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Acoustic hyperbolic metamaterials are artificial materials with hyperbolic dispersion characteristics and strong anisotropy. Their negative refractive properties are the theoretical basis for studying the implementation of high-resolution focused superlenses. In response to the problem that the recognition of far-field noise sources is limited by the resolution of 0. 5 times wavelength acoustic Rayleigh diffraction recognition, combined with the excellent control effect of acoustic metamaterials on sound waves, a hyperbolic metamaterial that can achieve sub wavelength super-resolution imaging is introduced, and its negative refractive characteristics are used to design an acoustic hyperbolic structure for working at a frequency of 2 271. 5 Hz. The dispersion and negative refraction characteristics of the hyperbolic structure of this configuration were analyzed, and the results show that, the group velocity direction of sound waves propagating in this hyperbolic metamaterial is perpendicular to the wave vector and follows the normal direction of the dispersion curve. The research in this paper provides some design references for realizing arbitrary regulation of sound wave and elastic wave, as well as focusing, locating, identifying and amplifying noise sources. [ABSTRACT FROM AUTHOR]

The attitude and velocity of inertial navigation are included in a group at the same time. According to two kinds of definition of error, the left error model and right error model of Lie group are formed. In view of two Lie group error models based on Euler angles, a comparison and analysis of two error models are carried out, and the difference of two error models based on Lie group is given. In view of two typical integrated navigation system of strapdown inertial navigation system (SINS)/global position system (GPS) and SINS/Doppler velocity log (DVL), a suitable error model selection scheme is proposed. The vehicle test and boat test results show that the two error models based on Lie Group are more suitable to SINS/GPS and SINS/DVL respectively, especially with large initial misalignment angles. The selected error model has a better position accuracy, convergence speed and stability. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the noise covariance matrix parameters of the unscented quaternion estimation (USQUE) in integrated navigation attitude estimation cannot be given accurately, an USQUE based on particle swarm optimization (PSO-USQUE) algorithm is proposed. PSO is used to optimize the noise covariance matrices Q and R, and the filtering prior conditions such as the optimized noise covariance matrix are obtained. In order to verify the effectiveness of the algorithm proposed, simulation tests and micro-electro-mechanical system (MEMS) inertial navigation system/GPS vehicle tests are performed separately. The experimental results show that the PSO-USQUE algorithm has higher accuracy than the conventional algorithm for the attitude estimation problem of USQUE. [ABSTRACT FROM AUTHOR]