glvs; nn = 2;ts = 0.1; nts = nn*ts;% 子样数和采样时间 att0 = [0; 0; 30]*arcdeg; qnb0 = a2qua(att0); vn0 = [0;0;0]; pos0 = [34*arcdeg; 108*arcdeg; 100]; qnb = qnb0; vn = vn0; pos = pos0;% 姿态、速度和位置初始化 eth = earth(pos, vn); wm = qmulv(qconj(qnb),eth.wnie)*ts: vm = qmulv(qconj(qnb),-eth.gn)*ts wm = repmat(wm', nn, 1); vm = repmat(vm', nn, 1); % 仿真静态IMU数据 phi = [0.1; 0.2; 3]*arcmin: qnb = qaddphi(qnb, phi) % 失准角 eb =[0.01;0.015;0.02]*dph; web = [0.001;0.001;0.001]*dpsh; % 陀螺常值零偏，角度随机游走 系数 db = [80;90;100]*ug; wdb = [1;1;1]*ugpsHz; % 加速度计常值偏值，速度随机游走系数 Qk = diag([web; wdb; zeros(9,1)])/2*nts; rk = [[0.1;0.1;0.1];[[10;10]/Re;10]] Rk = diag(rk)/2; "zv([6n*[001:001:001] 'udpx[L'0:L'0:L'0] :[ol:ad/[0l:0l]] :[L:L:L] :Bapoyex[0L:L'0:L'0]])be!p = 0d Hk = [zeros(6,3),eye(6),zeros(6)] kf = kfinit(Qk, Rk, P0, zeros(15), Hk); % kf滤波器初始化 len = fix(3600/ts) % 仿真时长

这段代码是一个惯性导航系统的仿真代码。它使用了CSDN开发的一些函数和库来进行模拟。主要包括以下几个部分：

1. 初始化部分：设置了子样数和采样时间，初始的姿态、速度和位置。还初始化了一些常数和参数。

2. 仿真IMU数据部分：根据姿态和地球参数，生成了静态IMU数据。

3. 失准角部分：添加了一些失准角。

4. 零偏和随机游走部分：设置了陀螺零偏和加速度计常值偏值，并给它们添加了一些随机游走的效果。

5. 噪声部分：设置了测量噪声的参数。

6. KF滤波器初始化部分：根据设置的噪声参数，初始化了一个KF滤波器。

7. 仿真时长部分：根据采样时间计算了仿真的时长。

以上是对代码的简要介绍，具体的功能和实现细节可能需要进一步了解代码的上下文和具体实现。

相关问题

% Ballistic missile trajectory & SINS/GPS simulation glvs ts = 0.01; %% trajectory simulation % stage 1: 1/4 circle T = 60; t = (0:ts:T)'; afadot2 = 2*pi/2/T^2; afa = 1/2*afadot2*t.^2; r = 60000; y = r-r*cos(afa); z = r*sin(afa); pitch = pi/2-afa; tt = t; % stage 2: leveling constant acceleration v = (y(end)-y(end-1))/ts; T = 120; t = (ts:ts:T)'; a = -2; y = [y; y(end)+v*t+1/2*a*t.^2]; z = [z; z(end)+t*0]; pitch = [pitch; t*0]; tt = [tt; tt(end)+t]; % stage 3: leveling constant velocity v = (y(end)-y(end-1))/ts; T = 120; t = (ts:ts:T)'; a = -0; y = [y; y(end)+v*t+1/2*a*t.^2]; z = [z; z(end)+t*0]; pitch = [pitch; t*0]; tt = [tt; tt(end)+t]; myfig, plot(y, z, '-'); xygo('front/m','up/m'); %% imu,avp,gps dxyz = [y*0,y,z,tt]; dxyz(:,1:3) = ar1filt(dxyz(:,1:3), 100); pos = dxyz2pos(dxyz); ap = [pitch, zeros(length(pos),2), pos]; [imu, avp0, avp] = ap2imu(ap); imuplot(imu); gps = gpssimu(avp(1:100:end, :), 0.1, 10, 1,0,0, 0); %% sins/gps imuerr = imuerrset(0.15, 100, 0.001, 10); davp = avperrset([1;1;3]*3, [1;1;1], [1;1;3]*100); imu1 = imuadderr(imu,imuerr); ins = insinit(avpadderr(avp0,davp), ts); ins.nts=ts; avp1 = sinsgps(imu1, gps, ins, davp, imuerr); avpcmpplot(avp, avp1(:,[1:9,end]), 'phi');帮我注释代码

