MIMO SAR Using Orthogonal Coding: Design, Performance Analysis, and Verifications

Lin, Yun; Fan, Yida; Jiang, Chenglong; Wang, Zhiqiang; Shao, Weizeng

doi:10.1155/2015/629506

Cited by 3 publications

(1 citation statement)

References 20 publications

(21 reference statements)

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“…The second point is to transmit the data after on-orbit interpretation through the space-based IoT, which can realize data enhancement of remote sensing information in many aspects. Take MIMO-SAR [7][8][9] as an example: The SAR performance is limited by target scintillation; by exploiting the diversity of target scattering, MIMO technology applied to SARs can significantly improve resolution and sensitivity, and detection and estimation performance for IoT space applications [10]. The third is that due to the satellite being powered by solar energy, the energy consumed by the interpretation of a large number of remote sensing images does not impose an additional burden on the ground stations, which is more environmentally friendly.…”

Section: Introductionmentioning

confidence: 99%

Using Clean Energy Satellites to Interpret Imagery: A Satellite IoT Oriented Lightweight Object Detection Framework for SAR Ship Detection

Xie

Luo

et al. 2022

Sustainability

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This paper studies the lightweight deep learning object detection algorithm to detect ship targets in SAR images that can be deployed on-orbit and accessed in the space-based IoT. Traditionally, remote sensing data must be transferred to the ground for processing. With the vigorous development of the commercial aerospace industry, computing, and high-speed laser inter-satellite link technologies, the interconnection of everything in the intelligent world has become an irreversible trend. Satellite remote sensing has entered the era of a big data link with IoT. On-orbit interpretation gives remote sensing images expanse application space. However, implementing on-orbit high-performance computing (HPC) is difficult; it is limited by the power and computer resource consumption of the satellite platform. Facing this challenge, building a processing algorithm with less computational complexity, less parameter quantity, high precision, and low computational power consumption is a key issue. In this paper, we propose a lightweight end-to-end SAR ship detector fused with the vision transformer encoder: YOLO−ViTSS. The experiment shows that YOLO−ViTSS has lightweight features, the model size is only 1.31 MB; it has anti-noise capability is suitable for processing SAR remote sensing images with native noise, and it also has high performance and low training energy consumption with 96.6 mAP on the SSDD dataset. These characteristics make YOLO−ViTSS suitable for porting to satellites for on-orbit processing and online learning. Furthermore, the ideas proposed in this paper help to build a cleaner and a more efficient new paradigm for remote sensing image interpretation. Migrating HPC tasks performed on the ground to on-orbit satellites and using solar energy to complete computing tasks is a more environmentally friendly option. This environmental advantage will gradually increase with the current construction of large-scale satellite constellations. The scheme proposed in this paper helps to build a novel real-time, eco-friendly, and sustainable SAR image interpretation mode.

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Section: Introductionmentioning

confidence: 99%