船舶组合导航自适应迭代粒子滤波方法及应用
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摘要
针对多传感器观测信息较多、粒子采样效率较低的问题,提出了一种自适应迭代粒子滤波(adaptive iterated particle filter,AIPF)算法并应用于船舶全球定位系统/惯性导航系统组合导航系统。首先通过粒子滤波自身迭代进行其重要性密度函数的更新。其次,采用自适应退火参数的模拟退火算法,使当前量测量能够快速进入到采样过程,进而大大提高了采样效率。最后,进行了仿真对比计算以及实船试验,结果表明,AIPF算法不仅可以提供精度较高的导航精度,而且增强了滤波性能。
In multi-sensor integrated navigation,extensive observation information and low sampling efficiency will lead to poor navigation performance.An adaptive iterated particle filter is presented and applied in global positioning system/inertial navigation system integrated navigation.Firstly,importance density function is updated iteratively by the particle filter itself.Secondly,by using a simulated annealing algorithm with an adaptive annealing parameter,the current measurement can be quickly incorporated into the sampling process,resulting in greatly improved sampling efficiency.Finally,the performance of the proposed method is examined on the sea trail and the simulation experiment,respectively.The results show that the method can provide better navigation accuracy and improve performance of the filter.
In multi-sensor integrated navigation,extensive observation information and low sampling efficiency will lead to poor navigation performance.An adaptive iterated particle filter is presented and applied in global positioning system/inertial navigation system integrated navigation.Firstly,importance density function is updated iteratively by the particle filter itself.Secondly,by using a simulated annealing algorithm with an adaptive annealing parameter,the current measurement can be quickly incorporated into the sampling process,resulting in greatly improved sampling efficiency.Finally,the performance of the proposed method is examined on the sea trail and the simulation experiment,respectively.The results show that the method can provide better navigation accuracy and improve performance of the filter.
引文
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[27]张闯,郭晨.二阶自适应权值粒子滤波的多传感器信息融合[J].哈尔滨工程大学学报,2016,37(10):1347-1352.ZHANG C,GUO C.Multi-sensor information fusion of a particle filter on the basis of a two-stage adaptive weight[J].Journal of Harbin Engineering University,2016,37(10):1347-1352.
[2]KOTECHA J H,DJURIC P M.Gaussian particle filtering[J].IEEE Trans.on Signal Processing,2003,51(10):2592-2601.
[3]YIN J J,ZHANG J Q,KLAAS M.The marginal Rao-Blackwellized particle filter for mixed linear/nonlinear state space models[J].Chinese Journal of Aeronautics,2007,20(4):346-352.
[4]ZHANG C,GUO C,ZHANG D.Ship navigation via GPS/IMU/LOG integration using adaptive fission particle filter[J].Ocean Engineering,2018,156:435-445.
[5]MA Y,CHEN Y,ZHOU X,et al.Remaining useful life prediction of lithiumion battery based on Gauss-Hermite particle filter[J].IEEE Trans.on Control Systems Technology,2018,99:1-8.
[6]CHEN Y,ZHAO S,FARRELL J A.Computationally efficient carrier integer ambiguity resolution in multiepoch GPS/INS:a common-position-shift approach[J].IEEE Trans.on Control Systems Technology,2016,24(5):1541-1556.
[7]LI Z,CHANG G,GAO J,et al.GPS/UWB/MEMS-IMU tightly coupled navigation with improved robust Kalman filter[J].Advances in Space Research,2016,58(11):2424-2434.
[8]MUSAVI N,KEIGHOBADI J.Adaptive fuzzy neuro-observer applied to low cost INS/GPS[J].Applied Soft Computing Journal,2015,29(C):82-94.
[9]HU G,GAO S,ZHONG Y.A derivative UKF for tightly coupled INS/GPS integrated navigation[J].ISA Transactions,2015,56:135-144.
