Design of optical remote sensing satellite onboard processing system based on model definition

doi:10.11947/j.AGCS.2024.20220658

Abstract

Abstract:

In this paper, a design method of optical remote sensing satellite on-board processing system based on model definition and model-based systems engineering (MBSE) is proposed, and a model paradigm of on-board processing task behavior of “hardware resource-operator module-general processing core-typical application” is constructed. At the hardware level, the on-board heterogeneous embedded computing platform is adopted to integrate the design of multiple processors, and complete high-speed signal transmission and data processing through standardized high-speed data interconnection. In order to support changes in device scale and data processing complexity, good scalability through network topology should be achieved. At the software level, in view of the requirements of frequent data read and write operations, a large number of repeated computing operations, and general computing and acceleration of convolutional neural networks in on-board intelligent processing tasks, a configurable operator module composed of the instruction set of the on-board platform, the general operator of the on-board algorithm and the components of the on-board intelligent network is designed, and the hardware IP core of the specific algorithm can be quickly implemented based on the module. Simulation experiments show that this method can adapt the best software and hardware solutions according to the needs of satellite platforms and on-board processing tasks, and effectively improve the utilization rate of computing resources. The proposed real-time streaming compression coding scheme combined with cloud detection significantly improves the compression performance. The designed lightweight target detection and recognition method achieves a computing resource efficiency of 91.5%. Taking the raw data rates of GF-1 and GF-7 as examples, the overall computing resource utilization rate increased by 16.51% and 17.77% respectively compared with the conventional solution. The method has realized the modeling of the development process, the definable working mode, and the sharing of logical resources of on-board processing system, which is a reasonable optimization choice to adapt to the miniaturization and rapid deployment of satellites.

Key words: remote sensing satellite, on-board processing, model-defined, on demand adaptation, heterogeneous computing

CLC Number:

P237

Wei LIU, Songlin LIU, Zibo GUO, Kai LIU, Lizhe ZHANG. Design of optical remote sensing satellite onboard processing system based on model definition[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 689-699.

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处理器	GPU	FPGA	DSP	NPU
架构特点	基于单指令多数据流（SIMD）的架构	可基于特定算法设计专用硬件架构	适应并行的哈佛架构	可基于特定算法设计专用硬件架构
优缺点	生态好、算力高功耗高、散热难	高并行性、可重构开发效率低	稳定性高、接口全性能弱	能耗低、性能强架构固定
专用算力	高	动态算力	低	高
国产化程度	低	高	高	高
宇航级产品	无	有	有	待验证
本文选择的硬件组合（综合性能、功耗、国产化、宇航级成熟度）		√	√	√

个数	类型	卷积核数	尺寸	输出	运算量（OPS）	参数量
5	卷积	16/32/64/128/256	3×3/1	256×256	889 192 448	397 312
5	最大池化		2×2/2	128×128	889 192 448	397 312
1	卷积	512	3×3/1	8×8	150 994 944	1 179 648
1	最大池化		2×2/1	8×8	150 994 944	1 179 648
2	卷积	1024/512	3×3/1	8×8	805 306 368	6 291 456
2	卷积	256/45	1×1/1	8×8
1	卷积	128	1×1/1	8×8	4 202 496	32 768
1	上采样		×2	16×16
1	卷积	384	3×3/1	16×16	453 050 368	884 736
1	卷积	45	1×1/1	16×16	33 619 968	65 536

硬件平台	XC7VX690 T			XCVU9 P
硬件平台	单IP核/（Gb/s）	最大核数	总算力	单IP核/（Gb/s）	最大核数	总算力
云检测压缩	1.31	4	5.24 Gb/s（1.31 Gb/s×4）	1.31	6	7.86 Gb/s（1.31 Gb/s×6）
目标检测识别	0.22	6	1.32 Gb/s（0.22 Gb/s×6）	0.33	12	3.96 Gb/s（0.33 Gb/s×12）

处理需求	非模型定义方案			模型定义方案
处理需求	目标检测任务	图像压缩任务	算力利用率/（%）	目标检测任务	图像压缩任务	算力利用率/（%）
高分一号@1.28 Gb/s	1×690 T	1×690 T	39.02	5/6×VU9 P	1/6×VU9 P	55.53
高分七号@3.00 Gb/s	1×VU9 P	1×690 T	65.22	1/2×VU9 P＋1×690 T	1/2×VU9 P	82.99