摘要
针对基于扩展卡尔曼滤波(EKF)的航位推算(DR)在系统非线性、噪声非高斯情况下导航精度严重下降的问题,提出一种基于权值方差缩减粒子滤波的航位推算.建立无人水下航行器(UUV)的非线性运动学模型以及传感器的测量模型,利用模拟退火算法的退温函数产生自适应指数渐消因子以降低粒子权值的方差,进而增加有效粒子数,并以此替代标准粒子滤波中的重采样步骤.海试数据仿真试验表明,与基于EKF的航位推算算法相比,所设计算法避免了模型线性化、噪声非高斯的影响;与基于标准粒子滤波的航位推算相比,所设计算法降低了由于重采样导致的粒子贫化程度,从而提高了UUV导航系统的稳定性和精确性.
If the system is nonlinear and the noise is non-Gaussian, the navigation accuracy of dead reckoning （DR） based on extended Kalman filtering （EKF） decreases seriously. In order to avoid it, a new dead reckoning based on particle filtering with variance reduction of weight is presented. The non-linear kinematic model of unmanned underwater vehicle （UUV） and measurement models of sensors are formulated. The variance of particles＇ weights are reduced with an adaptive exponential fading factor produced by cooling function in the simulated annealing algorithm, and thus the number of particles is increased. The previous method is used to replace resampling procedure in standard particle filtering algorithm. Simulation results with trial data show that compared with EKF based dead reckoning, the proposed method can avoid the influence of model linearization and non-Gaussian noise, and compared with particle filtering based dead reckoning, it reduces the degree of the particles impoverishment due to the resampling, and eventually enhances the stability and accuracy of UUV＇s navigation system.
出处
《信息与控制》
CSCD
北大核心
2013年第2期173-180,188,共9页
Information and Control
基金
国家自然科学基金资助项目(E091002/50979017)
教育部高等学校博士学科点专项科研基金资助项目(20092304110008)
中央高校基本科研业务费专项资金资助项目(HEUCFZ 1026)
哈尔滨市科技创新人才(优秀学科带头人)研究专项资金项目(2012RFXXG083)
