标签:blog ar io color os sp for on 数据
clear all; close all; clc; % warning off all; T_step = 1; Q_delte = 1e-4; %8e1;%1e-4; Q_deturn = 1e-10; %1e0;%1e-10; fai_theta = 0.5*pi/180; %0.5*pi/180; fai_r = 10; %10; Np = 300; Lamdanoise = 4e-4; %30;%10;%15;%4*1e-4; no_run = 1; % 蒙特卡洛次数 D = 5; %T = T_step; T0 = 100; x1_true = zeros(D,T0); x2_true = zeros(D,T0); x1_true(:,1) = [0;5;0;-2;0]; % 第一帧时刻的 位置 和 速度 信息 x2_true(:,1) = [530;1;600;-5;0]; F1 = [ 1 T_step 0 0; 0 1 0 0; 0 0 1 T_step; 0 0 0 1 ]; %% 真实轨迹 for t = 2:100 x1_true(1:4,t) = F1 * x1_true(1:4,t-1); end for t = 2:100 x2_true(1:4,t) = F1 * x2_true(1:4,t-1); end X = cat(3,x1_true,x2_true); % a=cat(3,A,B) 左括号后的3表示构造出的矩阵维数 合成 figure(‘color‘,‘w‘); plot(x1_true(1,:),x1_true(3,:),‘g-o‘,x2_true(1,:),x2_true(3,:),‘r-o‘); T = 100; D = 6; Ps = 0.99; % 存在概率 Pd = 1.00; % N2 = Np/2; Nb = 30; % 新生粒子数 Nt = 2; % 目标个数 X_o = -5000; Y_o = -5000; A1 = [T_step^3/3 T_step^2/2; T_step^2/2 T_step ]; F1 = [1 T_step;0 1]; F_const = blkdiag(F1,F1,1); % 状态转移矩阵,多加了一维幅度 Q_const = blkdiag(A1,A1,0)*Q_delte; % Q_turn = blkdiag(A1*Q_delte,A1*Q_delte,T_step*Q_deturn); Matrxi_T = [0.9 0.1;0.1 0.9]; Q = cat( 3,sqrtm(Q_const),sqrtm(Q_turn) ); zabopdf = Lamdanoise/(10000*pi); % 杂波 % Measurement Model h1 = inline(‘sqrt((x(1,:)-X_o).^2+(x(D-3,:)-Y_o).^2) ‘,‘x‘,‘D‘,‘X_o‘,‘Y_o‘); h2 = inline(‘ atan((x(D-3,:)-Y_o)./(x(1,:)-X_o)) ‘,‘x‘,‘D‘,‘X_o‘,‘Y_o‘); R = diag([fai_r fai_theta]); True_Num = zeros(1,T); for i = 1:10 True_Num(i) = 2; end for i = 11:90 True_Num(i) = 2; end for i = 91:100 True_Num(i) = 2; end % Probability Model概率模型 Pe = inline(‘1/( sqrt(2*pi)*deta )*exp( -0.5*x.^2/deta^2 )‘,‘x‘,‘deta‘); Pe_AP = inline(‘1/( sqrt(2*pi)^D*sqrt(det(deta)) )*exp( -0.5*ctranspose(x)/deta*x )‘,‘x‘,‘deta‘,‘D‘); %新生目标高斯和 % Jrk = 2; %wr = zeros(1,2); %wr(1) = 0.01; wr(2) = 0.01; %mr = zeros(5,2); %mr(:,1) = x1_true(:,1); %mr(2,1) = 0;mr(4,1) = 0;mr(5,1) = 0; %mr(:,2) = x2_true(:,1); % Pr = diag([100^2,5^2,100^2,5^2,0.001^2]‘); %pai_new = [0.5,0.5]; %新生目标对应两个模型的概率 V_max = 5; time_PPHD_D = zeros(no_run,T); % 存储时间矩阵 for ll = 1 : no_run rand(‘state‘,sum(100*clock)); randn(‘state‘,sum(100*clock)); %% 第一帧初始化 Num_noise = poissrnd(Lamdanoise); X0 = x1_true(:,1); %假设已知第一时刻的位置??? M = size( X0,2 ); % 初始目标数,目标数从列数可以看出 ZD_ini = [ feval(h1,X0,D,X_o,Y_o);feval(h2,X0,D,X_o,Y_o) ] + R*randn(2,M); % R是噪声协方差。