指数平滑软卡方的低成本惯导组合导航方法OA

In low-altitude complex environments,GNSS observation quality is strongly non-stationary,and abrupt epoch-to-epoch changes can trigger pronounced solution jitter and accuracy degradation in loose-coupled strapdown inertial navigation system(SINS)/GNSS integration with low-cost sensors.To enhance robustness and positioning accuracy under such abrupt quality variations,an exponentially smoothed sequential soft chi-square adaptive measurement-weighting method is proposed.Within an error-state Kalman filter(ESKF)framework,component-wise normalized innovation statistics are constructed to perform consistency checking,and a dual-threshold soft regulation mechanism is employed to continuously down-weight abnormal measurement components,thereby avoiding information loss caused by hard rejection.Meanwhile,an exponential weighted moving average(EWMA)is applied to the online-updated measurement covariance matrix,which suppresses high-frequency switching of measurement weights and filter gains in abnormal intervals and consequently reduces oscillatory behavior of the fused solution.Experiments conducted on an open-source MPU6500-GNSS dataset with pronounced GNSS fluctuations demonstrate that the proposed method significantly mitigates horizontal error spikes and improves trajectory smoothness.Over the 1 100-1 800 s interval,the root mean square(RMS)of the east-position error decreases from 19.303 m to 7.986 m and the maximum decreases from 99.512-55.325 m;the RMS of the 3D position-error norm decreases from 19.411-10.201 m and the maximum decreases from 99.523-63.223 m.The results indicate that the proposed scheme effectively limits the adverse impact of abrupt GNSS quality degradation,offering a simple,computationally light,and statistically interpretable strategy for stable and accurate low-cost inertial/GNSS positioning.