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With the miniaturization of inertial instruments, sensors mounted inside are vulnerable to interference. In a complex thermal transmission environment, temperature drift is the main factor restricting the precision of high-performance inertial sensors. To solve this problem, a new method for compensating the time-related cold starting temperature drift of the inertial sensors is introduced in this paper. Based on the perspective that temperature drift can be regarded as the response curve of the sensor system to temperature and temperature gradient, temperature compensation models of first-order, second-order, and higher-order are proposed. Meanwhile, the particle swarm optimization algorithm is used to solve the model parameters. Under various practical circumstances, the method can be used to flexibly compensate the temperature drift and reduce the standard deviation of the output signal by about four times. Compared to other models or algorithms, the simulation and experimental results indicate that the proposed model is superior in adaptability, stability, and reliability.

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H∞ filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H∞ filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms.

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