光纤陀螺仪信号处理与温度补偿的研究
发布时间：2018-10-09 19:35
【摘要】：光纤陀螺是一种基于Sagnac效应的新型全固态惯性测量传感器，因其结构紧凑、精确度高、抗干扰能力强、启动时间短、寿命长等优势，使得光纤陀螺拥有十分广阔的发展前景；本文结合前人对光纤陀螺的研究经验，设计了以FPGA为核心的数字双闭环干涉式光纤陀螺系统，，并在光纤陀螺数字信号处理、硬件平台开发、软件系统设计实现以及离线温度补偿方法上做了较为深入的研究；具体开展的研究工作如下： 首先，本文概述了光纤陀螺的发展历程、研究现状以及技术特点；以干涉式光纤陀螺为研究对象，在介绍了光纤陀螺的基本原理-Sagnac效应基础上，着重分析了干涉式光纤陀螺的信号检测方案，并针对由Y波导集成光学器件随温度变化而产生调制增益误差的问题，设计了数字双闭环信号检测控制方案。 其次，本文通过深入研究分析对比了方波偏置调制方案与双方波调制方案的优缺点，最终选择了对交叉干扰抑制效果更好的双方波调制解调方案；然后根据光纤陀螺输出干涉信号的特点与信号检测系统设计的要求，完成主要元器件的选型、硬件电路的设计以及PCB板的绘制焊接，进而利用Verilog HDL硬件描述语言完成软件系统的设计，并对系统软、硬件进行组合测试。 最后，本文阐述了粒子群与混沌粒子群算法的基本原理，通过Matlab仿真对比分析了混沌粒子群算法较普通粒子群算法的性能优势，进而给出了基于混沌粒子群优化的BP神经网络的训练方法，并采用混沌粒子群优化的BP神经网络算法对光纤陀螺进行离线温度补偿，补偿结果表明该方法能够较为快速准确的拟合陀螺零偏与标度因数随温度变化的规律，降低了零偏与标度因数温度误差产生的角速度输出误差。
[Abstract]:Fiber optic gyroscope (fog) is a new all-solid-state inertial measurement sensor based on Sagnac effect. Because of its compact structure, high precision, strong anti-interference ability, short start-up time and long life, fiber optic gyroscope has a very broad development prospect. Based on the previous research experience of fiber optic gyroscope (fog), a digital double closed loop interferometric fiber optic gyroscope (fog) system with FPGA as the core is designed and developed in the digital signal processing and hardware platform of fog. The design and implementation of the software system and the off-line temperature compensation method are studied deeply. The specific research work is as follows: firstly, the development history, research status and technical characteristics of fiber optic gyroscope (fog) are summarized in this paper. Taking the interferometric fiber optic gyroscope as the research object, the basic principle of the fiber optic gyroscope (fog) is introduced, and the signal detection scheme of the interferometric fiber optic gyroscope is analyzed emphatically. Aiming at the problem of modulation gain error caused by Y waveguide integrated optical device with temperature, a digital double closed loop signal detection control scheme is designed. Secondly, this paper analyzes and compares the advantages and disadvantages of square wave offset modulation scheme and two-wave modulation scheme, and finally chooses the two-wave modulation and demodulation scheme which has better effect on cross-interference suppression. Then according to the characteristics of the output interference signal of fog and the design requirements of signal detection system, the selection of main components, the design of hardware circuit and the drawing and welding of PCB board are completed. Then the software system is designed by using Verilog HDL hardware description language, and the software and hardware of the system are tested. Finally, the basic principle of particle swarm optimization (PSO) and chaotic PSO (chaotic PSO) are described. The performance of chaotic PSO is compared with that of ordinary PSO by Matlab simulation. Furthermore, the training method of BP neural network based on chaotic particle swarm optimization is presented, and the BP neural network algorithm based on chaotic particle swarm optimization is used to compensate the off-line temperature of fiber optic gyroscope. The compensation results show that the method can fit the variation rule of gyro zero bias and scale factor with temperature quickly and accurately, and reduce the angular velocity output error caused by zero deviation and scale factor temperature error.
【学位授予单位】：北京理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN253;TP212
本文编号：2260524
[Abstract]:Fiber optic gyroscope (fog) is a new all-solid-state inertial measurement sensor based on Sagnac effect. Because of its compact structure, high precision, strong anti-interference ability, short start-up time and long life, fiber optic gyroscope has a very broad development prospect. Based on the previous research experience of fiber optic gyroscope (fog), a digital double closed loop interferometric fiber optic gyroscope (fog) system with FPGA as the core is designed and developed in the digital signal processing and hardware platform of fog. The design and implementation of the software system and the off-line temperature compensation method are studied deeply. The specific research work is as follows: firstly, the development history, research status and technical characteristics of fiber optic gyroscope (fog) are summarized in this paper. Taking the interferometric fiber optic gyroscope as the research object, the basic principle of the fiber optic gyroscope (fog) is introduced, and the signal detection scheme of the interferometric fiber optic gyroscope is analyzed emphatically. Aiming at the problem of modulation gain error caused by Y waveguide integrated optical device with temperature, a digital double closed loop signal detection control scheme is designed. Secondly, this paper analyzes and compares the advantages and disadvantages of square wave offset modulation scheme and two-wave modulation scheme, and finally chooses the two-wave modulation and demodulation scheme which has better effect on cross-interference suppression. Then according to the characteristics of the output interference signal of fog and the design requirements of signal detection system, the selection of main components, the design of hardware circuit and the drawing and welding of PCB board are completed. Then the software system is designed by using Verilog HDL hardware description language, and the software and hardware of the system are tested. Finally, the basic principle of particle swarm optimization (PSO) and chaotic PSO (chaotic PSO) are described. The performance of chaotic PSO is compared with that of ordinary PSO by Matlab simulation. Furthermore, the training method of BP neural network based on chaotic particle swarm optimization is presented, and the BP neural network algorithm based on chaotic particle swarm optimization is used to compensate the off-line temperature of fiber optic gyroscope. The compensation results show that the method can fit the variation rule of gyro zero bias and scale factor with temperature quickly and accurately, and reduce the angular velocity output error caused by zero deviation and scale factor temperature error.
【学位授予单位】：北京理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN253;TP212
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