Fisher Information Limits of Satellite RF Fingerprint Identifiability for Authentication

TL;DR

This paper develops a theoretical framework based on Fisher information to analyze satellite RF fingerprinting, predicting hardware feature dominance and validating with real satellite data, leading to improved authentication performance.

Contribution

It derives Fisher information bounds for satellite RF fingerprinting, explaining performance limitations and guiding feature selection for authentication.

Findings

CRB predictions match experimental data from Iridium satellites.

Discrimination metric predicts dominant hardware features for given modulation.

DR-weighted authentication outperforms machine learning classifiers in AUC.

Abstract

RF fingerprinting authenticates satellite transmitters by exploiting hardware-specific signal impairments, yet existing methods operate without theoretical performance guarantees. We derive the Fisher information matrix (FIM) for joint estimation of in-phase/quadrature (IQ) imbalance and power amplifier (PA) nonlinearity parameters, establishing Cram\'{e}r-Rao bounds (CRBs) whose structure depends on constellation moments. A necessary condition for full IQ identifiability is that the identifiability factor~$\beta$ exceeds zero; for binary phase-shift keying (BPSK), $\beta = 0$ yields a rank-deficient FIM, rendering IQ parameters unidentifiable. This provides a plausible theoretical explanation for OrbID's near-random performance (area under the ROC curve, AUC~$= 0.53$) on Orbcomm. From the FIM, we define a discrimination metric that predicts which hardware parameters dominate authentication for a given modulation. For constant-modulus PSK signals, PA nonlinearity features are predicted to dominate while IQ features are ineffective. We validate the framework on 24~Iridium satellites using two recording campaigns, achieving cross-file PA fingerprint correlation $r = 0.999$ and confirming all four CRB predictions. A discrimination-ratio-weighted (DR-weighted) authentication test achieves AUC~$= 0.934$ from six features versus $0.807$ with equal weighting, outperforming machine-learning classifiers (AUC~$\leq 0.69$) on the same data.