初始时刻的观测量,这里分别是目标到观测站的距离和角度 ZD_noise = [25000*rand(1,Num_noise);pi/2*rand(1,Num_noise)]; % 第一帧的虚警 ZD_old = [0;0]; InitialParticle1 = x1_true(:,1)*ones(1,Np) + Q(:,:,1)*randn(D-1,Np); % 目标1 的粒子 5*300 Q(:,:,1)=sqrtm(Q_const) InitialParticle2 = x2_true(:,1)*ones(1,Np) + Q(:,:,2)*randn(D-1,Np); % 目标2 的粒子 5*300 Q(:,:,2)=sqrtm(Q_turn) PHD_D = [ InitialParticle1,InitialParticle2 ]; % 所有初始化 粒子 5*600 PHD_D = cat(1,PHD_D,[1*ones(1,Np),2*ones(1,Np)]); % 按列 5*600 + 1*600=6*600 w = 1/Np*ones(1,2*Np); % 1*600 %% 第二帧到最后 for t = 2 : T Num_noise = poissrnd(Lamdanoise); ZD_noise = [25000*rand(1,Num_noise);pi/2*rand(1,Num_noise)]; % 以后每一帧产生的随机虚警噪声 X0 = squeeze( X(:,t,:) ); % 除去size为1的维度 5*2 M = size( X0,2 ); % M=2 ZD = [ feval(h1,X0,D,X_o,Y_o);feval(h2,X0,D,X_o,Y_o) ] + R*randn(2,M); ZD = cat( 2,ZD,ZD_noise ); %2*2 tic; A = [1 T_step;0 1] ; F_const = blkdiag(A,A,1); %状态转移矩阵 R1 = R; %diag([10 0.5*pi/180]); M = size(ZD_old,2); PHD_brith_Pre = zeros(D,Nb*M); % 新生预测 for m = 1 : M x = ZD_old(1,m)*cos( ZD_old(2,m) ) + X_o; %上一时刻的观测量,距离和角度信息 y = ZD_old(1,m)*sin( ZD_old(2,m) ) + Y_o; var_x = (ZD_old(1,m)*R(2,2)*sin(ZD_old(2,m)))^2 + (R(1,1)*cos(ZD_old(2,m)))^2; var_y = (ZD_old(1,m)*R(2,2)*cos(ZD_old(2,m)))^2 + (R(1,1)*sin(ZD_old(2,m)))^2; cov_xy = (R(1,1)^2 - (ZD_old(1,m)*R(2,2))^2)*sin(ZD_old(2,m))*cos(ZD_old(2,m)); R_Z = [var_x,cov_xy;cov_xy,var_y]; Begin = (m-1)*Nb + 1; End = m * Nb; Rej_S = [x;y]*ones(1,Nb) + sqrtm(R_Z)*randn(2,Nb); % 状态,坐标 Rej_V = 2*V_max*randn(2,Nb) - V_max; % 速度 Rej_w = zeros(1,Nb); Rej_Model = zeros(1,Nb); % 权重 for n = 1 : Nb % 选择目标运动模型 if( 0.5 < rand ) Rej_w(n) = 0; Rej_Model(n) = 1; else Rej_w(n) = rand/25-0.02; Rej_Model(n) = 2; end end PHD_brith_Pre(:,Begin:End) = [Rej_S(1,:);Rej_V(1,:);Rej_S(2,:);Rej_V(2,:);Rej_w;Rej_Model]; end N_brith = size( PHD_brith_Pre,2 ); w_brith = 0.01/N_brith*ones(1,N_brith); % 新生粒子的权重服从均匀分布,0.01就是新生目标的PHD PHD_extend = [ PHD_D,PHD_brith_Pre ]; % 两部分的粒子, w_extend = [ Ps*w,w_brith ]; % (1*600)两部分的权重,继续存在和新生粒子权重预测。