一种基于灰色粒子滤波算法的机动AUV航深内测方法

A method for self-estimating the depth of maneuvering AUV based on the grey particle filter

提出了一种将灰色预测和小波变换与标准粒子滤波相结合的灰色粒子滤波算法(GPF),并将其应用于机动AUV的航深内测。GPF针对机动AUV航深内测过程中由于AUV运动状态未知和测量噪声不断变化而导致的滤波失效问题,在粒子采样过程中结合了标准采样和灰色预测采样,保证了采样得到充分多的有效粒子。在计算粒子权重时,利用小波变换跟踪测量噪声统计特性的变化,提高了各粒子似然概率计算和权重分配的正确性。最后以外测法测得的高精度的机动AUV航深作为真实航深,对该GPF算法进行了实验对比验证,并与EKF和MMPF算法的结果作对比,实验结果表明了本文方法的有效性和实用性。

A grey particle filter （GPF） that incorporates the grey prediction algorithm and wavelet transform into the particle filter （PF） is presented. The GPF self-estimates the depth of maneuvering autonomous underwater vehicle （AUV） using the data measured by sensors equipped in the AUV under the condition that the prior maneuvering information is unknown and the measurement noise is time-varying. To implement the proposed method, the particles were sampled by standard sampling and grey prediction to insure the particles contain enough information about the true state of the maneuvering AUV. In addition, the measurement noise eovariance was modified by wavelet transform in real time. Therefore, the GPF can effectively correct the prior distribution and likelihood function of the particles and then alleviate the sample degeneracy problem which is common in the particle filter. A high accuracy depth trajectory, which tracks by the outside position sensor as the true depth of the maneuvering AUV, was employed. Then the performance of the EKF, MMPF and GPF was evaluated through the experimental data. The results show the effectiveness and robustness of the GPF.