面对未来大规模无人机集群任务,若采用集中式的信息融合,计算和通信负担重,并且容错性差。而联邦滤波算法作为一种新型的分散化滤波方法,降低了算法的复杂性,提高了算法的容错性与可靠性,而且联邦滤波算法易于实现,信息分配方式灵活,计算量小。
联邦滤波器中,主滤波器与各子滤波器的状态方程相同,如式所示。假设对式进行n次独立的测量,相应的量测方程如式所示。设Xˆg(k|k)和Pg(k|k)为联邦滤波器的最优估计和协方差阵,Xˆi(k|k)和Pi(k|k)为第i个子滤波器的估计值与协方差阵(i=1,2,⋯,n),Xˆm(k|k)和Pm(k|k)为主滤波器的估计值和协方差阵。联邦滤波器的一般结构如图所示。
图 联邦滤波结构框架
联邦滤波器的工作流程分为4个步骤。
步骤1信息分配。系统的信息Q−1(k)和P−1g(k|k)在子滤波器与主滤波器的信息分配原则为
步骤2时间更新。子滤波器与主滤波器的时间更新相互独立,其中i=1,2,⋯,n,m,则时间更新方程为
步骤3量测更新。量测更新只在子滤波器中进行,即i=1,2,⋯,n,则量测更新方程为
步骤4信息融合。将各个局部滤波器的局部估计值进行融合,得到全局最优估计,即
% GPS/INS/地磁组合导航,采用联邦滤波算法 clear R=6378137; omega=7292115.1467e-11; g=9.78; T=14.4; time=3750; yinzi1=0.5; yinzi2=0.5; %initial value fai0=30*pi/180; lamda0=30*pi/180; vxe0=0.01; vye0=0.01; faie0=2.0/60*pi/180; lamdae0=2.0/60*pi/180; afae0=3.0/60*pi/180; beitae0=3.0/60*pi/180; gamae0=5.0/60*pi/180; hxjz=pi/4; vx=20*1852/3600*sin(hxjz); vy=20*1852/3600*cos(hxjz); % weichagps=25;%GPS位置误差 suchagps=0.05;%GPS速度误差 gyroe0=(0.01/3600)*pi/180; gyrotime=1/7200;%陀螺漂移反向相关时间 atime=1/1800; gyronoise=(0.001/3600)/180*pi;%陀螺漂移白噪声 beta_d=1/6000.0; %速度偏移误差反向相关时间 beta_drta=1/6000.0; %偏流角误差反向相关时间 %matrix of system equation fai=fai0; lamada=lamda0; zong=0*pi/180; heng=0*pi/180; hang=45*pi/180; F(16,16)=0; G(16,9)=0; %initial value x1(16,1)=0; %the error of sins xx=x1; xx(1)=faie0; %ljn xx(2)=lamdae0; xx(5)=afae0; xx(6)=beitae0; xx(7)=gamae0; xx(8)=(0.01/3600)*pi/180; xx(9)=(0.01/3600)*pi/180; xx(10)=(0.01/3600)*pi/180; xx(11)=0.0005; xx(12)=0.0005; xx(13)=0.0005; %w=[gyronoise,gyronoise,gyronoise,gyronoise,gyronoise,gyronoise,g*1e-5,g*1e-5]'; g1=randn(1,time); g2=randn(1,time); g3=randn(1,time); g4=randn(1,time); g5=randn(1,time); g6=randn(1,time); g7=randn(1,time); g8=randn(1,time); g9=randn(1,time); % attitude change matrix cbn(1,1)=cos(zong)*cos(hang)+sin(zong)*sin(heng)*sin(hang); cbn(1,2)=-cos(zong)*sin(hang)+sin(zong)*sin(heng)*cos(hang); cbn(1,3)=-sin(zong)*cos(heng); cbn(2,1)= cos(heng)*sin(hang); cbn(2,2)=cos(heng)*cos(hang); cbn(2,3)=sin(heng); cbn(3,1)= sin(zong)*cos(hang)-cos(zong)*sin(heng)*sin(hang); cbn(3,2)=-sin(zong)*sin(hang)-cos(zong)*sin(heng)*cos(hang); cbn(3,3)=cos(zong)*cos(heng); F(1,4)=1/R; F(2,3)=1/(R*cos(fai)); %F(3,1)=2*omega*vx*cos(fai)+vx*vy*sec(fai)^2/R; F(3,1)=2*omega*vy*cos(fai)+vx*vy*sec(fai)^2/R; %F(3,3)=vx*tan(fai)/R; F(3,3)=vy*tan(fai)/R; F(3,4)=vx*tan(fai)/R+2*omega*sin(fai); F(3,6)=-g; %F(4,1)=-(2*omega*vx*cos(fai)+vx^2*sec(fai)^2/R); F(4,1)=-(2*omega*vx*sin(fai)+vx^2*sec(fai)^2/R); F(4,3)=-2*(vx*tan(fai)/R+omega*sin(fai)); F(4,5)=g; %F(4,7)=-g; F(5,4)=-1/R; F(5,6)=omega*sin(fai)+vx*tan(fai)/R; F(5,7)=-(omega*cos(fai)+vx/R); F(5,8)=1; F(6,1)=-omega*sin(fai); %F(6,3)=-1/R; F(6,3)=1/R; F(6,5)=-(omega*sin(fai)+vx*tan(fai)/R); %F(6,7)=-vx/R; F(6,7)=-vy/R; F(6,9)=1; F(7,1)=omega*cos(fai)+vx*sec(fai)^2/R; F(7,3)=tan(fai)/R; F(7,5)=omega*cos(fai)+vx/R; %F(7,6)=vx/R; F(7,6)=vy/R; F(7,10)=1; F(8,8)=-gyrotime; F(9,9)=-gyrotime; F(10,10)=-gyrotime; F(3,11)=cbn(1,1); F(3,12)=cbn(1,2); F(3,13)=cbn(1,3); F(4,11)=cbn(2,1); F(4,12)=cbn(2,2); F(4,13)=cbn(2,3); F(5,8)=cbn(1,1); F(5,9)=cbn(1,2); F(5,10)=cbn(1,3); F(6,8)=cbn(2,1); F(6,9)=cbn(2,2); F(6,10)=cbn(2,3); F(7,8)=cbn(3,1); F(7,9)=cbn(3,2); F(7,10)=cbn(3,3); F(11,11)=-atime; F