CAJ | 학술논문

陀螺作为捷联惯性导航系统的关键传感器，其测量精度直接决定了整个系统的性能和精度指标。针对舰船高过载环境下捷联惯性导航系统陀螺输出信号出现畸变的问题，提出一种基于 BP 神经网络技术的陀螺信号智能模拟滤波方法。该方法根据系统加速度计输出值对舰船运动状态进行判断，当其输出小于设定阈值时，视为非过载环境，此时将陀螺输出用于导航计算并作为 BP 神经网络在线训练样本，以保证网络参数与当前舰船运动态势的一致性；否则视为进入高过载环境，并利用之前最新训练好的BP神经网络模拟当前陀螺信号输出，保证捷联惯性系统的平稳工作。采用智能模拟的优点是：数据并行计算速度快，不需要改变系统硬件条件。半物理仿真试验结果表明：该方法在加速度计输出为5～50g的高过载环境下，可有效改善陀螺输出信号出现畸变的问题，实现舰船运动状态的实时模拟。
타라작위첩련관성도항계통적관건전감기，기측량정도직접결정료정개계통적성능화정도지표。침대함선고과재배경하첩련관성도항계통타라수출신호출현기변적문제，제출일충기우 BP 신경망락기술적타라신호지능모의려파방법。해방법근거계통가속도계수출치대함선운동상태진행판단，당기수출소우설정역치시，시위비과재배경，차시장타라수출용우도항계산병작위 BP 신경망락재선훈련양본，이보증망락삼수여당전함선운동태세적일치성；부칙시위진입고과재배경，병이용지전최신훈련호적BP신경망락모의당전타라신호수출，보증첩련관성계통적평은공작。채용지능모의적우점시：수거병행계산속도쾌，불수요개변계통경건조건。반물리방진시험결과표명：해방법재가속도계수출위5～50g적고과재배경하，가유효개선타라수출신호출현기변적문제，실현함선운동상태적실시모의。
Under high-overload ship environment, the gyro signal in strapdown inertial navigation system may have the problem of distortion. To solve this problem, an intelligent gyro signal filtering method based on BP neural network is proposed. This method takes the accelerometer output values as the threshold to judge whether the high overload happens or not. If the output is less than the setting threshold, it means a non-overload environment, in this case the gyro outputs are used for navigation calculation and used to train BP neural networks online, to ensure that the network parameters are kept consistent with the current situation of the ship movements; otherwise it means high overload happened, and the latest trained BP neural networks are used to replace the gyro signal outputs, which can ensure the strapdown inertial navigation system works smoothly. Adopting intelligent filtering technology has lots of advantages, such as fast speed in parallel calculating, and not having to change the system hardware environment. The simulation results show that this method can effectively improve the problem of the gyro signal distortion under high overload environment with 5g to 50g accelerometer outputs, and achieve real time calculating of ship movements.