Modeling and Prediction of Electromagnetic Compatibility (EMC) Performance for GPS Systems in Mixed-Signal and RF Environments

Abstract

Global Navigation Satellite System receivers operate in crowded electromagnetic conditions where small, unintended emissions and strong in-band or adjacent-band signals can alter timing and positioning observables. In mixed-signal architectures that combine sensitive RF front-ends, high-resolution data converters, and dense digital logic, electromagnetic compatibility challenges arise from distributed coupling paths that span packages, interconnects, and enclosures. The resulting interference does not behave as a single deterministic blemish; rather, it manifests as a variable environment whose statistics, modal structure, and nonlinearity depend on layout decisions, clocking strategies, and front-end bias points. This paper investigates a modeling and prediction framework for electromagnetic compatibility performance of GPS systems in mixed-signal and RF environments, emphasizing transparent abstractions that connect field-level descriptions, circuit macromodels, and system metrics. The approach integrates multiport representations of packages and printed-circuit boards with stochastic field descriptions, enabling consistent propagation of uncertainty from emissions through coupling to desensitization metrics at the correlator. The analysis explores the role of nonlinearity in low-noise amplifiers and mixers, converter aperture jitter and quantization noise under interference, and power integrity fluctuations that translate conducted noise into phase and gain perturbations. Attention is given to statistical indicators of robustness that align with compliance practices while avoiding overreliance on worst-case assumptions. The narrative maintains a neutral tone, focusing on reproducible formulations, algorithmic steps for prediction, and validation procedures that connect bench measurements with over-the-air results. The outcome is a cohesive set of models suitable for early-stage design exploration and for later diagnostic use when empirical artifacts emerge.