F(16,16)=0; G=[0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0; 1,0,0,0,0,0,0,0,0; 0,1,0,0,0,0,0,0,0; 0,0,1,0,0,0,0,0,0; 0,0,0,1,0,0,0,0,0; 0,0,0,0,1,0,0,0,0; 0,0,0,0,0,1,0,0,0; 0,0,0,0,0,0,1,0,0; 0,0,0,0,0,0,0,1,0; 0,0,0,0,0,0,0,0,1]; [A,B]=c2d(F,G,T); for i=1:time w(1,1)=gyronoise*g1(1,i); w(2,1)=gyronoise*g2(1,i); w(3,1)=gyronoise*g3(1,i); w(4,1)=(0.5*g*1e-5)*g4(1,i); w(5,1)=(0.5*g*1e-5)*g5(1,i); w(6,1)=(0.5*g*1e-5)*g6(1,i); w(7,1)=0.005*g7(1,i); w(8,1)=1/600*pi/180*g8(1,i); w(9,1)=0.0001*g9(1,i); xx=A*xx+B*w/T^2; sins1(1,i)=xx(1,1); sins1(2,i)=xx(2,1); sins1(3,i)=xx(3,1); sins1(4,i)=xx(4,1); sins1(5,i)=xx(5,1); sins1(6,i)=xx(6,1); sins1(7,i)=xx(7,1); s1(i)=xx(1,1)/pi*180*60; fai0=30*pi/180; lamda0=30*pi/180; vxe0=0.01; vye0=0.01; faie0=2*pi/(180*60); lamdae0=2*pi/(180*60); afae0=3*pi/(180*60); beitae0=3*pi/(180*60); gamae0=5*pi/(180*60); hxjz=pi/4; vx=20*1842/3600*sin(hxjz); vy=20*1842/3600*cos(hxjz); %vx=0; %vy=0; fe=0; fn=0; fu=g; % attitude change matrix zong=0*pi/180; heng=0*pi/180; hang=45*pi/180; cbn(1,1)=cos(zong)*cos(hang)+sin(zong)*sin(heng)*sin(hang); cbn(1,2)=-cos(zong)*sin(hang)+sin(zong)*sin(heng)*cos(hang); cbn(1,3)=-sin(zong)*cos(heng); cbn(2,1)= cos(heng)*sin(hang); cbn(2,2)=cos(heng)*cos(hang); cbn(2,3)=sin(heng); cbn(3,1)= sin(zong)*cos(hang)-cos(zong)*sin(heng)*sin(hang); cbn(3,2)=-sin(zong)*sin(hang)-cos(zong)*sin(heng)*cos(hang); cbn(3,3)=cos(zong)*cos(heng); % gpstime=1/600; weichagps=25;%GPS位置误差 suchagps=0.05;%GPS速度误差 gyroe0=(0.01/3600)*pi/180; gyrotime=1/7200;%陀螺漂移反向相关时间 atime=1/1800; gyronoise=(0.01/3600)/180*pi;%陀螺漂移白噪声 tcm2time=1/300; tcm2noise=0.04*pi/(60*180); afatcm2=6*pi/(180*60); betatcm2=6*pi/(180*60); gamatcm2=6*pi/(180*60); %matrix of system equation fai=fai0; lamada=lamda0; F(22,22)=0; F(1,4)=1/R; F(2,1)=vx*tan(fai)*sec(fai)/R; F(2,3)=sec(fai)/R; F(3,1)=2*omega*vx*cos(fai)+vx*vy*sec(fai)^2/R; F(3,3)=vx*tan(fai)/R; F(3,4)=vx*tan(fai)/R+2*omega*sin(fai); F(3,6)=-fu; F(3,7)=fn; F(4,1)=-(2*omega*vx*cos(fai)+vx^2*sec(fai)^2/R); F(4,3)=-2*(vx*tan(fai)/R+omega*sin(fai)); F(4,5)=fu; F(4,7)=-fe; F(5,4)=-1/R; F(5,6)=omega*sin(fai)+vx*tan(fai)/R; F(5,7)=-(omega*cos(fai)+vx/R); %F(5,8)=1; F(6,1)=-omega*sin(fai); F(6,3)=1/R; F(6,5)=-(omega*sin(fai)+vx*tan(fai)/R); F(6,7)=-vx/R; %F(6,9)=1; F(7,1)=omega*cos(fai)+vx*sec(fai)^2/R; F(7,3)=tan(fai)/R; F(7,5)=omega*cos(fai)+vx/R; F(7,6)=vx/R; %F(7,10)=1; F(5,8)=cbn(1,1); F(5,9)=cbn(1,2); F(5,10)=cbn(1,3); F(5,11)=cbn(1