An MBPLE-enabled architecture design method for remote sensing satellites

doi:10.23919/JSEE.2026.000127

Abstract

Abstract:

Remote sensing satellites (RSS) are highly complex and customized from a common product family. It makes the traditional model-based system engineering (MBSE) method, which lacks architecture-level reusability, difficult to apply to their architecture design. Considering RSS has a relatively fixed common architecture from which the various design solutions are customized specific to different missions, the model-based product line engineering (MBPLE) methodology can be leveraged. In this paper, based on the analysis of the current RSS development process in practice and the main issues of current MBPLE methods, an RSS-specific MBPLE approach is proposed. Firstly, the RSS domain terminology is consolidated into the RSS-MBPLE SysML profile to support the construction of models in the design process. Then, the two key steps, i.e., architecture configuration and standalone product selection are efficiently conducted following the MBPLE principles supported by plugins of the mainstream SysML platform. Finally, a typical RSS control subsystem is illustrated as the case study to demonstrate the effectiveness of the proposed method. The results show that the proposed approach improves architecture-level reusability and design automation compared with traditional methodology, thereby reducing manual clone-and-modify efforts and enhancing the efficiency of RSS architecture design.

Key words: remote sensing satellites, model-based product line engineering (MBPLE), domain modeling, architecture design

Yaodong WANG, Yue CAO, Houman YU, Yusheng LIU. An MBPLE-enabled architecture design method for remote sensing satellites[J]. Journal of Systems Engineering and Electronics, 2026, 37(3): 933-951.

Figures/Tables 19

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Fig 7

Fig 8

Fig 9

Table 1

Fig 10

Table 2

List of available star sensor products"

Number	Name	Product technical status	Application
1	High-precision star sensor APS VI type	Measurement accuracy (3σ): 3″(x/y), 25″(z) Optical axis thermal drift better than 0.3″/℃ Stray light suppression angle: 35° Dynamic performance: 3°/s Data update rate: 12 Hz Weight: probe 800g; line box 700g Power consumption: probe 1.8 W; line box 5W Power supply: 20−50 V, relay mode configurable Data interface: RS422, 1553	Applicable to low, medium, and high orbits
2	High-precision star sensor APS II type	Measurement accuracy (3σ): When the absolute value of angular velocity is less than 0.1º/s, single head: x/y≤1"; z≤30" Two probes: When the installation angle is 90°, ≤0.7"(x/y); ≤0.7"(z) When the installation angle is 60°, ≤0.9"(x/y); ≤1.0"(z) Three probes: When the installation angle is 90°, ≤0.6"(x/y); ≤0.6"(z) When the installation angle is 60°, ≤0.8"(x/y); ≤1.0"(z) Optical axis thermal drift is better than 0.3″/℃ (15℃−25℃) Sunlight suppression angle 30° Earth-air light suppression angle 30° Dynamic performance: 2°/s Data update rate: 8 Hz Weight: Probe ≤3.7 kg, circuit box ≤7 kg Dimensions: Probe 156 mm×170 mm×392 mm Line box 144 mm×196 mm×258.5 mm Power consumption: Probe ≤4 W, Line box ≤45 W. Data interface: 1553B interface Power supply: +28−+42 V	Applicable to low, medium, and high orbits
3	Ultra-high precision star sensor APS I type	Field of view (°): 2×2; Sensitive magnitude (Mv): +10.5; Optical axis pointing accuracy (3σ): 0.3"; Update rate (Hz): 8; Dynamic performance (°/s): 0.2; Working temperature (℃): −30−+40; Sunlight suppression angle (°): 35; Body size (mm×mm×mm): Probe: 360×360 ×655; Line: 190×110 ×157; Weight (kg): Probe: to be determined; Line: to be determined; Electrical interface: RS422/1553B/LVDS; Power consumption (W): Probe: 2; Line: 10; Design life (a): 15;	Applicable to low, medium, and high orbits
4	Ultra-high precision star sensor APS II	Measurement accuracy (3σ): When angular velocity ≤0.1/s, better than 0.5″ (X/Y); Angular velocity 0.1−1 /s, better than 1″ (X/Y) Angular velocity 1−2/s, better than 2″ (X/Y) Angular velocity 2−5/s, better than 15″ (X/Y) Optical axis thermal drift is better than 0.1″/℃ (15℃−25℃) Sunlight suppression angle 30° Earth-air light suppression angle 30° Dynamic performance: 5°/s Data update rate: 8 Hz Weight: probe ≤5 kg, circuit box ≤10 kg Dimensions: probe 156 mm×170 mm×392 mm Circuit box 144 mm×196 mm×258.5 mm Power consumption: probe ≤4 W, circuit box ≤45 W. Data interface: 1553B interface Power supply: +28−+42 V	Applicable to low, medium orbits

