计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
7期
254-258
,共5页
马春来%朱立新%孟%耿晓娟
馬春來%硃立新%孟%耿曉娟
마춘래%주립신%맹%경효연
无迹粒子滤波算法(UPF)%建议分布%全局采样%全球定位系统/惯性导航系统(GPS/INS)%组合导航
無跡粒子濾波算法(UPF)%建議分佈%全跼採樣%全毬定位繫統/慣性導航繫統(GPS/INS)%組閤導航
무적입자려파산법(UPF)%건의분포%전국채양%전구정위계통/관성도항계통(GPS/INS)%조합도항
Unscented Particle Filter(UPF)%proposal density%global sampling%Global Positioning System/Inertial Navi-gation System(GPS/INS)%integrated navigation
针对UPF(Unscented Particle Filter)由于计算量大而难以应用于GPS/INS(Global Positioning System/Iner-tial Navigation System)组合导航中的问题,提出一种基于全局采样的UPF算法。结合了最新的量测值,对粒子集整体利用一次UKF(Unscented Kalman Filter)算法产生建议分布,免去了UPF中对每个粒子循环套用UKF的环节,省去了重采样步骤,减少了UPF的计算量。仿真实验采用以伪距为观测量的状态变量为10维的非线性模型,结果表明,改进的UPF与PF相比,具有更高的估计精度,与UPF相比,具有更小的计算量,能够解决UPF难以应用于GPS/INS组合导航中的问题。
針對UPF(Unscented Particle Filter)由于計算量大而難以應用于GPS/INS(Global Positioning System/Iner-tial Navigation System)組閤導航中的問題,提齣一種基于全跼採樣的UPF算法。結閤瞭最新的量測值,對粒子集整體利用一次UKF(Unscented Kalman Filter)算法產生建議分佈,免去瞭UPF中對每箇粒子循環套用UKF的環節,省去瞭重採樣步驟,減少瞭UPF的計算量。倣真實驗採用以偽距為觀測量的狀態變量為10維的非線性模型,結果錶明,改進的UPF與PF相比,具有更高的估計精度,與UPF相比,具有更小的計算量,能夠解決UPF難以應用于GPS/INS組閤導航中的問題。
침대UPF(Unscented Particle Filter)유우계산량대이난이응용우GPS/INS(Global Positioning System/Iner-tial Navigation System)조합도항중적문제,제출일충기우전국채양적UPF산법。결합료최신적량측치,대입자집정체이용일차UKF(Unscented Kalman Filter)산법산생건의분포,면거료UPF중대매개입자순배투용UKF적배절,성거료중채양보취,감소료UPF적계산량。방진실험채용이위거위관측량적상태변량위10유적비선성모형,결과표명,개진적UPF여PF상비,구유경고적고계정도,여UPF상비,구유경소적계산량,능구해결UPF난이응용우GPS/INS조합도항중적문제。
It is difficult for UPF(Unscented Particle Filter)applying to the GPS/INS(Global Positioning System/Inertial Navigation System)integrated navigation because of its large calculation, so the method of proposal distribution generation for UPF based on global sampling is presented. Combining the latest measured values, it uses UKF to generate a proposal distribution for the particle set, eliminating the need for the UPF which applies UKF(Unscented Kalman Filter)link to each particle for circulation, eliminating the resembling step, reducing the computation of the UPF. Simulations use the pseudo range as the observations in nonlinear model which has 10-dimensional state variables. The results show that com-pared with PF, improved UPF has a higher accuracy in estimation, compared with UPF, it has smaller computation. Therefore it can solve the problem that it is difficult to apply to the GPS/INS integrated navigation for UPF because of its large calculation.