RadarTD: A Radar Text Dataset for multi-parameter optimization

Abstract

This paper introduces the radar text dataset (RadarTD) for technical language modeling. This dataset is comprised of sentences containing radar parameters, values, and units determined from published radar literature. Additionally, each statement is assigned a sentiment, goal priority, and goal direction label. In this work, we show how RadarTD may be used to train simple Natural Language Processing (NLP) models to identify the attributes of each sentence listed in RadarTD. Once the NLP models have identified these attributes from text, we can use this information to develop Language Based Cost Functions (LBCF). Our study shows that the proposed text classification model achieves a classification accuracy between 96.7% and 97.8%, while the proposed named entity recognition model achieves an F1 score of 99.7. These findings suggest that the developed models are capable of achieving good performance for both text classification and named entity recognition for autonomous radar applications. We then illustrate an example of how these models could be used with Language Based Cost Functions to develop multi-parameter radar optimization schemes. We also provide a method of providing scalarization weights for each parameter, to improve the results of the optimization process.