IMMKF.rar_TDOA粒子滤波_kf 均衡_卫星测距_基于TDOA的定位_无线信道估计

function object = setMPGPara(object, fc, BSPosition, MSPosition, BSHeight,... MSHeight, RoadsWidth, RoadOrientation, BuildingHeight, BuildingSeparation) % 计算MPG的幅度、延时、相位、入射角、多普勒频移 % Reference: % [1]Comparison and Implementation of the Directional Geometric-Stochastic % Based Channel Model % [2]The COST259 Directional Channel Model-Part I: Overview and % Methodology % [3]The COST 259 Directinal Channel Model-PartII: Macrocells % [4]A Stochastic Model of the Temporal and Azimuthal Dispersion Seen % at the Base Station in Outdoor Propagation Environments % -t_add分布：[1]eq(10)，laplacian分布 % Note: % (1)下行信道，电磁波方向从BS到MS（上行信道到达角方向不同） % (2)由于没有关于BS处角度扩散的信息，每个MPG的DOD与cluster中心一致 % (3)没有对DOD，DOA的仰角做具体的建模，因此仰角信息有可能不准确 % (4)对于LOS路径损耗建模为自由空间的路径损耗，没有考虑转移函数[3] % 输入参数： % object = 类的对象 % fc = 载波频率 % BSPosition = 基站位置坐标，参考坐标系 % MSPosition = 移动台位置坐标，参考坐标系 % BSHeight = 基站天线高度 % MSHeight = 移动台天线高度 % RoadsWidth = 道路宽度，单位m % RoadOrientation = 道路方向(针对于直射路径),单位度,[0, 90] % BuildingHeight = 建筑物平均高度,单位m % BuildingSeparation = 建筑物间距，单位m % 输出参数： % object = 类的对象 % 作者：金乐（SEU，2007.10.14） % ************************************************************************* % 配置全局变量(GPs) c = 3*10^8; MPG_NUM_PER_CLUSTER = 6; % 每个cluster的MPG数目 G = [1 -0.75 -0.75; -0.75 1 0.5; -0.75 0.5 1]; % 时延扩散，角度扩散，阴影衰落[1]eq(4) Sshf = 6; % shadow fading, [3]Table IX Ss_psi = 3; % elevation spread, [3]Table IX L_E = 8; % 参考文献中未给出该参数，暂设为此值 epsilon = 0.5; % delay spread, [3]Table IX Ss_tau = 3; % delay spread, [3]Table IX switch object.REType case 1 % GTU m_s_psi = 10*pi/180; % elevation spread, [3]Table IX m_s_tau = 0.4; % delay spread, [3]Table IX, 单位us r_DS = 1.7; r_AS = 1.3; case 2 % GRA m_s_psi = 5*pi/180; m_s_tau = 0.1; r_DS = 1.4; r_AS = 1.2; case 3 % GBU m_s_psi = 10*pi/180; m_s_tau = 0.4; r_DS = 1.7; r_AS = 1.3; case 4 % GHT m_s_psi = 5*pi/180; m_s_tau = 0.1; r_DS = 1.4; r_AS = 1.2; end % ************************************************************************* ClusterNum = length(object.ActiveFC)+1; % far cluster + near cluster MPGNum = MPG_NUM_PER_CLUSTER*ClusterNum; object.MPGPara = zeros(MPGNum+1, 7); % 第一行存放LOS的参数，若LOS不存在，则为0 IndexFC = object.ActiveFC; % 计算cluster处的距离，角度, 延时 s_CL = zeros(ClusterNum, 1); % cluster center处的路径长度，单位m s_MS_CL = zeros(ClusterNum, 1); % cluster center与BS间的距离，单位m s_BS_CL = zeros(ClusterNum, 1); % cluster center与MS间的距离，单位m theta_BS_CL = zeros(ClusterNum, 1); % BS与cluster center的水平角(downlink), 单位弧度 psi_BS_CL = zeros(ClusterNum, 1); % BS与cluster center的仰角(downlink)，单位弧度 theta_MS_CL = zeros(ClusterNum, 1); % MS与cluster center的水平角(downlink)，单位弧度 psi_MS_CL = zeros(ClusterNum, 1); % MS与cluster center的仰角(downlink)，单位弧度 % near cluster [psi_BS_CL(1) theta_BS_CL(1) s_CL(1)] = cart2sph(MSPosition(1)-BSPosition(1),... MSPosition(2)-BSPosition(2), MSPosition(3)-BSPosition(3)); s_MS_CL(1) = s_CL(1); theta_MS_CL(1) = theta_BS_CL(1); psi_MS_CL(1) = psi_BS_CL(1); % far cluster if ClusterNum > 1 [psi_BS_CL(2:end) theta_BS_CL(2:end) s_BS_CL(2:end)] =... cart2sph(object.FCPosition(IndexFC, 1)-BSPosition(1),... object.FCPosition(IndexFC, 2)-BSPosition(2),... object.FCPosition(IndexFC, 3)-BSPosition(3)); [psi_MS_CL(2:end) theta_MS_CL(2:end) s_MS_CL(2:end)] =... cart2sph(MSPosition(1)-object.FCPosition(IndexFC, 1),... MSPosition(2)-object.FCPosition(IndexFC, 2),... MSPosition(3)-object.FCPosition(IndexFC, 3)); s_CL(2:end) = s_BS_CL(2:end) + s_MS_CL(2:end); end tau_CL = s_CL / c * 10^6; % cluster center处的延时, 单位为us % 计算LOS参数 K0 = 0; % Ricean衰落因子 if object.LOSBeing == 1 % 计算LOS的path gain，建模为自由空间的路径损耗.