Grey_predition_algorithm.m

                            %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                            %   GREY PREDICTION ALGORITHM   %
                            %           for lOCALIZATION    %
                            %       of MOBILE USER          %
                            %             By                %
                            %           P.Bachan            %
                            %       NIT Kurukshetra         %
                            %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tic
clc
clear all
clf
d0=rand;
n=4;
s=300;
w1=0.5;w2=0.5
mrss=-10*n*log10(d0);
a=0.5;b=6;
d=a+(b-a)*rand(1,s);
d=sort(d);
for i=1:s
    RSS_0(i)=mrss-10*n*log10(d(i)/d0)-20;
end
for i=1:s
    rss_0(i)=10^(-RSS_0(i)/10);
end
sum=0;
for i=1:s
    sum=sum+rss_0(i);
    rss_1(i)=sum;
end
for i=1:s-1
    B(i,1)=-1/2.*(rss_1(i)+rss_1(i+1));
end
B(:,2)=1;
yn=(rss_0(2:s))';
v=inv(B'*B)*B'*yn;
a=v(1);
b=v(2);
for i=1:s
    prss_1(i)=rss_1(i);
    prss_1(i+1)=(rss_0(i)-b/a)*exp(-a*s)+(b/a);
    prss_0(i)=rss_0(i);
    prss_0(i+1)=prss_1(i+1)-prss_1(i);
end
PRSS_0(1:s)=-10*log10(prss_0(2:s+1));
for i=1:s
    prss_2(i)=w1*rss_0(i)+w2*prss_0(i+1);
end
PRSS_2=-10*log10(prss_2);

%results and plots
fprintf('observed and predicted values of the RSSIs in dB are %g\n')
disp([RSS_0' PRSS_0' PRSS_2'])
rs_0(1:s)=awgn(RSS_0(1:s),d0-10);
prs_0(1:s)=awgn(PRSS_0(1:s),d0-10);
prs_2=awgn(PRSS_2,d0-10);
plot(d(1:s),rs_0(1:s),d(1:s),prs_0(1:s))
xlabel('distance in meters')
ylabel('signal strength in dB')
legend('Original RSSI','Predicted RSSI')
grid on
figure
plot(d(1:s),rs_0(1:s),d(1:s),prs_2(1:s))
xlabel('distance in meters')
ylabel('signal strength in dB')
legend('Original RSSI','Weight Predicted RSSI')
grid on
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