按照公式(29) N_extend = length(w_extend); % 权重的维数(600) L_Q = size( Q,1 ); % 行数 PHD_Pre = zeros(D,N_extend); % PHD 预测 PHD就是离散化的粒子 %% 预测 for n = 1 : N_extend % Prediction of particle state预测 PHD_Pre(1:D-1,n) = F_const*PHD_extend(1:D-1,n) + sqrt(Q(:,:,1))*randn(L_Q,1); PHD_Pre(end,n) = 1; %最后一行给1 end %% 更新 Z_Pre = [ feval(h1,PHD_Pre,D,X_o,Y_o);feval(h2,PHD_Pre,D,X_o,Y_o) ]; %2*600,一步预测的预测和角度信息 M = size( ZD,2 ); % 2,列数 w = zeros(M+1,N_extend); w(M+1,:) = (1-Pd)*w_extend; for m = 1 : M pian = Z_Pre - ZD(:,m)*ones(1,N_extend); % 预测数据和测量数据的偏差 lik = feval(Pe,pian(1,:),R(1,1)).*feval(Pe,pian(2,:),R(2,2)); % 量测似然函数 w_lik = Pd*w_extend.*lik; % 分子 w(m,:)= w_lik./( zabopdf + sum(w_lik) ); % 权重更新 end num = sum(w,2); N = round( num*Np ); %% Resampling Processing PHD_D = []; Count_PPHD = []; B = 1; Num = 0; for m = 1:M if (N(m) > 0) % [P_mid,w_pre,index] = Resample_P_PHD(PHD_Pre,w(m,:),N(m)); w_in = w(m,:);P_in = PHD_Pre;N_re = N(m); q = cumsum( w_in./sum(w_in) ); l = 1; L = size( w_in,2 ); k = 1; hp = zeros(1,L); % Pick the Particles for n = 1 : N_re u = rand/N_re + (n-1)/N_re; while ( q(k) < u ) k = k + 1; end if ( l == k ) hp(l) = hp(l) + 1; else in = find( max(w_in(l+1:k)) == w_in(l+1:k) ); in1 = in(1) + l; hp(in1)= hp(in1) + 1; l = k; end end index1 = find( hp ~= 0 ); P_mid = P_in( :,index1 ); w_pre = w_in( index1 ); index = hp( index1 ); Num = Num + sum(w_pre); L = length(w_pre); Rej1 = []; L1 = sum(index); for n = 1:L Rej0 = repmat(P_mid(1:5,n),1,index(n)) + Q(:,:,P_mid(6,n))*randn(5,index(n)); Rej1 = cat(2,Rej1,[Rej0;P_mid(6,n)*ones(1,index(n))]); end PHD_D = cat(2,PHD_D,Rej1); Count_PPHD = cat(2,Count_PPHD,[B;B+L1-1]); B = B + L1; end end B = size(PHD_D,2); w = Num/B*ones(1,B); ZD_old = ZD; % Estimating the state of target L = round( sum(w) );%对sum求和,然后四舍五入,L为目标的个数 if( L > 0 ) Num_PHD = Count_PPHD(2,:) - Count_PPHD(1,:); PHD_S_D = zeros(D-1,L); for n = 1:L N_max = max(Num_PHD); index = find(N_max == Num_PHD); index_PPHD = Count_PPHD(1,index(1)):Count_PPHD(2,index(1)); PHD_S_D(:,n) = mean(PHD_D(1:end-1,index_PPHD),2); Num_PHD(index(1)) = 0; end else PHD_S_D = []; end time_PPHD_D(ll,t) = toc; figure(1); hold on; for abc = 1:size(PHD_S_D,2) plot(PHD_S_D(1,abc),PHD_S_D(3,abc),‘b-+‘); end end end fprintf(‘Time of P-PHD-Div:%fs\n‘,mean( mean(time_PPHD_D) )*T);
标签:blog ar io color os sp for on 数据
原文地址:http://www.cnblogs.com/longxingxiu/p/4167143.html