摘要
针对过程噪声为非理想高斯分布时无人水下航行器(UUV)自主导航定位存在噪声模型失配的问题,将高斯混合密度模型与容积卡尔曼滤波(CKF)相结合,设计了基于高斯混合容积卡尔曼滤波(GM-CKF)的UUV导航定位算法。建立了UUV运动模型及观测模型,利用CKF完成各高斯分量的预测更新,并将更新结果进行融合缩减与加权求和,从而实现UUV自主导航定位。通过与EKF、UKF和CKF算法仿真对比实验,验证了GM-CKF可以提高估计精度;通过UUV湖试试验,验证了基于GM-CKF的UUV自主导航定位精度和稳定性优于传统算法,其计算时间满足实时导航定位的要求。
Aiming at the problem of mismatched noise model of autonomous navigation of unmanned underwater vehicle（ UUV） with non-ideal Gaussian distribution noise,the Gaussian mixture cubature Kalman filter（ GM-CKF） based navigation algorithm of UUV is designed through combining the Gaussian mixture density distribution model with CKF.The motion model and observation model of UUV are established;the Gaussian components are predicted and updated with CKF;the results are merged and weighted,and the autonomous navigation and localization of UUV is realized.The simulation comparison experiments with EKF,UKF and CKF algorithms were conducted,which prove that the GM-CKF algorithm could improve the estimation precision.The UUV lake trial experiment was also conducted,and the result indicates that the proposed GM-CKF algorithm can provide better accuracy and stability than conventional navigation algorithms,and the computation time satisfies the requirement of real time navigation and localization of UUV.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2015年第2期254-261,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(E091002/50979017
E091002/51309067)
教育部高等学校博士学科点专项科研基金(20092304110008)
哈尔滨市科技创新人才(优秀学科带头人)研究专项资金(2012RFXXG083)资助项目
