Randomized trial improves navigation accuracy in integrated systems, indicating significant error reduction.
The strapdown inertial navigation system (SINS) suffers from cumulative error growth, where velocity and acceleration drifts cause position errors to grow quadratically. While multi-sensor fusion using Doppler Velocity Sensors (DVS) can correct these drifts, traditional methods often assume a uniform radial velocity across the solar disk, thereby ignoring the differential surface velocities caused by solar rotation. This mismatch introduces a significant unmodeled system bias with a mean value of approximately 1.45 km/s and an upper bound of up to 2.07 km/s, which is orders of magnitude larger than the standard measurement error (~1 m/s). Such a dominant deterministic error leads to severe filter divergence, undermining the reliability of the entire integrated navigation system. This mismatch introduces unmodeled system bias, degrading filter performance. To address this issue, this paper proposes a solar disk differential velocity method within a multi-source fusion framework. By constructing a velocity measurement model that reflects the Sun’s actual geometric and rotational characteristics, this method effectively eliminates the deterministic system bias. Combined with dual-star Doppler measurements, the method enhances the observability of 3D velocity errors. Simulation results show that the proposed method significantly improves navigation accuracy compared to the traditional method. Position errors are reduced from kilometer-level magnitudes to the range of tens to hundreds of meters, with velocity errors remaining within 10−2–10−1 m/s.
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Yueqing et al. (2026) studied this question.
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