知识与财富的链接
陆用航位推算系统（DR）的精度主要受到里程系数和航向误差的制约,其误差模型本质具有非线性,因此采用非线性滤波算法能显著提高里程系数和航向误差的估计精度。本文将粒子滤波应用到航位推算（DR）/GPS组合导航系统数据融合过程中,对航位推算 (DR) 与GPS组合导航系统中的里程系数误差和航向误差进行辨识估计,并对里程系数和航向进行修正。粒子滤波存在的主要问题是粒子的退化现象严重,本文将量子粒子群优化 (PSO) 算法与粒子滤波相结合,提出了量子PSO粒子滤波算法,该算法采用量子位对粒子进行编码,引入量子旋转门与变异操作保持了粒子集的多样性,通过量子PSO搜索寻优重新分配粒子,使粒子集有效地逼近真实的后验概率分布,从而有效地减轻了退化现象,提高了粒子滤波的精度。跑车实验结果表明,该算法有效地抑制了DR导航系统误差的增长,提高了组合导航系统的定位精度。
The accuracy of dead reckoning system(DR) is mainly restrained by two factors, which are odometer scale factor error and azimuth error. The error model for DR system is nonlinear in essence, which means nonlinear filtering method can better estimate the two error factors. Particle filter is applied in the data fusion process of Dead Reckoning (DR)/GPS integrated navigation system, estimating the mileage coefficient error and azimuth error. The main problem for particle filter is the degeneracy of particles. Quantum PSO particle filter (QPSO-PF) is proposed by combing the quantum PSO algorithm with particle filter. In QPSO-PF, the quantum bit is used to encode the particle and the quantum rotating gate and mutation are introduced to guarantee the diversity of particle, and the particles are redistributed by the optimization search of Quantum PSO, and makes the particle set effectively approximate the true posterior probability distribution, which effectively alleviates the degeneracy of particle and improves the precision of particle filter. The results of vehicle experiment show the increase trend of error of DR navigation system is effectively restrained, and the position precision of integrated navigation system is effectively improved by using QPSO-PF.
