Ground-based SAR system for object classification with parameter optimization based on deep learning feedback algorithm

Filip Turčinović; Marin Kačan; Dario Bojanjac; Marko Bosiljevac

doi:10.1117/12.2679852

19 October 2023 Ground-based SAR system for object classification with parameter optimization based on deep learning feedback algorithm

Filip Turčinović, Marin Kačan, Dario Bojanjac, Marko Bosiljevac

Author Affiliations +

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 127330R (2023) https://doi.org/10.1117/12.2679852
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

The convergence of microwave technology and machine learning has fostered the development of smart radar systems. This paper introduces an energy-efficient radar system for object classification, employing a microcomputer mediated interaction between radar and deep learning model. Specifically, our approach focuses on Ground-Based Synthetic Aperture Radar (GBSAR) and utilizes a Raspberry Pi microcomputer to dynamically adjust the number of positions from which GBSAR sensor obtains measurements. The system operates in two phases: initially recording the scene from reduced number of positions, followed by capturing segments of the scene that contain objects classified below a preset certainty from additional positions. Experimental findings highlight consistent improvements in classification accuracy across all test scenarios. This methodology enhances both energy efficiency and classification outcomes, effectively balancing resource consumption and accuracy.

Conference Presentation

Citation Download Citation

Filip Turčinović, Marin Kačan, Dario Bojanjac, and Marko Bosiljevac "Ground-based SAR system for object classification with parameter optimization based on deep learning feedback algorithm", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 127330R (19 October 2023); https://doi.org/10.1117/12.2679852

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