Response Characteristics of Key Filtering Parameters and Applicability of Interference Detection for Integrated Navigation Under Spoofing Interference

To address Global Navigation Satellite System (GNSS) spoofing threats to Inertial Navigation System (INS)/GNSS integrated navigation systems, this paper analyzes the internal error propagation mechanisms and quantifies perturbation patterns within the Kalman filter (KF) architecture. Mathematical models for step-type, linear ramp, and nonlinear smooth ramp spoofing are established, and the Anomaly Signal-to-Noise Ratio (ASNR) is adopted to quantify disturbances across four core filtering dimensions based on real-world vehicular test data. The results demonstrate that filtering innovations at the forefront of information fusion respond most directly and sensitively (peaking at an ASNR of 341.4181) with a standard zero-mean Gaussian baseline, serving as the optimal metric for spoofing detection; in contrast, error states exhibit marked amplitude attenuation (maximum ASNR of 116.3291), while filter gains and state covariance show negligible variations (maximum ASNRs of 22.6023 and 19.3014, respectively). Further evaluation of Inertial Measurement Unit (IMU) accuracy constraints reveals that under step spoofing, position innovations remain robust (ASNR: 260–510), whereas velocity innovation ASNR drops by approximately 50% with IMU degradation; under linear ramp spoofing, velocity innovations dominate the response (ASNR: 41.16–70.46) while position innovations decay markedly; and under nonlinear smooth ramp spoofing, overall innovations are suppressed, and low-grade IMUs suffer severe noise masking (peak horizontal ASNRs dropping below 10), significantly enhancing attack stealthiness. The findings provide quantitative empirical evidence and theoretical guidance for anti-spoofing design in integrated navigation.