Abstract: Developing large-scale sensor systems spanning thousands of antenna elements, dense readout electronics, and networked data pathways is constrained less by any single component than by the interactions among them. This paper examines subsystem modeling as a central methodology for managing that complexity. By partitioning a sensor system into physically and functionally coherent subsystems (transducer, analog front-end, digitization, signal conditioning, and communication) and representing each with a validated behavioral model, designers can predict end-to-end performance, isolate error sources, and evaluate design trade-offs before committing to fabrication. We argue that well-scoped subsystem models serve three roles: they act as contracts that decouple parallel engineering efforts through defined interfaces, as simulation primitives that make full-system co-simulation tractable, and as diagnostic references against which measured hardware is benchmarked. Using representative development workflows, we show how model fidelity should be matched to design-stage coarse analytical models for architecture exploration, refined mixed-signal models for verification, and how consistent interface definitions prevent integration failures that dominate schedule risk at scale. We conclude that treating subsystem models as first-class, version-controlled artifacts rather than disposable analyses materially reduces development costs and improves the reliability of large sensor systems.

Index Terms: architecture, Large-scale system, and System-of-systems model.