📄 calcerror.m
字号:
%|
%| FUNTION: calcError
%|
%| PURPOSE: Calculate the sum squared error between model and the data
%| when measurements are RSS (with log-normal errors).
%|
%| ASSUMPTIONS:
%| 1. that the blind (and the reference) device coordinates are put in
%| vectors which look like this:
%| [x1, x2, ... xn, y1, y2, ..., yn];
%|
%| 2. Assumes the dhat matrix is lower triangular.
%|
%| AUTHOR: Neal Patwari
%| http://www.engin.umich.edu/~npatwari/
%|
function [sumError] = calcError(guessBlindLocs)
global refDevices; % number of reference devices
global blindDevices; % number of blind devices
global totalDevices; % the total number of devices
global linearRefLocs; % locations of the reference devices
global dhat; % estimated distance between devices based on the measured
% received power.
global funcEvals; % counter for number of function evaluations.
funcEvals = funcEvals + 1;
TINY = 1e-5;
x = [linearRefLocs(1:refDevices), guessBlindLocs(1:blindDevices)];
y = [linearRefLocs(refDevices+1:2*refDevices), ...
guessBlindLocs(blindDevices+1:2*blindDevices)];
%| 1. For each blind device, add in the error that is calculated between
%| itself and each other blind or reference device.
sumError = 0;
for i = refDevices+1 : totalDevices,
j = 1:i-1;
sumError = sumError + sum( log( max(TINY, ((x(i)-x(j)).^2 + (y(i)-y(j)).^2)) ...
./ (dhat(i,j).^2) ).^2);
end
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