基于神经网络的航位推算导航

针对航位推算法中陀螺仪对方向测量存在较大噪声,进而导致航位推算误差较大的问题,提出一种基于神经网络的航位推算方法.该方法利用神经网络考察加速度测量值与方向测量值之间的时变关系,从而在不使用陀螺仪的情况下,通过该时变关系使用加速度值计算方向值,进而完成水下自主航行器(AUV)的航位推算导航.结果表明,该算法能在仅使用加速度计的情况下完成航位推算导航,因此可避免近海面由陀螺仪噪声导致的航位推算误差问题.仿真实验证明了该算法准确率较高.

Dead Reckoning Navigation Based on Neural Network

Aiming at the problem that there was a lot of noise in the measurement of the direction of the gyroscope, resulting in a large error to the dead-reckoning navigation, we proposed a method of dead\|reckoning navigation based on neural network. The method used neural network to investigate the time-varying relationship between acceleration measurement and orientation measurement. Thus, we used acceleration value to calculate direction value through this time varying relationship without using the gyroscope, and then completed the autonomous underwater vehicle (AUV) dead\|reckoning navigation. The results show that the algorithm can only use the accelerometer to complete dead\|reckoning navigation. So we can avoid dead-reckoningerrors caused by gyroscope noise at the sea surface. Simulation results show that the algorithm has higher accuracy.