spectral power within the Rx IF channel bandwidth due to interference. This metric reports the presence of the unwanted signal regardless of its modulation or other characteristics [5]. While the susceptibility model provides a better approach for cosite EMI predictions, models based on sensitivity are useful and are often all that is available. ...
Conference Paper
The accurate prediction of cosite electromagnetic interference (EMI) in complex radio frequency (RF) environments is often limited by the nature of the data available to describe the systems involved. Analysts attempting to predict cosite EMI are often faced with not only performing accurate simulations that yield useful results when using mixed data types, but also with managing a large amount of disparate data types in a way that allows straightforward predictions to be made and, just as importantly, allowing the accuracy of those predictions to be refined as more data becomes available to improve the description of the systems in the cosite scenario. In this paper, we address efficient approaches for both aspects of the cosite prediction challenge: simulation and data management.
... These models allow users to enter their data, which they have, and run the program to calculate the EMI threats and margin based on the system sensitivity. The cosite interference analyses were extended in Ref. [6] in which an automated measurement system was proposed. It analyzed the EMI in the existence of a transmitter and a receiver. ...
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The accurate prediction of cosite electromagnetic interference (EMI) in complex radio frequency (RF) environments is often limited by the nature of the data available to describe the systems involved. Analysts attempting to predict cosite EMI are often faced with not only performing accurate simulations that yield useful results when using mixed data types, but also with managing a large amount of disparate data types in a way that allows straightforward predictions to be made and, just as importantly, allowing the accuracy of those predictions to be refined as more data becomes available to improve the description of the systems in the cosite scenario. In this paper, we address efficient approaches for both aspects of the cosite prediction challenge: simulation and data management.
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