Table 2

Fig 11

Fig 12

Fig 13

Fig 14

Fig 15

Fig 16

Fig 17

References 34

1	RAMOS A L, FERREIRA J V, BARCELÓ J Model-based systems engineering: an emerging approach for modern systems. IEEE Trans. on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2011, 42 (1): 101- 111.
2	MADNI A M, SIEVERS M Model-based systems engineering: motivation, current status, and research opportunities. Systems Engineering, 2018, 21 (3): 172- 190. doi: 10.1002/sys.21438
3	KANG K C, LEE J, DONOHOE P Feature-oriented product line engineering. IEEE Software, 2002, 19 (4): 58- 65. doi: 10.1109/MS.2002.1020288
4	HAUSE M, HUMMELL J Model-based product line engineering–enabling product families with variants. INCOSE Insight, 2019, 22 (2): 43- 48. doi: 10.1109/aero.2015.7119108
5	LYKINS H, FRIEDENTHAL S, MEILICH A Adapting UML for an object oriented systems engineering method (OOSEM). INCOSE International Symposium, 2000, 10 (1): 490- 497. doi: 10.1002/j.2334-5837.2000.tb00416.x
6	DOUGLASS B P. Agile systems engineering[M]. Waltham: Morgan Kaufmann, 2015.
7	MORKEVICIUS A, ALEKSANDRAVICIENE A, et al Towards a aommon systems engineering methodology to cover a complete system development process. INCOSE International Symposium, 2020, 30 (1): 138- 152. doi: 10.1002/j.2334-5837.2020.00713.x
8	CHEN R R, LIU Y S, FAN H R, et al An integrated approach for automated physical architecture generation and multi-criteria evaluation for complex product design. Journal of Engineering Design, 2019, 30 (2−3): 63- 101. doi: 10.1080/09544828.2018.1563287
9	GAO S, CAO Y, LI Y Q, et al. Reusability, reconfigurability and efficiency optimization of satellite network modeling and simulation. IEEE Access, 2023: 13: 122035−122058.
10	FU C, LIU J B, WANG S D Building SysML model graph to support the system model reuse. IEEE Access, 2021, 9, 132374- 132389. doi: 10.1109/ACCESS.2021.3115165
11	CAO Y, LIU Y S, YE X P, et al An automated approach for execution sequence-driven software and physical co-design of mechatronic systems based on hybrid functional ontology. Computer Aided Design, 2021, 131, 102942. doi: 10.1016/j.cad.2020.102942
12	PAHL G, BEITZ W. Engineering design-a systematic approach. 3rd ed. London: Springer-Verlag, 2007.
13	GÓNGORA H G C, FERROGALINI M, MOREAU C. How to boost product line engineering with MBSE-a case study of a rolling stock product line. Proc. of the 5th International Conference on Complex Systems Design & Management, 2015: 239–256.
14	YOUNG B, CLEMENTS P Model based engineering and product line engineering: combining two powerful approaches at raytheon. INCOSE Insight, 2019, 22 (2): 67- 75. doi: 10.1002/inst.12248
15	BILIC D, BROSSE E, SADOVYKH A, et al. An integrated model-based tool chain for managing variability in complex system design. Proc. of the ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, 2019: 288–293.
16	TEKİNDERDOĞAN B, ÖZCAN M K, YAKıN İ, et al Model-based systems product line engineering of physical protection systems. INCOSE International Symposium, 2021, 31 (1): 1- 15. doi: 10.1002/j.2334-5837.2021.00822.x
17	FORLINGIERI M. The four dimensions of variability and their impact on MBPLE: how to approach variability in the development of aircraft product lines at Airbus. Proc. of the 16th International Working Conference on Variability Modelling of Software-Intensive Systems, 2022. DOI: 10.1145/3510466.3511275.
18	COLLETTI R A, QAMAR A, NUESCH S P, et al Best practice patterns for variant modeling of activities in model-based systems engineering. IEEE Systems Journal, 2020, 14 (3): 4165- 4175. doi: 10.1109/JSYST.2019.2939246
19	FORLINGIERI M, WEILKIENS T Two variant nodeling nethods for MBPLE at airbus. INCOSE International Symposium, 2022, 32 (1): 1097- 1113. doi: 10.1002/iis2.12984
