Resource-efficient In-orbit Detection of Earth Objects
CoRR(2024)
摘要
With the rapid proliferation of large Low Earth Orbit (LEO) satellite
constellations, a huge amount of in-orbit data is generated and needs to be
transmitted to the ground for processing. However, traditional LEO satellite
constellations, which downlink raw data to the ground, are significantly
restricted in transmission capability. Orbital edge computing (OEC), which
exploits the computation capacities of LEO satellites and processes the raw
data in orbit, is envisioned as a promising solution to relieve the downlink
burden. Yet, with OEC, the bottleneck is shifted to the inelastic computation
capacities. The computational bottleneck arises from two primary challenges
that existing satellite systems have not adequately addressed: the inability to
process all captured images and the limited energy supply available for
satellite operations. In this work, we seek to fully exploit the scarce
satellite computation and communication resources to achieve satellite-ground
collaboration and present a satellite-ground collaborative system named
TargetFuse for onboard object detection. TargetFuse incorporates a combination
of techniques to minimize detection errors under energy and bandwidth
constraints. Extensive experiments show that TargetFuse can reduce detection
errors by 3.4 times on average, compared to onboard computing. TargetFuse
achieves a 9.6 times improvement in bandwidth efficiency compared to the
vanilla baseline under the limited bandwidth budget constraint.
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