[3]eq(11) PathGain = (c/(4*pi*s_CL(1)*fc))^2; % 计算LOS的PDDP，即因子K0，服从lognormal分布。标准差为6，均值由[3]eq(28)给出 K0 = 10^(((26-L_E)/6 + 6*randn)/10); PDDP = K0/(1+K0); object.MPGPara(1, :) = [PathGain tau_CL(1) theta_BS_CL(1)... psi_BS_CL(1) theta_MS_CL(1) psi_MS_CL(1) PDDP]; else object.MPGPara(1, 2) = tau_CL(1); end % 计算各个cluster的时延扩散，角度扩散，阴影衰落因子 % -CorrRandn = [shadowing azimuth_spread delay_spread; % | | | ; % | | | ] C = chol(G); CorrRandn = randn(ClusterNum, 3) * C; S = 10.^(0.1*Sshf*CorrRandn(:, 1)); %shadowing gain, [3]eq(24), 每一例对应一个cluster sigma_psi = m_s_psi * 10.^(0.1*Ss_psi*CorrRandn(:, 2)); % azimuth spread, [3]eq(25) sigma_tau = m_s_tau * (s_CL/1000).^epsilon .* 10.^(0.1*Ss_tau*CorrRandn(:, 3)); % delay spread, [3]eq(26) % 计算各个cluster的路径增益；大尺度衰落和阴影衰落 PathGain = setPathGain(object.REType, fc, s_CL, s_MS_CL, S, BSHeight,... MSHeight, RoadsWidth, RoadOrientation, BuildingHeight, BuildingSeparation); object.MPGPara(2:end, 1) = resample(PathGain, MPG_NUM_PER_CLUSTER); % 为每个cluster的MPG产生延时, 超量延时服从指数分布 sigma_tau = resample(sigma_tau, MPG_NUM_PER_CLUSTER); sigma_Delay = r_DS * sigma_tau; tau_MPG = exprnd(sigma_Delay); object.MPGPara(2:end, 2) = tau_MPG + resample(tau_CL, MPG_NUM_PER_CLUSTER); % 为每个cluster的MPG产生DOD，DOA， 仅对DOA的水平角进行建模，服从高斯分布 sigma_psi = resample(sigma_psi, MPG_NUM_PER_CLUSTER); sigma_AS = r_AS * sigma_psi; psi_MPG = sigma_AS .* randn(MPGNum, 1); object.MPGPara(2:end, 3) = resample(theta_BS_CL, MPG_NUM_PER_CLUSTER); object.MPGPara(2:end, 4) = resample(psi_BS_CL, MPG_NUM_PER_CLUSTER); object.MPGPara(2:end, 5) = resample(theta_MS_CL, MPG_NUM_PER_CLUSTER); object.MPGPara(2:end, 6) = resample(psi_BS_CL, MPG_NUM_PER_CLUSTER) + psi_MPG; % 为每个cluster的MPG产生功率延时角度分布PDDP PDDP = 1 ./ sigma_tau .* exp(-tau_MPG./sigma_tau) .*... 1 ./ (sqrt(2)*sigma_psi) .* exp(-sqrt(2)*abs(psi_MPG)./sigma_psi); % 对各个cluster的PDDP归一化 for i = 1:6:MPGNum PDDP(i:i+5) = PDDP(i:i+5)/sum(PDDP(i:i+5)); end PDDP(1:6) = 1 / (1+K0) * PDDP(1:6); % 对near cluster可能存在LOS，需要修正 object.MPGPara(2:end, 7) = PDDP; %************************************************************************** %************ 子 函 数 ********** %************************************************************************** function PathGain = setPathGain(REType, fc, s_CL, s_MS_CL, S, BSHeight,... MSHeight, RoadsWidth, RoadOrientation, BuildingHeight,... BuildingSeparation) % 计算路径增益：大尺度衰落和阴影衰落 % [3]eq(11) switch REType case 1 % GTU Rc = 100; Lc = 20; case 2 % GRA Rc = 300; Lc = 20; case 3 % GBU Rc = 100; Lc = 20; case 4 % GHT Rc = 300; Lc = 20; end c = 3*10^8; PathGain = zeros(length(s_CL), 1); tau = s_CL / c * 10^6; % 计算P_NLOS switch REType case {1 3} % COST-231-Walfisch-Ikegami模型 L_FS = 32.45 + 20*log10(fc/10^6) + 20*log10(s_CL/1000); if RoadOrientation < 35 % RoadOrientation范围[0 90]; Lori = -10 + 0.354*RoadOrientation; elseif RoadOrientation < 55 Lori = 2.5 + 0.075*(RoadOrientation-35); else Lori = 4.0 - 0.114*(RoadOrientation-55); end Lrts = -16.9 - 10*log10(RoadsWi