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TCHSLAM
Commit 13959d59 authored by Sensing's avatar Sensing
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delete some files

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AP Clustering/apclusterSparse.m deleted 100644 → 0
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main.m deleted 100644 → 0
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ITER = 100000;
for i = 10001:ITER
% time = datetime;
% save(['time',num2str(i),'.mat'],'time');
softmodem(i);
end
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softmodem.m deleted 100644 → 0
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function [] = softmodem(i)
save('i.mat','i');
clc;
clear all;
warning off;
global M;
global filename;
global VSP;
global VSS;
global VSB;
global VSA;
i = importdata('i.mat');
T = 200;
step = 2;
M_idx = 6;
data_file = ['all_data',num2str(i)];
mkdir(data_file);
while M_idx <= 6
i = importdata('i.mat');
% if i == 1 && M == 1
% M = M + 3;
% end
% init_const_para(M);
i = importdata('i.mat');
M_vec = importdata('M_vec.mat');
M = M_vec(M_idx);
fprintf('[ NUMBER ]the number of vehicle is %d\n',M);
filename = ['all_data',num2str(i),'/data_vehicle_number=',num2str(M)];
new_folder = filename;
mkdir(new_folder);
fprintf('[ M O D E ]vehicle number is %d, cluster\n',M);
top_file;
% save('M\M.mat','M');
% clear all;
% global M;
% M = importdata('M\M.mat');
% init_const_para(M);
% filename = ['all_data',num2str(i),'/data_without_cluster_vehicle_number=',num2str(M)];
% new_folder = filename;
% mkdir(new_folder);
% fprintf('[ M O D E ]vehicle number is %d, no-cluster\n',M);
% if M == 1
% copyfile('all_data\data_vehicle_number=1\*' ,'all_data\data_without_cluster_vehicle_number=1');
% else
% top_file_without_cluster;
% end
% % data_p(M,T,step);
save('M\M_idx.mat','M_idx');
clear all;
global M;
M_idx = importdata('M\M_idx.mat');
M_vec = importdata('M_vec.mat');
M = M_vec(M_idx);
M_idx = M_idx + 1;
end
end
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% clear;
% clear state;
% clear ob;
% clear PF;
% clear obj;
% clear cluster;
%%%用观测去采样粒子！！！------learning approach
global state;
global ob;
global PF;
global obj;
global cluster;
global path_track;
global VSP;
global VSS;
global VSB;
global VSA;
global M;
global filename;
global reflector;
global vehicle_vec;
global recorder;
reflector.N_ref = 0;
PI = 3.1415926;
T_RECLUSTER = 10;
t_del = 15;
if M == 1
t_del = 100;
end
%% remained to be done
%%%1. 要与没有聚类的结果进行对比%%%注释掉cluster_update，重新跑一遍即可
%%%3. T_duration和加速度，加速度的方差需要调整
len_road=150;
len_veh_trace=100;
lane_wedith = 4;
len_ref_x=12;%15;
dis_ref_x=6;%7.5;
y_line=35;%40;%35;
building_height=15;%20;
bs_height=8;
theta_building.theta1 = PI/6;
theta_building.theta2 = PI/9;
theta_building.theta3 = PI/12;
% theta1_building=PI/4;
% theta2_building=PI/6;
% theta3_building=PI/12;
veh_speed = 10;
veh_acce_speed_straight = 9.8*0.5;
veh_angle_acce_speed = 0.8;
ref_step = 0.5;
edge_limit = 0.5;
limit_N = 200;
limit_percentage = 0.1;
batch_limit = 10;
lr = 5e-5;
ITER = 100;
% limit_dist = 8;
svt_combine_limit = 10;
del_limit = 20;
particle_limit = 120;
PF_step = 10;
N_batch = 1;
overlap = 1;
p_min_veh = 4;%exp(-4.25^2);
p_min_svt = 7;%exp(-7^2);
if M == 1
p_min_veh = 10;%exp(-4.25^2);
p_min_svt = 10;%exp(-7^2);
end
%% vehicle parameters
derta_d_super= 0.1*pow2(-1*(M>1));%0.2*pow2(-floor(0.34*log2(M)));%0.1,0.25**********************************vehicle
L_d_super = 6;%40*******************************************vehicle
k=6;%%%%2*pi*derta_d_super/k
%% svt parameters
sigma_vh.rvh = 0.5;%*********************************************SVT
derta_d_svt = 0.125*power(1.25,-1*(M>8));%******************************************SVT
L_d_svt = 2;%**********************************************SVT
derta_d_svt_upd = 0.15;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%svt_upd