20	ARIFIN H H, KIT H, DAI J, et al. Model-based product line engineering with genetic algorithms for automated component selection. Proc. of the 4th International Conference on Complex Systems Design & Management Asia and of the 12th Conference on Complex Systems Design & Management, 2021: 287−310.
21	WAGNER H, ZUCCARO C. Managing variability in digital twins and system development through product line engineering. Proc. of the IEEE International Symposium on Systems Engineering, 2024. DOI: 10.1109/ISSE63315.2024.10741090.
22	LAMEH J, DUBRAY A, JANKOVIC M Towards a configuration management integration to feature models in model-based product line engineering. Proceedings of the Design Society, 2023, 3 (7): 3581- 3590.
23	WAGNER H, RÖSSLER W, ZUCCARO C. How to structure variability in large-scale complex engineered systems. Proc. of the Tag des Systems Engineering, 2023: 272–279.
24	ESSOUSSI M H, VIVOT P, DELOYE L, et al. Large legacy systems design maintainability through modeling. Proc. of the ERTS 2024, 2024: 1–10.
25	COLLETTI R A, QAMAR A, NUESCH S, et al. Variant modeling for multi-perspective, multi-fidelity systems simulation. Recent Trends and Advances in Model Based Systems Engineering. Cham: Springer, 2022.
26	COLLETTI R A, QAMAR A, BAILEY W, et al A variable reference architecture for the management and configuration of ground vehicle simulations. Innovations in Systems and Software Engineering, 2024, 20 (3): 185- 207. doi: 10.1007/s11334-022-00441-x
27	BAJAJ M, FRIEDENTHAL S, SEIDEWITZ E. Systems modeling language (SysML v2) support for digital engineering. INCOSE Insight 2022, 25(1): 19–24.
28	EPP J, ROBERT T, RUCH O, et al. Towards SysML v2 as a variability modeling language. Proc. of ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, 2023: 251–256.
29	EPP J, ROBERT T, RUCH O, et al Transitioning towards SysML v2 as a variability modeling language. Innovations in Systems and Software Engineering, 2024, 20 (2): 585- 596.
30	LU J Z, WANG G X, YAN Y, et al Semantic model-based systems engineering based on KARMA: a research and practice roadmap 2025. INCOSE International Symposium, 2022, 32 (1): 706- 720. doi: 10.1002/iis2.12959
31	LU J Z, WANG G X, MA J D, et al General modeling language to support model-based systems engineering formalisms (Part 1). INCOSE International Symposium, 2020, 30 (1): 323- 338. doi: 10.1002/j.2334-5837.2020.00725.x
32	WADA A, KOMATSU Y, TATE D, et al Phased demonstrations of MBSE in small demonstration satellite series: development of system model and environment for full application of MBSE. INCOSE International Symposium, 2023, 33 (1): 1188- 1202. doi: 10.1002/iis2.13077
33	SELIC B. A Systematic approach to domain-specific language design using UML. Proc. of the 10th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing, 2007: 2–9.
34	ZHANG B, WU Y F, ZHAO B Y, et al Progress and challenges in intelligent remote sensing satellite systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15, 1814- 1822. doi: 10.1109/JSTARS.2022.3148139

Typical configuration	Application description
Low orbit stable	Applicable to satellites working in low orbit in steady state or with general attitude maneuvering/side swinging capability
Low orbit agile	Applicable to satellites with high requirements for low orbit attitude maneuvering capability
Medium and high orbit stable	Applicable to satellites working in medium and high orbit in steady state or with general attitude maneuvering/side swinging capability
Medium, high, and low orbit agile	Applicable to medium, high, and low orbits, with high system integration, high-precision measurement, pointing, and rapid maneuvering capability can be achieved through the optional three-supervision platform, achieving comprehensive performance improvement