Determining what radar parameters to use for a given scenario is a non-trivial task. When working in a new radar domain, it is quite common to turn to published literature to understand how to approach a new problem. When reviewing research, there can be such a wide range of values used in a radar design that it can become difficult to determine what values to use when designing a new system. An ideal scenario would be to turn to a single source that provides base listings for different radar parameters, but at the time of writing no source is known. In this work, we aim to statistically analyze published radar literature to determine a base set of radar parameter values for a given domain. These parameters include things such as the carrier frequency, bandwidth, pulse repetition frequency, and target range, among many others. To do this, a base set of parameters that are included in nearly all radar systems design will need to be established. Then, by selecting published research in specific domains (ground penetrating radar, atmospheric sensing, imaging, etc . . . ), we can determine the most common values for these parameters. In this paper, we examine the most common values for a given domain, as well as analyze the relationships between these parameters. This information could then be used to develop simulations, optimization problems, or provide insight when developing a new radar system.
This paper introduces the radar text data set (RadarTD) for technical language modeling. This data set is comprised of sentences containing radar parameters, values, and units determined from real-world values. This data set is created based on values determined from published academic research. Additionally, each statement is assigned a sentiment label and goal priority label. Preliminary investigations into the applicability of this data set are explored using the BERT model and several bi-directional LSTM models. These models are evaluated on text classification and named entity recognition tasks. This study evaluates the applicability of technical language modeling using neural networks to analyze input statements for cognitive radar applications. These findings suggest that this data set can be used to achieve reasonable performance for both text classification and named entity recognition for autonomous radar applications.
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