derta_theta_upd = pi/4;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%svt_upd
L_d_upd = 4;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%svt_upd
%% const para
%%%% max iteration of time
T = 300;
T_duration = 0.1;%%0.5
%%%% number of vehicle
% M = 5;
% %%% VSP: vehicle_start_position---> M*2
% VSP = [ 0,0;8,5;16,-5;24,5;32,5 ];
% %%% VSS: vehicle start speed---> M*2
% VSS = [ 17,0;16,0;15,0;14,0;13,0 ];
% %%% VSB:vehicle start bias----> M*1
% VSB = [ 0;0;0;0;0 ];
% VSA = [ 0,0;0,0;0,0;0,0;0,0 ];
%%% the minimum weight that two reflector is combineed
limit_combine = 0.6;
%%%% the weight of the direction of the estimated reflector points
%%% hight for autenna in vehicle
H_v = 1;
%%% hight for building
H_b = 10;
%%% hight for base sta,tion
% H_bs = 15;
%%% the number of objects
% obj_N = 400;
%%% std for observation of TOA and AOA
sigma_d = 2.62;%%%3************************************************TOA
sigma_theta = 0.036;%%%0.01*****************************************AOA
sigma_theta_init = 0.01;
sigma_a = 0;%2*0.001*9.8/3;%%2
sigma_a_vec = 0;%0.5/(180*3)*pi;%*********************************************a
sigma_v = 0.1;
sigma_v_vec = 0.1*T_duration;%************************************************v
sigma_center = 4;
sigma_a_init = 1;
sigma_vh.dvh = 0.03;
sigma_gps = 3;
LAMDA = (sigma_d/sigma_theta)^2;
%%% block size for distance(for Xvt_ja)
derta_d = 0.1;%%0.5
%%%%the distance resolution of the reflector line
derta_d_reflector = 0.1;
beta = 0.95;
%%% block size for distance(for Xu_j)
%%% block size for and angle(for Xvt_j)
derta_theta = 10*pi/180;
%%% block size for speed(for Xu_j)
derta_v_super= 0.2;
%%% block size for bias(for Xu_j
derta_b_super= 5e-12;
%%% number of particle size in distance domain(for Xu_j)
L_d = 8;%8
L_d_cluster = 10;
%%% number of particle size in speed domain(for Xu_j)
L_v = 7;
%%% number of particle size in bias domain(for Xu_j)
L_b = 1;
%%% the number of particles for super PF
N_beta = 6;%6
N_beta_super = 18;%36********************************************vehicle
N_beta_cluster= 9;
N_super = L_d * L_v * L_b * N_beta * N_beta;
%%% K times of AOA estimation std to decide the number of particles(for
%%% Xvt_ja)
K = 2;%3
%%% time duration for one time slot
%%% the max number of particles(for Xvt_ja) in one block
N_max = 100;
%%% sigma_vt : the std of the noise added to the particles of virtual
%%% transmitters, with
%%%% sigma_vt.Rvt : the std for position
%%%% sigma_vt.Dvt : the std for addentional distance
% sigma_vt.Rvt = 3;
% sigma_vt.Dvt = 2;
sigma_u = sigma_v*T_duration*sqrt(2/pi);
step = 2;
rita = 6;
gama = 2;
N_select = ceil((gama/10)*L_d_super*N_beta_super);
%%% init the map
% init_map(T,T_duration,sigma_a_init,sigma_center,M,obj_N,VSP,VSS,VSA);
% filename = 'data';
% obj = importdata('obj.mat');
%%% generate the objects
%%%end
%% dynamic input
%%% veh_para: the parameters of vehicle including
%%% vehicle[0...M].MultiPath[0...N].d
%%% vehicle[0...M].Multipath[0...N].theta
%%% vehicle[0...M].Multipath[0...N].id //ascending order with the id
%%% u is a motion with
%%%% u.a accelerate speed
%%%% u.b bias
%%% end
%% main loop for estimation
%%% initialize the bias,speed and positions for vehicle
MAP_generation(M,len_road,len_veh_trace,len_ref_x,dis_ref_x,y_line,building_height,bs_height,theta_building,T_duration,veh_speed,veh_acce_speed_straight,veh_angle_acce_speed,lane_wedith,T)
obj_N = length(obj);
arrived = 1:1:M;
arrived_pre = [];
%%% initialize the position, speed and bias for state 1.
for t = 1: T
fprintf('\nvehicle = %d, cluster\nFor time slot T = %d*%.1f(second)\n',M,t,T_duration);
initialize(t,arrived,sigma_gps,sigma_v,sigma_v_vec,sigma_a,sigma_a_vec);%%% for new vehicle
[veh_para] = generate_para(t,M,obj_N,T_duration,sigma_d,sigma_theta,sigma_a,sigma_v);%%% the observation for state 1
%%% generate the parameters for multipath and the motion information.