以下是对代码的注释：

% Ballistic missile trajectory & SINS/GPS simulation
glvs ts = 0.01; % 设置时间步长

%% 轨迹模拟
% 阶段1：1/4圆形轨迹
T = 60; % 转弯时间
t = (0:ts:T)'; % 时间向量
afadot2 = 2*pi/2/T^2; % 角速度
afa = 1/2*afadot2*t.^2; % 角度
r = 60000; % 距离
y = r-r*cos(afa); % y坐标
z = r*sin(afa); % z坐标
pitch = pi/2-afa; % pitch角
tt = t;

% 阶段2：匀加速直线运动
v = (y(end)-y(end-1))/ts; % 初始速度
T = 120; % 时间
t = (ts:ts:T)'; % 时间向量
a = -2; % 加速度
y = [y; y(end)+v*t+1/2*a*t.^2]; % y坐标
z = [z; z(end)+t*0]; % z坐标
pitch = [pitch; t*0]; % pitch角
tt = [tt; tt(end)+t];

% 阶段3：匀速直线运动
v = (y(end)-y(end-1))/ts; % 初始速度
T = 120; % 时间
t = (ts:ts:T)'; % 时间向量
a = -0; % 加速度
y = [y; y(end)+v*t+1/2*a*t.^2]; % y坐标
z = [z; z(end)+t*0]; % z坐标
pitch = [pitch; t*0]; % pitch角
tt = [tt; tt(end)+t];

% 绘制轨迹图
myfig, plot(y, z, '-');
xygo('front/m','up/m');

%% IMU、AVP和GPS
% 计算位置
dxyz = [y*0,y,z,tt];
dxyz(:,1:3) = ar1filt(dxyz(:,1:3), 100);
pos = dxyz2pos(dxyz);

% 计算姿态
ap = [pitch, zeros(length(pos),2), pos];
[imu, avp0, avp] = ap2imu(ap);

% 绘制IMU图
imuplot(imu);

% 生成GPS数据
gps = gpssimu(avp(1:100:end, :), 0.1, 10, 1,0,0, 0);

%% SINS/GPS
% 设置IMU误差和姿态误差
imuerr = imuerrset(0.15, 100, 0.001, 10);
davp = avperrset([1;1;3]*3, [1;1;1], [1;1;3]*100);

% 添加IMU误差
imu1 = imuadderr(imu,imuerr);

% 初始化INS
ins = insinit(avpadderr(avp0,davp), ts);
ins.nts=ts;

% 进行SINS/GPS解算
avp1 = sinsgps(imu1, gps, ins, davp, imuerr);

% 绘制姿态角度对比曲线
avpcmpplot(avp, avp1(:,[1:9,end]), 'phi');

