【作者】 李雪莲；

【导师】 孙尧；

【作者基本信息】 哈尔滨工程大学 ， 导航、制导与控制， 2009， 博士

【摘要】 以超空化减阻技术运动的航行体在速度、灵活性等方面都有了全新的改善,改变了传统水下航行体普遍存在速度慢、航程短、精度低的缺点,开辟了水下超高速航行体更为广阔的发展空间,在水下超高速航行体上装置导航制导设备,将大大提高航行体的效能,随着航行体结构设计的不断优化,以及在体积、可靠性和抗冲击性能等诸多方面的要求更加突出,使得航行体内置惯导装置小型化发展成为一种必然趋势。随着微机电技术的发展,加之微惯性导航系统可在不依赖任何外部信息条件下实现全自主导航的突出优点,微惯性测量组合技术在水下超高速航行体上具有广阔的应用前景。但是,与传统的惯性测量组合相比,微惯性测量组合的精度明显偏低,这极大地限制了它的应用。如何充分发挥微惯性器件的优势,不断提高其测量精度,进而提高微惯性测量组合的测量精度,具有极其重要的意义。本文的研究工作正是针对提高微惯性元件和微惯性测量组合可靠性及精度这一中心展开的。进行了一种新型的九陀螺的微惯性测量组合设计,在设计中,采取斜置轴冗余配置方式,运用第4轴冗余信息实现对3个正交轴信息的校验和互补,系统的可靠性和精度较以往常用配置方式有一定的提高。对微机械陀螺漂移特性进行了分析,微机械陀螺的随机漂移比系统性漂移更复杂,对它的建模补偿对于提高测量信息精度非常重要。论文分别运用基于指数平滑的GM(1,1)模型和基于动窗平滑的GM(1,1)模型对漂移中的确定性趋势项进行了提取,并运用AR(3)模型对随机漂移进行了建模分析,经Allan方差分析表明,经GM-AR模型补偿后的漂移噪声有大幅度的降低。此外,通过对微机械陀螺同一日内的多组数据和不同日期的多组数据进行了重复性检验,结果表明,同一环境条件下的随机漂移数据具有很好的重复性,可以通过多次测量结果预先离线估计出来。在时间序列分析模型基础上,对微机械陀螺随机漂移信号进行了卡尔曼滤波处理,随机漂移信号在滤波后虽然没有完全消除,但却大幅度降低,在实际应用中,通过以上方法对微机械陀螺仪的输出信号进行滤波处理,得到了更高精度的测量结果。采用数据融合算法进一步提高本文设计系统精度。分别采用基于最优加权的最小二乘算法、有限窗加权最小二乘算法和测量方差自学习的最小二乘算法,综合利用分布在不同位置传感器的冗余和互补信息,降低了测量信息的不确定性,系统测量精度进一步提高。研究结果表明,适应水下超高速航行的复杂环境设计的新型微惯性测量组合,可以充分发挥微惯性器件高可靠性、抗动态冲击能力强的突出优点,使系统的可靠性和精度综合性能显著提高,通过漂移误差模型的补偿技术和数据融合方法,能够在现有的硬件基础上进一步提高微惯性测量组合的精度,具有良好的效果。更多还原

【Abstract】 Underwater vehicles with supercavitating and drag reduction technologies have got considerable improvement in speed and flexibility, which overcomes the defects of traditional underwater vehicles such as low speed, short voyage and bad precision, opening a wider development area for underwater superspeed vehicles. Installation of navigation equipment in such vehivles becomes essential for raising the efficiency of vehicles. Following the continuing progress in design of vehicle’s structure, we have higher requirements for volume, reliability and anti-shocking. Therefore the minimization of built-in inertial navigation system becomes more urgent. Micro inertial navigation system can realize a wholly-automated navigation independent of any external information. Combining micro mechatronic technique, the micro inertial measurement unit (MIMU) possesses wide application perspective for underwater superspeed vehicles. However, comparing with conventional inertial measurement components, MIMU has lower precision, which severely handicap its application. Hence,it is significant to know how to take full advantages of microscopic inertial parts and improve the measuring precision,so that the measuring precision of MIMU can be also improved. In the paper, a series of research work was performed for improvement of reliability and precision of MIMU.First, the MIMU with nine gyroscopes was designed, in which the 4th redundant information were used by the collocation method of inclined axis redundancy to realize check and complementation for information of 3 orthogonal axes, remarkably increasing the reliability and precision of this system compared to the conventional collocation methods.Then, the feasibility of increasing the precision of the system proposed in this thesis was discussed by using a data fusion algorithm. In order to further increase system measurement precision, redundant and complementary information coming from the sensors in different locations is synthetically utilized to reduce uncertainty of measured information by use of optimally weighted least square method (OW-LSM)and finite windowing weighted algorithm(FW-LSM) and the measuring variance self-learning weighted least squares (SL-LSM) respectively.Finally, the drift characteristics of micromechanical gyroscopes were analyzed by the method of collecting a large number of sample data. The stochastic drift of micromechanical gyroscopes is more complex than systematic drift, and its modeling and compensation are very important to increasing precision of measurement information. In this thesis, certainty trend terms in drifting were abstracted based on two improved GM(1,1) models, and modeling analysis was done on the stochastic drift using AR(3) model. Allan variance analysis shows that the drift noises after compensation of GM-AR models are greatly reduced.In addition, repeated tests were done on the micromechanical gyroscopes by using the multi group data in one day and different days, showing well information repeatability of the micromechanical gyroscopes. And the stochastic drift signals were processed using Kalman filter. Although not all the stochastic drift signals were removed, they were reduced substantially. Measurement results with higher precision can be obtained in practical application by using the method mentioned above.The research results in this thesis show that the design of MIMU can make full use of the advantages of micro inertial components, increasing the reliability and precision of the system. Compensation techniques of the data fusion algorithm and drift error model can further increase the precision of micromechanical gyroscopes under the condition of existing hardware, showing a good effect.更多还原