clear clc close all load('data1.mat'); nn = size(imuPosX,1); %% lightHouse坐标系转换 x = lightHousePosX * 100; y = -lightHousePosZ * 100; % 定义误差函数,即均方根误差 errorFunction = @(params) sqrt(mean((x(1:500) * cos(params(1)) - y(1:500) * sin(params(1)) + params(2) - imuPosX(1:500)).^2 + (x(1:500) * sin(params(1)) + y(1:500) * cos(params(1)) + params(3) - imuPosY(1:500)).^2)); % 使用 fminsearch 优化误差函数,找到使误差最小的旋转角和位移 initialGuess = [0, 10, 10]; % 初始猜测值,[旋转角, 位移] optimizedParams = fminsearch(errorFunction, initialGuess); % 输出优化后的旋转角和位移 rotationAngle = optimizedParams(1); xOffset = optimizedParams(2); yOffset = optimizedParams(3); % 旋转坐标 xt = x * cos(rotationAngle) - y * sin(rotationAngle) + xOffset; yt = x * sin(rotationAngle) + y * cos(rotationAngle) + yOffset; %% tagN = 4; %标签坐标 XN(:,1)=[-300;-300]; XN(:,2)=[-300;300]; XN(:,3)=[300;300]; XN(:,4)=[300;-300]; sim2=6; Q=diag(repmat(sim2,1,2*tagN));%协方差矩阵 measure_AOA = zeros(4,nn); measure_d = zeros(4,nn); measure_AOA(1,:) = aoa1'; measure_AOA(2,:) = aoa2'; measure_AOA(3,:) = aoa3'; measure_AOA(4,:) = aoa4'; measure_d(1,:) = d1'; measure_d(2,:) = d2'; measure_d(3,:) = d3'; measure_d(4,:) = d4'; %% uwb解算 x_uwb(1) = 0; y_uwb(1) = 0; theta_uwb=zeros(nn,1); for i=2:nn if measure_d(1,i) == 0 x_uwb(i) = x_uwb(i-1); y_uwb(i) = y_uwb(i-1); continue; end [t1,theta] = WLS(XN,measure_AOA(:,i),measure_d(:,i),Q); x_uwb(i) = t1(1); y_uwb(i) = t1(2); theta_uwb(i) = 90-theta; theta_uwb(i) = mod(theta_uwb(i)+180,360)-180; derr_WLS(i)=norm(t1-[xt(i);yt(i)]); end thetat=zeros(nn,1); for i=2:nn detx=xt(i)-xt(i-1); dety=yt(i)-yt(i-1); thetat(i)=atan2d(detx,dety); end % Uwb.x=x_uwb; % Uwb.y=y_uwb; % Uwb.alpha=theta_uwb; % 角速度校准 detW = mean(wZ(1:200)); wZ = wZ - detW; x_imu(1) = 0; y_imu(1) = 0; Z=zeros(3,1); WW = 0.05; theta_imu(1) = 0; %% EKF KK = zeros(5,3); %% %%AEKF R = diag([1 1 1]); % qq = 50; % qq = 10; qq = 0.01; Q = diag([qq qq qq qq qq]); P0 = diag([0 0 0 0 0]); H = [1 0 0 0 0; 0 1 0 0 0; 0 0 0 1 0]; X_aekf = zeros(5,nn); X_aekf(:,1) = [imuPosX(1);imuPosY(1);vXY(1)*100;0;wZ(1)]; I = eye(5); JF = zeros(5,5); X_pre = zeros(5,nn); alfa = 0.97; windowlength=5; x_aekf_sw=zeros(1,nn); y_aekf_sw=zeros(1,nn); for i=2:nn detImuTime = imuDataRxTime(i) - imuDataRxTime(i-1); detOdomTime = odomDataRxTime(i)-odomDataRxTime(i-1); w = wZ(i); v = vXY(i)*100; detV=(vXY(i)-vXY(i-1))*100; detD = v*detImuTime; detTheta = w*180/pi*detImuTime; detW = w-wZ(i-1); theta_imu(i) = theta_imu(i-1)+detTheta; theta_imu(i) = mod(theta_imu(i)+180,360)-180; F = [1 0 detImuTime*cosd(X_aekf(4,i-1)) 0 0; 0 1 detImuTime*sind(X_aekf(4,i-1)) 0 0; 0 0 0 0 0; 0 0 0 0 0; 0 0 0 0 0;]; if w15 % xxx = 1; % end end errAEKf_sw=zeros(1,nn); weightedvector=[0.3;0.25;0.2;0.15;0.1]; for i=1:nn if i>windowlength-1 x_win=X_aekf(1,i-windowlength+1:i); y_win=X_aekf(2,i-windowlength+1:i); x_aekf_sw(i)=x_win*weightedvector; y_aekf_sw(i)=y_win*weightedvector; else x_aekf_sw(i)=X_aekf(1,i); y_aekf_sw(i)=X_aekf(2,i); end errAEKf_sw(i) = norm([x_aekf_sw(i);y_aekf_sw(i)]-[xt(i);yt(i)]); end figure; plot(errAEKf_sw); figure; plot(x_imu,y_imu,xt,yt); mean(errAEKf_sw)