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steepest descent algorithms are,
用函数表示为:
function g = grad_ml(X,x,r,sigma2)% ML gradient computation% --------------------------------% g = grad_ml(X,x,r);% g = gradient vector % X = matrix for receiver positions% x = 2D position estimate% r = TOA measurement vector% sigma2 = noise variance vector%L = size(X,2); % number of receiverst1 = 0;t2 = 0;ds = sum((x*ones(1,L)-X).^2,1);ds = ds';for i=1:L t1 = t1 + (1/sigma2(i))*(r(i)-ds(i)^(0.5))*(x(1)-X(1,i))/ds(i)^(0.5); t2 = t2 + (1/sigma2(i))*(r(i)-ds(i)^(0.5))*(x(2)-X(2,i))/ds(i)^(0.5);endg=-2.*[t1; t2];
首先还是先给出该方法的定位示意图 :
下面分析均方根误差rmse:
废话不必多说,这个方法在信噪比为30dB时候的RMSE为223m和其他方法类似。
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