% Error Distribution Method and Analysis of Observability Degree Based on the Covariances in Kalman Filter. % Copyright(c) 2009-2021, by Gongmin Yan, All rights reserved. % Northwestern Polytechnical University, Xi An, P.R.China % 2/03/2018, 16/05/2021 glvs avp0 = avpset([0;0;0], [30;108;380]); % init AVP ts = 0.1; T = 600; %% twopos = 2; % =1 for single-position; =2 for two-position alignment if twopos==1 imu = imustatic(avp0, ts, T); elseif twopos==2 xxx = []; seg = trjsegment(xxx, 'init', 0); seg = trjsegment(seg, 'uniform', T/2-10); seg = trjsegment(seg, 'turnleft', 20, 9); seg = trjsegment(seg, 'uniform', T/2-10); trj = trjsimu(avp0, seg.wat, ts, 1); imu = trj.imu; end %% phi = [.1;.1;1]*glv.deg; imuerr = imuerrset(0.03, 100, 0.001, 10); wvn = [0.01;0.01;0.01]; [att0v, attkv, xkpk, kfs] = alignvn_kfs(imuadderr(imu,imuerr), qaddphi(a2qua(avp0(1:3)),phi), avp0(7:9), phi, imuerr, wvn); myfig; % Observability plot spk = sqrt(xkpk(:,13:end-1)); t = xkpk(:,end); for k=1:12, spk(:,k)=spk(1,k)./spk(:,k); end subplot(221), semilogy(t, spk(:,1:3),'linewidth',2); title('( a )'); xygo('Observibility'); legend('\phi_E', '\phi_N', '\phi_U') subplot(222), semilogy(t, spk(:,4:6),'linewidth',2); title('( b )'); xygo('Observibility'); legend('\deltav^n_E', '\deltav^n_N', '\deltav^n_U') subplot(223), semilogy(t, spk(:,7:9),'linewidth',2); title('( c )'); xygo('Observibility'); legend('\epsilon^b_x', '\epsilon^b_y', '\epsilon^b_z') subplot(224), semilogy(t, spk(:,10:12),'linewidth',2); title('( d )'); xygo('Observibility'); legend('\nabla^b_x', '\nabla^b_y', '\nabla^b_z')帮我注释代码

这段代码展示了一个基于卡尔曼滤波器的姿态对准方法，并通过观测度分析评估系统的可观测性。下面是代码的解释：

1. 初始化一个位置和速度:

avp0 = avpset([0;0;0], [30;108;380]); % 初始位置和速度

2. 设定时间步长和时间:

ts = 0.1; % 时间步长
T = 600; % 时间长度

3. 选择单位置或双位置对准方法:

twopos = 2; % =1 for single-position; =2 for two-position alignment
if twopos==1
    imu = imustatic(avp0, ts, T);
elseif twopos==2
    xxx = [];
    seg = trjsegment(xxx, 'init', 0);
    seg = trjsegment(seg, 'uniform', T/2-10);
    seg = trjsegment(seg, 'turnleft', 20, 9);
    seg = trjsegment(seg, 'uniform', T/2-10);
    trj = trjsimu(avp0, seg.wat, ts, 1);
    imu = trj.imu;
end

4. 设定IMU误差参数和陀螺仪漂移参数:

phi = [.1;.1;1]*glv.deg; % 陀螺仪漂移
imuerr = imuerrset(0.03, 100, 0.001, 10); % IMU误差
wvn = [0.01;0.01;0.01]; % 观测噪声

5. 使用卡尔曼滤波器进行姿态对准:

[att0v, attkv, xkpk, kfs] = alignvn_kfs(imuadderr(imu,imuerr), qaddphi(a2qua(avp0(1:3)),phi), avp0(7:9), phi, imuerr, wvn);

6. 绘制可观测度图:

myfig; % 创建新的绘图窗口
spk = sqrt(xkpk(:,13:end-1)); % 提取状态协方差矩阵
t = xkpk(:,end); % 提取时间向量
for k=1:12, spk(:,k)=spk(1,k)./spk(:,k); end % 计算可观测度
subplot(221), semilogy(t, spk(:,1:3),'linewidth',2); title('( a )'); xygo('Observibility'); legend('\phi_E', '\phi_N', '\phi_U')
subplot(222), semilogy(t, spk(:,4:6),'linewidth',2); title('( b )'); xygo('Observibility'); legend('\deltav^n_E', '\deltav^n_N', '\deltav^n_U')
subplot(223), semilogy(t, spk(:,7:9),'linewidth',2); title('( c )'); xygo('Observibility'); legend('\epsilon^b_x', '\epsilon^b_y', '\epsilon^b_z')
subplot(224), semilogy(t, spk(:,10:12),'linewidth',2); title('( d )'); xygo('Observibility'); legend('\nabla^b_x', '\nabla^b_y', '\nabla^b_z')

这段代码的作用是评估系统在不同状态下的可观测性，即系统是否能够根据观测值准确地估计状态变量。最终，代码绘制了四个子图，分别表示系统的姿态、速度、陀螺仪漂移和加速度计误差在不同时间点的可观测度。