[10]CUI B,CHEN X,TANG X,et al.Robust cubature Kalman filter for GNSS/INS with missing observations and colored measurement noise[J].ISA Transactions,2017,72:1-9.
[11]CUI B,CHEN X,TANG X.Improved cubature Kalman filter for GNSS/INS based on transformation of posterior sigmapoints error[J].IEEE Trans.on Signal Processing,2017,65(11):2975-2987.
[12]KHALAF W,CHOUAIB I,WAINAKH M.Novel adaptive UKF for tightly-coupled INS/GPS integration with experimental validation on an UAV[J].Gyroscopy&Navigation,2017,8(4):259-269.
[13]LIU Y,FAN X,LC,et al.An innovative information fusion method with adaptive Kalman filter for integrated INS/GPSnavigation of autonomous vehicles[J].Mechanical Systems&Signal Processing,2017,100:605-616.
[14]GAO Z,GE M,SHEN W,et al.Evaluation on the impact of IMUgrades on BDS+GPS PPP/INS tightly coupled integration[J].Advances in Space Research,2017,60(6):1283-1299.
[15]GEORGY J,KARAMAT T,IQBAL U,et al.Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter[J].GPS Solutions,2011,15(3):239-252.
[16]RABBOU M A,ELRABBANY A.Integration of GPS precise point positioning and MEMS-based INS using unscented particle filter[J].Sensors,2015,15(4):7228-7245.
[17]WANG X,NI W.An improved particle filter and its application to an INS/GPS integrated navigation system in a serious noisy scenario[J].Measurement Science&Technology,2016,27(9):1-10.
[18]薛丽,高社生,胡高歌.自适应GHPF及其在组合导航中的应用[J].计算机仿真,2013,30(5):108-111.XUE L,GAO S S,HU G G.Adaptive GHPF for integrated navigation[J].Computer Simulation,2013,30(5):108-111.
[19]ZHONG M,GUO J,YANG Z.On real time performance evaluation of the inertial sensors for INS/GPS integrated systems[J].IEEE Sensors Journal,2016,16(17):6652-6661.
[20]ZHANG B Q,CHU H R,SUN T T,et al.Precision improvement methodology for INS/GPS after GPS outage using RB-UKF[J].Optics&Precision Engineering,2016,24(4):835-843.
[21]SINGH A K,RADHAKRISHNAN R,BHAUMIK S,et al.Adaptive sparse-grid Gauss Hermite filter[J].Journal of Computational and Applied Mathematics,2018,342:305-316.
[22]XIA G,WANG G.INS/GNSS tightly-coupled integration using quaternion-based AUPF for USV[J].Sensors,2016,16(8):1215-1229.
[23]XU Q,LI X,CHAN C Y.Enhancing localization accuracy of MEMS-INS/GPS/in-vehicle sensors integration during GPSoutages[J].IEEE Trans.on Instrumentation&Measurement,2018,67(8):1-13.
[24]YAFRANI M E,AHIOD B.Efficiently solving the traveling thief problem using hill climbing and simulated annealing[J].Information Sciences,2018,432:231-244.
[25]LI F,CHANG L,LI B H K.Marginalized unscented quaternion estimator for integrated INS/GPS[J].Journal of Navigation,2016,69(5):1125-1142.
[26]熊剑,魏林生,郭杭,等.基于加性四元数的SINS/CNS非线性紧组合方法[J].中国惯性技术学报,2012,20(5):596-600.XIONG J,WEI L S,GUO H,et al.SINS/CNS nonlinear tightly integrated method based on additive quaternion[J].Journal of Chinese Inertial Technology,2012,20(5):596-600.
[27]张闯,郭晨.二阶自适应权值粒子滤波的多传感器信息融合[J].哈尔滨工程大学学报,2016,37(10):1347-1352.ZHANG C,GUO C.Multi-sensor information fusion of a particle filter on the basis of a two-stage adaptive weight[J].Journal of Harbin Engineering University,2016,37(10):1347-1352.