%%% update the observation
% para_update(t,M,veh_para,sigma_d,sigma_theta);%start from time 1
%%% give the multipath parameter to observation.
motion_update(t,M,T_duration,sigma_a,sigma_a_vec,sigma_v,sigma_v_vec,arrived);
% if ~isempty(arrived)
% %%% init the particles of super_PF
% veh_PF_init(t,M,arrived);
% % init_super_PF(M,N_super,derta_d_super,derta_v_super,derta_b_super,L_d,L_v,L_b,N_beta); % for time 1 only
% %%% generate the super particles in blocks
% end
veh_PF_update(t,M,T_duration,L_d_super,derta_d_super,k,arrived,particle_limit);
%%% update the super_PF using the motion information u.
VTCI_update_del(t,M,derta_d_svt,derta_theta,K,sigma_theta_init,beta,sigma_vh,arrived);
% VTCH_estimate(t,M,sigma_d,sigma_theta,rita,derta_d,L_d,N_beta,arrived);
% vehicle_state_estimation(t,M,gama,arrived);
isChanged = VTCI_update_add(t,M,L_d_upd,derta_d_svt,derta_theta_upd,particle_limit);
% svt_cluster_judge(t,M,svt_combine_limit,particle_limit,del_limit,arrived);
svt_cluster_judge(t,M,svt_combine_limit,particle_limit,del_limit,T_RECLUSTER,t_del,L_d_upd,derta_d_svt,derta_theta_upd)
Analysis(t);
% if ~isempty(arrived)
% svt_re_drawing(t,L_d_upd,derta_d_svt_upd,derta_theta_upd,particle_limit);
% end
Coupled_PF(t,M,sigma_d,sigma_theta,rita,arrived,N_batch,overlap,sigma_u,gama,p_min_veh,p_min_svt,sigma_vh);
% state(t).vehicle(1).Xu.Ru
Analysis(t);
% cluster_update(t,M,N_beta,derta_d,particle_limit,limit_dist,sigma_vh,derta_d_reflector,limit_combine,ref_step,edge_limit,limit_N,limit_percentage,batch_limit,lr,LAMDA,ITER,sigma_d);
% reflector_building(t,sigma_d,sigma_vh,limit_percentage,limit_N,derta_d_reflector,limit_combine,step,edge_limit,batch_limit,lr,LAMDA,ITER);
% svt_re_drawing(t,L_d_upd,derta_d_svt_upd,derta_theta_upd,particle_limit);
update_path_track();
update_state(t,M,T_duration);
line_detection(t,sigma_d);
arrived_pre = arrived;
arrived = [];
% arrived = vehicle_arrived(t,M,veh_speed,len_road,len_veh_trace,T_duration);
if mod(t,step)==1 || t == T
clean_data(t,T,step,filename);
%%% clean the
end
fprintf('[REFLECTOR] THE NUMBER OF REFLECTOR IS %d...\n',reflector.N_ref);
end
% reflector_estimate(T);
obj_name = [filename,'\obj'];
save(obj_name,'obj');
ob_name = [filename,'\ob'];
save(ob_name,'ob');
recorder_name = [filename,'\recorder'];
save(recorder_name,'recorder');
% show_result(T_duration,M,T);
%%%end
%%%1. batch 应该shaffule一下
%%%2. 应该更新一下svt的位置
%%%3. 应该更多得体现随机性以提高精度
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% clear;
% clear state;
% clear ob;
% clear PF;
% clear obj;
% clear cluster;
global state;
global ob;
global PF;
global obj;
global cluster;
global VSP;
global VSS;
global VSB;
global VSA;
global M;
global filename;
%% remained to be done
%%%1. 要与没有聚类的结果进行对比%%%注释掉cluster_update，重新跑一遍即可
%%%3. T_duration和加速度，加速度的方差需要调整
%% const para
%%%% max iteration of time
T = 100;
% %%%% number of vehicle
% M = 5;
% %%% VSP: vehicle_start_position---> M*2
% VSP = [ 0,0;8,5;16,-5;24,5;32,5 ];
% %%% VSS: vehicle start speed---> M*2
% VSS = [ 17,0;16,0;15,0;14,0;13,0 ];
% %%% VSB:vehicle start bias----> M*1
% VSB = [ 0;0;0;0;0 ];
% VSA = [ 0,0;0,0;0,0;0,0;0,0 ];
%%% hight for autenna in vehicle
H_v = 1;
%%% hight for building
H_b = 10;
%%% hight for base station
H_bs = 15;
%%% the number of objects
obj_N = 400;
%%% std for observation of TOA and AOA
sigma_d = 2;%%%1
sigma_theta = 0.01;
sigma_theta_init = 0.01;
sigma_a = 0.5;%%2
sigma_v = 0.1;
sigma_center = 4;
sigma_a_init = 1;
sigma_vh.rvh = 0.2;
sigma_vh.dvh = 0;
