协同导航网络多传感器信息融合技术研究

发布时间：2019-02-19 10:22

【摘要】：随着世界各国对海洋问题的逐步重视,水面高机动无人艇获得了空前的高速发展,并将会在将来海战中扮演重要角色。为了适应更复杂的未来海洋作战环境,无人艇协同导航技术与传统导航技术相比也面临着巨大的挑战。本课题将从协同导航网络中的多传感器信息融合技术入手,首先对目前各国无人艇技术研究状况进行了简要介绍,通过对国内关于无人艇发展的研究分析可知,我国虽然在近几年获得了较大的发展,但是与美国和以色列等无人艇技术强国之间还存在较大的差距。就无人艇的协同导航网络而言,缩短其差距的方法不仅是要提高协同网络中的传感器精度,而且还要改进信息融合中的最优估计准则。所以本文主要就协同导航网络多传感器信息融合的结构、信息来源和融合准则进行了分析和仿真比较,得出针对不同噪声环境和不同系统方程的融合准则优劣不同。其次,本文针对无人艇协同导航网络中的信息来源进行分析,并且根据信息特征建立了合适的数学模型进行仿真分析。鉴于惯性导航系统在其协同导航网络中的重要地位,本文对惯性导航系统的原理及建模和仿真进行了较为详尽的分析介绍。通过对惯性导航系统、GPS全球定位系统、DR航位推算系统和移动长基线定位系统的建模和仿真,为后文中信息融合最优估计准则的算法实现提供了数据来源。最后,本文对系统导航网络中信息融合最优估计准则进行了仿真分析,并且根据根据进行数据融合子系统的特点分别设计了不同的卡尔曼滤波器。GPS和惯性导航系统进行信息融合时,融合准则采用线性的卡尔曼滤波器,并且通过仿真分析可以得出对于线性组合导航系统,自适应卡尔曼滤波器在计算量没有大幅度增加的情况下,可以获得良好的滤波效果。而对于GPS和DR信息融合系统,则采用非线性的卡尔曼滤波器包括扩展卡尔曼滤波器和无迹卡尔曼滤波器。在外部噪声为高斯白噪声的情况下,无迹卡尔曼滤波器获得了良好的滤波效果。但是当系统噪声和量测噪声的统计特性不满足高斯白噪声的时候,以上两种非线性滤波器将出现较大的误差,甚至导致滤波器的发散。经过信息融合主要技术的研究和仿真分析可得,针对无人艇协同导航网络而言,利用多传感器信息融合技术可以极大改善其定位精度和导航信息质量。
[Abstract]:With the gradual attention of the world to the ocean problem, the surface high motorized unmanned craft has obtained unprecedented high speed development, and will play an important role in the future naval battle. In order to adapt to the more complex future marine combat environment, the cooperative navigation technology of unmanned craft is facing a great challenge compared with the traditional navigation technology. This subject will start with the multi-sensor information fusion technology in the cooperative navigation network. Firstly, the research status of unmanned craft technology in various countries is briefly introduced, and through the research and analysis on the development of unmanned craft in China, we know that, Although China has made great progress in recent years, there is still a big gap between China and the United States and Israel. As far as the cooperative navigation network of unmanned craft is concerned, the method to shorten the gap is not only to improve the sensor accuracy in the cooperative network, but also to improve the optimal estimation criterion in information fusion. In this paper, the structure, information source and fusion criteria of multi-sensor information fusion in cooperative navigation network are analyzed and compared, and the results show that the fusion criteria for different noise environments and different system equations are different. Secondly, this paper analyzes the sources of information in the UAV cooperative navigation network, and establishes a suitable mathematical model for simulation analysis according to the characteristics of the information. In view of the important position of inertial navigation system in its cooperative navigation network, the principle, modeling and simulation of inertial navigation system are analyzed and introduced in detail in this paper. Through the modeling and simulation of inertial navigation system, GPS global positioning system, DR dead reckoning system and mobile long baseline positioning system, this paper provides a data source for the algorithm realization of information fusion optimal estimation criterion. Finally, the optimal estimation criterion of information fusion in system navigation network is simulated and analyzed. According to the characteristics of the data fusion subsystem, different Kalman filters are designed. When GPS and inertial navigation system are fused, the linear Kalman filter is used as the fusion criterion. The simulation results show that the adaptive Kalman filter can obtain a good filtering effect without a large increase in the computational complexity for the linear integrated navigation system. For GPS and DR information fusion systems, the nonlinear Kalman filter includes extended Kalman filter and unscented Kalman filter. When the external noise is Gao Si white noise, the unscented Kalman filter has a good filtering effect. However, when the statistical characteristics of system noise and measurement noise are not satisfied with Gao Si white noise, the above two nonlinear filters will have large errors and even lead to the divergence of filters. Through the research and simulation analysis of the main technology of information fusion, the positioning accuracy and the quality of navigation information can be greatly improved by using multi-sensor information fusion technology for unmanned craft cooperative navigation network.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U666.1;TP202;TN96

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