rssi1.m

无线传感器中的DV-HOp算法仿真

% BorderLength-----正方形区域的边长，单位：m
% NodeAmount-------网络节点的个数
% BeaconAmount---信标节点数
% Sxy--------------用于存储节点的序号，横坐标，纵坐标的矩阵
%Beacon----------信标节点坐标矩阵;BeaconAmount*BeaconAmount
%UN-------------未知节点坐标矩阵;2*UNAmount
% Distance------未知节点到信标节点距离矩阵;2*BeaconAmount
%h---------------节点间初始跳数矩阵
%X---------------节点估计坐标初始矩阵,X=[x,y]'
% R------------------节点的通信距离，一般为10-100m

clear,close all;
BorderLength=100;
NodeAmount=30;
BeaconAmount=10;
UNAmount=NodeAmount-BeaconAmount;
R=50;
% D=zeros(BeaconAmount,UNAmount);%未知节点到信标节点距离初始矩阵； 
h=zeros(NodeAmount,NodeAmount);%初始跳数为0； 
X=zeros(2,UNAmount);%节点估计坐标初始矩阵
Sxy=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30;18.699,71.203,47.9631,28.7532,26.2458,91.709,1.3445,69.8641,59.3771,31.6689,0.8422,64.9868,76.881,71.4797,23.7565,26.3214,97.76,54.5924,80.2098,67.0983,97.0472,81.7469,30.6349,35.8017,48.7667,67.3834,22.1579,6.8247,20.7657,63.8221;99.1305,87.1365,49.6005,6.0941,18.6261,12.3279,36.9691,88.9347,15.6691,23.3395,39.6904,8.5001,96.9702,78.1962,19.5731,71.3785,63.7122,84.8057,66.8306,82.0644,48.6925,64.1571,66.0932,93.8199,9.099,51.488,72.5009,96.4124,16.1118,0.0228];
Beacon=[Sxy(2,1:BeaconAmount);Sxy(3,1:BeaconAmount)];%信标节点坐标
UN=[Sxy(2,(BeaconAmount+1):NodeAmount);Sxy(3,(BeaconAmount+1):NodeAmount)];%未知节点坐标 
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~初始化节点间距离 ~~~~~~~~~~~~~~~~~~~~~~
for i=1:NodeAmount
    for j=1:NodeAmount
        Dall(i,j)=((Sxy(2,i)-Sxy(2,j))^2+(Sxy(3,i)-Sxy(3,j))^2)^0.5;%所有节点间相互距离
    end
end
D2=Dall(1:BeaconAmount,(BeaconAmount+1):NodeAmount); %BeaconAmount行UNAmount列
D2
 
% ~~~~~~~~~~~~~~~~~~~~~利用RSSI求的距离~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a=7;
for i=1:BeaconAmount
    for j=1:UNAmount
        RR(i,j)=Distance(D2(i,j),a);%使用Distance函数利用RSSI模型求得距离
    end
end
RR
%~~~~~~~~~~~~~~~~~~~~~~~~~~~加权质心算法~~~~~~~~~~~~~~~~~~~~~~

for j=1:UNAmount
    sumx=0;
    sumy=0;
    sumw=0;
    for i=1:BeaconAmount
        if RR(i,j)<=R
            sumx=sumx+Beacon(1,i)/RR(i,j);
            sumy=sumy+Beacon(2,i)/RR(i,j);
            sumw=sumw+1/RR(i,j);
        end
    end     ;
    X(1,j)=sumx./sumw  ; %x的坐标平均值
    X(2,j)=sumy./sumw;  %y的坐标平均值
    error(1,j)=sqrt((UN(1,j)-X(1,j))^2+(UN(2,j)-X(2,j))^2);     
end
figure:plot(error,'-o');
   xlabel('未知节点数')
   ylabel('未知节点与定位误差值')
   title('每个未知节点的误差')
X
error=sum(error)/UNAmount
Accuracy=error/R