%%% block size for distance(for Xvt_ja)
derta_d = 0.5;%%0.1
beta = 0.95;
%%% block size for distance(for Xu_j)
derta_d_super= 0.1;
%%% block size for and angle(for Xvt_j)
derta_theta = 0.01;
%%% block size for speed(for Xu_j)
derta_v_super= 0.2;
%%% block size for bias(for Xu_j)
derta_b_super= 5e-12;
%%% number of particle size in distance domain(for Xu_j)
L_d = 8;
L_d_super = 40;
L_d_cluster = 10;
%%% number of particle size in speed domain(for Xu_j)
L_v = 7;
%%% number of particle size in bias domain(for Xu_j)
L_b = 1;
%%% the number of particles for super PF
N_beta = 6;%18
N_beta_super = 36;
N_beta_cluster= 9;
N_super = L_d * L_v * L_b * N_beta * N_beta;
%%% K times of AOA estimation std to decide the number of particles(for
%%% Xvt_ja)
K = 3;%3
%%% time duration for one time slot
T_duration = 0.2;%%0.5
%%% the max number of particles(for Xvt_ja) in one block
N_max = 100;
%%% sigma_vt : the std of the noise added to the particles of virtual
%%% transmitters, with
%%%% sigma_vt.Rvt : the std for position
%%%% sigma_vt.Dvt : the std for addentional distance
% sigma_vt.Rvt = 3;
% sigma_vt.Dvt = 2;
sigma_u = 1;
step = 2;
rita =2;
%%% init the map
init_map(T,T_duration,sigma_a_init,sigma_center,M,obj_N,VSP,VSS,VSA);
% filename = 'data_without_cluster';
% obj = importdata('obj.mat');
%%% generate the objects
%%%end
%% dynamic input
%%% veh_para: the parameters of vehicle including
%%% vehicle[0...M].MultiPath[0...N].d
%%% vehicle[0...M].Multipath[0...N].theta
%%% vehicle[0...M].Multipath[0...N].id //ascending order with the id
%%% u is a motion with
%%%% u.a accelerate speed
%%%% u.b bias
%%% end
%% main loop for estimation
%%% initialize the bias,speed and positions for vehicle
initialize( T,M,VSP,VSS,VSB);%%% for state 1
%%% initialize the position, speed and bias for state 1.
for t = 1: T
fprintf('\nvehicle = %d, no-cluster\nFor time slot T = %d*%.1f(second)\n',M,t,T_duration);
[veh_para] = generate_para(t,M,obj_N,T_duration,sigma_d,sigma_theta,sigma_a,sigma_v);%%% the observation for state 1
%%% generate the parameters for multipath and the motion information.
%%% update the observation
% para_update(t,M,veh_para,sigma_d,sigma_theta);%start from time 1
%%% give the multipath parameter to observation.
if t == 1
%%% init the particles of super_PF
veh_PF_init(M);
% init_super_PF(M,N_super,derta_d_super,derta_v_super,derta_b_super,L_d,L_v,L_b,N_beta); % for time 1 only
%%% generate the super particles in blocks
else
veh_PF_update(t,M,T_duration,L_d_super,derta_d_super,N_beta_super,sigma_u);
%%% update the super_PF using the motion information u.
end
isChanged = VTCI_update_del(t,M,derta_d,derta_theta,K,sigma_theta_init,beta,sigma_vh);
% if t>1
% cluster(t).VH(1).N_VH
% end
VTCH_estimate(t,M,sigma_d,sigma_theta,rita,derta_d,L_d,N_beta);
vehicle_state_estimation(t,M);
isChanged = VTCI_update_add(t,M,derta_d,derta_theta,K,sigma_theta,beta);
motion_update(t,M,T_duration,sigma_a,sigma_v);
% cluster_update(t,M,N_beta,derta_d,L_d);
%%% RB-particle_filter
% RBPF(t,M,T_duration,derta_d,derta_theta,K,u,N_max,sigma_vt,beta);
%%% MMSE to calculate the final state of vehicle
% mmse(t,M);
% noise_PF(t,M,sigma_vt,sigma_u);
update_state(t,M,T_duration);
if mod(t,step)==1 || t == T
clean_data(t,step,filename);
%%% clean the
end
end
obj_name = [filename,'\obj'];
save(obj_name,'obj');
ob_name = [filename,'\ob'];
save(ob_name,'ob');
% show_result(T_duration,M,T);
%%%end
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