Abstract: The rapid scaling of sensor hardware, massive antenna arrays, distributed radar networks, and high-channel-count digital receivers has shifted the bottleneck in modern sensing from data acquisition to data interpretation. As apertures grow and sampling densities increase, the physical, statistical, and system-level models that describe how a scene maps into measurements become more, not less, essential. Accurate forward models of electromagnetic propagation, scattering, mutual coupling, clutter, and hardware nonidealities enable raw high dimensional measurements to be inverted into meaningful estimates of range, velocity, angle, and material properties. This abstract argues that sensing models are the indispensable link between escalating hardware capability and usable inference: they define the resolution limits and ambiguity structure of a system, enable calibration and error correction at scales where manual tuning is infeasible, and provide the priors that make learning-based and model-based estimators data-efficient and physically consistent. In large-sensor regimes, model fidelity directly governs achievable performance, since array gain and bandwidth are only realized when steering, coupling, and channel imperfections are correctly represented. We contend that continued investment in rigorous, computationally tractable sensing models spanning waveform design, array manifolds, propagation and clutter statistics, and end-to-end system simulation is a prerequisite for translating the promise of next-generation radar and large-scale sensing arrays into robust operational capability. Rather than being displaced by abundant data and deep learning, sensing models increasingly serve as the framework that constrains, validates, and accelerates those data-driven methods.

 Index Terms: Advanced sensors, Large aperture radars, Sensor models.