ICCP算法是一种用于重力匹配导航的算法，请给出一段ICCP算法可以执行的matlab代码

ICCP算法是一种基于迭代最近点算法（ICP）的增强算法，用于解决多传感器数据融合的问题。以下是ICCP算法的Matlab实现示例：

function [R,t] = ICCP(X,Y,R0,t0)
% X and Y are the input point clouds
% R0 and t0 are the initial guess of the rotation and translation
% R and t are the final estimated rotation and translation
% ICCP algorithm
for i = 1:10 % maximum number of iterations 10
    % Find the corresponding points in Y for each point in X
    [idx,dist] = knnsearch(Y,X*R0+t0,'K',1);
    % Calculate the weights
    w = dist.^(-2);
    % Estimate the rotation and translation
    [R,t] = weighted_ICP(X,Y,idx,w);
    % Update the guess
    R0 = R0*R;
    t0 = t0+R0'*t;
end
end

function [R,t] = weighted_ICP(X,Y,idx,w)
% X and Y are the input point clouds
% idx contains the corresponding points in Y for each point in X
% w contains the weights
% R and t are the estimated rotation and translation
% Weighted ICP algorithm
for i = 1:10 % maximum number of iterations 10
    % Find the corresponding points in Y for each point in X
    Yk = Y(idx,:);
    % Calculate the centroid of X and Yk
    mX = mean(X);
    mYk = mean(Yk);
    % Calculate the centered coordinates
    Xc = X-repmat(mX,size(X,1),1);
    Ykc = Yk-repmat(mYk,size(Yk,1),1);
    % Calculate the weighted covariance matrix
    C = Xc'*diag(w)*Ykc;
    % Perform singular value decomposition
    [U,~,V] = svd(C);
    % Calculate the rotation matrix R
    R = V*U';
    % Calculate the translation vector t
    t = mYk'-R*mX';
    % Update the guess
    X = X*R+t';
end
end

这段代码实现了ICCP算法的主要步骤，包括计算权重、估计旋转和平移等。需要注意的是，这只是ICCP算法的一个示例实现，实际应用中可能需要根据具体的问题做一些修改和调整。