贝叶斯网络增强型多模型 AUV 组合导航算法

摘要

An improved interactive multiple model filter based on Bayesian network (BN-IMM)is pro-posed.The aim is to resolve the problem when the noise of the autonomous underwater vehicle (AUV)integrated navigation system in the tough environment is uncertain or time-varying.The proposed algorithm builds a Bayes-ian network according to the relationship of characteristic variables and the system model.The parameters of the Bayesian network are used to correct the model probabilities in the interactive multiple model (IMM)algorithm which can reduce the dependence to the prior knowledge in the real mode recognition of the system.The pro-posed method can solve the problems of time lag in model transformation and probability jump in the IMM algo-rithm.The outputs of gyros and accelerometers are used as characteristic variables to establish the Bayesian net-work.Simulation results show that the BN-IMM algorithm can improve the model converting speed and the pre-cision of estimation significantly when the AUV is in maneuvering state.%针对复杂环境下自主水下航行器（autonomous underwater vehicle，AUV）组合导航系统中存在噪声不确定或者易发生变化的情况，提出一种贝叶斯网络增强型交互式多模型（interactive multiple model filter based on Bayesian network，BN-IMM）滤波算法。该算法在多模型估计基础上，引入特征变量，并根据变量与系统模型之间存在的因果关系建立贝叶斯网络；利用贝叶斯网络参数修正多模型估计中的模型切换概率，能够降低多模型算法中真实模式识别对先验知识的依赖性。该算法能够解决交互式多模型（interactive multiple model，IMM）算法中模型转换存在滞后、模型概率易发生跳变等问题，增强多模型算法的自适应能力。以陀螺和加速度计的输出作为特征变量建立贝叶斯网络，对 AUV 组合导航系统进行仿真，结果表明所提出的 BN-IMM 算法相比于传统的IMM 算法能够显著提高机动状态时模型转换速度和估计精度。