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Fast and Accurate System for Onboard Target Recognition on Raw SAR Echo Data

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Publicado no:Remote Sensing vol. 17, no. 21 (2025), p. 3547-3560
Autor principal: Jacinto Gustavo
Outros Autores: Véstias Mário, Flores, Paulo, Duarte Rui Policarpo
Publicado em:
MDPI AG
Assuntos:
Accuracy
Deep learning
Datasets
Image resolution
Data acquisition
Algorithms
Electronic devices
Satellite imagery
Image processing
Airborne/spaceborne computers
Machine learning
Energy consumption
Energy requirements
Neural networks
Synthetic aperture radar
Target recognition
Classification
Network latency
Power management
Computing costs
Embedded systems
Optical data processing
Computers
Weather
Latency
Real time
Decision making
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Resumo:<sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>Direct target recognition from raw SAR echo data with an accuracy close to 100% with low power. <list-item> Recognition can be achieved with neural network models with low computational cost, yet fast enough to enable real-time processing. </list-item> What are the implications of the main findings? <list list-type="bullet"> <list-item> </list-item>Target recognition from SAR echo data can run onboard using embedded devices with less computing power. <list-item> Enables faster decision-making in time-critical missions, since data are processed near the sensor. </list-item> Synthetic Aperture Radar (SAR) onboard satellites provides high-resolution Earth imaging independent of weather conditions. SAR data are acquired by an aircraft or satellite and sent to a ground station to be processed. However, for novel applications requiring real-time analysis and decisions, onboard processing is necessary to escape the limited downlink bandwidth and latency. One such application is real-time target recognition, which has emerged as a decisive operation in areas such as defense and surveillance. In recent years, deep learning models have improved the accuracy of target recognition algorithms. However, these are based on optical image processing and are computation and memory expensive, which requires not only processing the SAR pulse data but also optimized models and architectures for efficient deployment in onboard computers. This paper presents a fast and accurate target recognition system directly on raw SAR data using a neural network model. This network receives and processes SAR echo data for fast processing, alleviating the computationally expensive DSP image generation algorithms such as Backprojection and RangeDoppler. Thus, this allows the use of simpler and faster models, while maintaining accuracy. The system was designed, optimized, and tested on low-cost embedded devices with low size, weight, and energy requirements (Khadas VIM3 and Raspberry Pi 5). Results demonstrate that the proposed solution achieves a target classification accuracy for the MSTAR dataset close to 100% in less than 1.5 ms and 5.5 W of power.
ISSN:2072-4292
DOI:10.3390/rs17213547
Fonte:Advanced Technologies & Aerospace Database