,1); F(5,12)=cbn(1,2); Q=[2*gyronoise^2/7200,0,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,2*gyronoise^2/7200,0,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,2*gyronoise^2/7200,0,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,gyronoise^2,0,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,gyronoise^2,0,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,gyronoise^2,0,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,2*5*g*1e-5/1800,0,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,2*5*g*1e-5/1800,0,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,2*(25/R)^2/600,0,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,2*(25/R)^2/600,0,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,2*0.05^2/600,0,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,2*0.05^2/600,0,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,2*tcm2noise^2/300,0,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,2*tcm2noise^2/300,0; 0,0,0,0,0,0,0,0,0,0,0,0,0,0,2*tcm2noise^2/300]; Q1=1/yinzi1*Q; Q2=1/yinzi2*Q; r=[(weichagps/R)^2,0,0,0,0,0,0; 0,(weichagps/R)^2,0,0,0,0,0; 0 , 0,suchagps^2,0,0,0,0; 0, 0, 0, suchagps^2,0,0,0; 0,0,0,0,tcm2noise^2,0,0; 0,0,0,0,0,tcm2noise^2,0; 0,0,0,0,0,0,tcm2noise^2]; r1=[(weichagps/R)^2,0,0,0; 0,(weichagps/R)^2,0,0; 0 , 0,suchagps^2,0; 0, 0, 0, suchagps^2]; r2=[tcm2noise^2,0,0; 0,tcm2noise^2,0; 0,0,tcm2noise^2]; %discrete manage [A,B]=c2d(F,G,T); r1=r1/T; r2=r2/T; Q1=Q1/T; Q2=Q2/T; %initial value p=[faie0^2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; figure(1); subplot(3,2,1) plot(t,sg1,'b:') grid xlabel('time(h)') ylabel('纬度误差估计(角分)') subplot(3,2,2) plot(t,ss1,'b:') grid xlabel('time(h)') ylabel('误差的残差曲线(角分)') subplot(3,2,3) plot(t,sg2 ,'b:') grid xlabel('time(h)') ylabel('经度误差估计(角分)') subplot(3,2,4) plot(t,ss2 ,'b:') grid xlabel('time(h)') ylabel('误差的残差曲线(角分)') set(gcf,'color',[1 1 1]) figure(2); subplot(3,2,1) plot(t,sg3,'b:') grid xlabel('time(h)') ylabel('东向速度误差估计(kn)') subplot(3,2,2) plot(t,ss3,'b:') grid xlabel('time(h)') ylabel('误差的残差曲线(kn)') subplot(3,2,3) plot(t,sg4 ,'b:') grid xlabel('time(h)') ylabel('北向速度误差估计(kn)') subplot(3,2,4) plot(t,ss4 ,'b:') grid xlabel('time(h)') ylabel('误差的残差曲线(kn)') set(gcf,'color',[1 1 1]) figure(3); subplot(3,2,1) plot(t,sg5,'b:') grid xlabel('time(h)') ylabel('纵摇角误差估计(角分)') subplot(3,2,2) plot(t,ss5,'b:') grid xlabel('time(h)') ylabel('误差的残差曲线(角分)') subplot(3,2,3) plot(t,sg6 ,'b:') grid xlabel('time(h)') ylabel('横摇角误差估计(角分)') subplot(3,2,4) plot(t,ss6 ,'b:') grid xlabel('time(h)') ylabel('误差的残差曲线(角分)') subplot(3,2,5) plot(t,sg7 ,'b:') grid xlabel('time(h)') ylabel('首向角误差估计(角分)') subplot(3,2,6) plot(t,ss7 ,'b:') grid xlabel('time(h)') ylabel('误差的残差曲线(角分)') set(gcf,'color',[1 1 1])
1 matlab版本
2014a
2 参考文献
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