Evaluation of CNN in ESM Data Classification by Perspective of Military Utility
Sammanfattning: Modern society has seen an increase in automation using AI in a variety of applications. To keep up with recent development, it is therefore logical to investigate the application of AI programs to military tasks. The great advantage with automation lies in the possible increase in efficiency and possible relocation of resources of personnel to other tasks. Therefore, this study aims to evaluate the use of Convolutional Neural Networks (CNN) in classification of communication and radar emitters based on collected Electronic Support Measures (ESM) data and to estimate to what extent human analysts could be replaced. The evaluation was performed by applying the concept of military Utility as a framework for evaluation with the addition of Technology Readiness Level (TRL) to survey how far the technology has developed. Data was collected using two methods: Firstly, through a literature review of research done on the application of CNNs in classifying information such as spectrograms and images. Secondly, by interviewing a subject matter expert from SAAB, who mainly helped estimate the TRL of the technology’s components. The study found that CNN appears suitable to apply on the proposed task and that the program could potentially replace human analysts to a great extent, at least when doing routine classifications. Full automation seems unlikely as analysts would be required with more challenging classifications, especially those outside the range of the training data used in teaching the CNN. Finally, challenges involved with deep learning programs inherent structure, demands and application to military tasks are discussed and subjects for future research are proposed.
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