This paper proposes a low cost and small size attitude and heading reference system based on MEMS inertial sensors. A dual-axis rotation structure with a proper rotary scheme according to the design principles is applied in the system to compensate for the attitude and heading drift caused by the large gyroscope biases. An optimization algorithm is applied to compensate for the installation angle error between the body frame and the rotation table’s frame. Simulations and experiments are carried out to evaluate the performance of the AHRS. The results show that the proper rotation could significantly reduce the attitude and heading drifts. Moreover, the new AHRS is not affected by magnetic interference. After the rotation, the attitude and heading are almost just oscillating in a range. The attitude error is about 3° and the heading error is less than 3° which are at least 5 times better than the non-rotation condition.

Doppler velocity log (DVL) aided strapdown inertial navigation system (SINS) is a common navigation method for underwater applications. Owing to the in‐motion condition and the lack of the GPS, it is a challenge to align a SINS under water. This paper proposed a complete in‐motion alignment solution for both attitude and position. The velocity update equation and its integral form in the body frame are studied, and the attitude coarse alignment becomes an optimization‐based attitude determination problem between the body frame velocity and the integral form of gravity. The body frame velocity and the Earth frame position are separately treated, and the position alignment problem turns into an equation solving problem. Simulation and on‐lake tests are carried out to examine the algorithm. The heading could reach around 10 deg accuracy and the pitch and roll could be aligned up to 0.05 deg in 60 s. With attitude error of this level, the heading could reach 1 deg accuracy in 240 s using unscented Kalman filter (UKF) based fine alignment. The final position error could achieve 1.5% of the voyage distance. This scheme can also be applied to other body frame velocity aided SINS alignments.

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.

Interferometric fiber-optic hydrophones are an important means in the field of underwater acoustic detection. The design of the hydrophone sensor head is the key technology related to its detection sensitivity. In this paper, a high-sensitivity cuboid interferometric fiber-optic hydrophone based on planar rectangular film sensing is proposed, and the sensitivity of the sensor is compared with that of the widely used air-backed mandrel hydrophone under the same conditions. The acoustic characteristic models of the two types of sensors were established by theoretical calculation and simulation analysis to obtain the theoretical pressure sensitivity. Some experiments were performed to examine the theory and design. According to the experiment results, the mean phase sensitivity of the mandrel type was −112.85 dB re 1 rad/μPa in the operating frequency range of 10–300 Hz, and that of the cuboid type was −84.50 dB re 1 rad/μPa. The latter was 28.35 dB higher than the former was. These results are useful for improving hydrophone sensitivity.

The multichannel active noise control (MCANC) approach is widely regarded as an effective solution to achieve a large noise cancellation zone in a complicated acoustic environment. However, the sluggish convergence and massive computation of traditional adaptive multichannel active control algorithms typically impede the MCANC system's practical applications. The recently developed selective fixed-filter method offers a way to decrease the computational load in real-time scenarios and enhance the reaction time. Nevertheless, this method is specifically designed for the single-channel ANC system and only considers the frequency information of the noise. This inevitably impacts the effectiveness of reducing noise from various directions, particularly in the MCANC system. Therefore, we proposed a spatial-frequency-based selective fixed-filter ANC technique that adopts the Bhattacharyya Distance Matching (SFANC-BdM). In our work, the BdM is a one-step spectra and is designed by calculating similarity of different data distribution. According to the most similar case, the corresponding control filter is then selected. By avoiding separately extracting the direction and frequency information, the proposed method significantly increases the algorithm's efficiency. Compared to the conventional SFANC method, it enables a more accurate filter choice and achieves better noise reduction.

The traditional temperature modeling method is based on the full heating of the accelerometer to achieve thermal balance, which is not suitable for the cold start-up phase of the micro-accelerometer. For decreasing the complex temperature drift of the cold start-up phase, a new temperature compensation method based on a high-order Fourier transform combined model is proposed. The system structure and repeatability test of the micro digital quartz flexible accelerometer are provided at first. Additionally, we analyzed where the complex temperature drift of the cold start-up phase comes from based on the system structure and repeatability test. Secondly, a high-order temperature compensation model combined with K-means clustering and the symbiotic organisms search (SOS) algorithm is established with repeatability test data as training data. To verify the proposed temperature compensation model, a test platform was built to transmit the measured values before and after compensation with the proposed Fourier-related model and the other time-related model, which is also a model aiming at temperature compensation in the cold start-up phase. The experimental results indicate that the proposed method achieves better compensation accuracy compared with the traditional temperature compensation methods and the time-related compensation model. Furthermore, the compensation for the cold start-up phase has no effect on the original accuracy over the whole temperature range. The stability of the accelerometer can be significantly improved to about 30 μg in the start-up phase of different temperatures after compensation.