Ground-penetrating radar (GPR) enables robust navigation in environments where vision and light detection and ranging systems fail as a result of environmental degradation. Despite this, the integration of GPR onto quadrupedal platforms remains a challenge. Therefore, we analyzed the feasibility of GPR-based navigation for quadruped robots and developed a sensor-integrated framework that mitigates the noise inherent in legged locomotion. By leveraging real-time kinematic telemetry for antenna–ground height correction and hardware-synchronized blanking gates to inhibit noise during foot-strike events, our approach addresses critical challenges in dynamic sensor acquisition. The analysis of the Carnegie Mellon University-GPR dataset showed that multimodal data fusion significantly improved localization performance. By integrating GPR-derived subsurface feature matrices (time–frequency representations of buried reflections) with inertial measurement unit and encoder data, the system showed a weighted root mean square error of 0.568 m, representing a 74.5% improvement over unimodal GPR-only localization. These results show that subsurface electromagnetic features provide stable localization benchmarks even in feature-sparse indoor environments. Deep-learning-based architectures, such as a GPR-based difference- and similarity-driven odometry estimation network, showed higher precision (0.449 m root mean square error), showing the potential for advanced sensor development. The results of this study provide a reference for developing robust, redundant navigation systems, proving that GPR integration is a viable solution for quadrupedal robots operating in smoke, dust, or GPS-denied subterranean corridors.