An integrated framework for GPS flex power analysis: robust detection, automated localization, and spatiotemporal characteristics

The dynamic redistribution of transmit power by modernized Global Position System (GPS) satellites, known as flex power, significantly enhances anti-jamming capabilities but simultaneously introduces code biases into precise observables, specifically affecting Differential Code Bias (DCB). Notably, the spatial centroids of these flex power zones frequently coincide with regions experiencing complex electromagnetic environments, thereby necessitating robust and reliable monitoring capabilities. However, current monitoring paradigms exhibit critical limitations: existing detection methods suffer from low-gain false alarms and transition-boundary delays, while spatial localization relies entirely on inefficient manual fitting. To address these deficiencies, this study proposes an integrated framework comprising rigorous theoretical modeling, a novel Closed-Loop Flex Power Detection with Sidereal Filtering (CL-FPD-SF) method, and a Greedy-Strategy-Based Adaptive Centroid Fitting Algorithm (GACFA). Experimental results demonstrate the CL-FPD-SF method yields an exceptional average True Positive Rate (TPR) of 0.99997 and a False Positive Rate (FPR) of 3.6× 10⁻⁶ 3.6 × 10 - 6 . Meanwhile GACFA automates the localization of enhancement centers, effectively replacing traditional manual fitting. Through continuous analysis of global data streams over a five-year period (2020–2025), the proposed framework successfully identified and mapped nine distinct flex power modes, thereby validating the algorithm's robustness and long-term reliability.