✅作者简介：热爱科研的Matlab仿真开发者，修心和技术同步精进，matlab项目合作可私信。
🍎个人主页：Matlab科研工作室
🍊个人信条：格物致知。
更多Matlab仿真内容点击👇
⛄ 内容介绍
多目标跟踪(MTT)技术作为多源信息融合领域内最重要的技术之一,已经在民用和军事领域中被广泛应用.随机有限集理论为多目标跟踪问题提供了一种工程友好型的数学工具。详细介绍了RFS理论框架下的三种近似最优贝叶斯滤波器:概率假设密度(PHD)、势PHD(CPHD)和多目标多伯努利(Me MBer)滤波器、cbmember过滤器、广义标记多贝努利滤波器、“lmb”标记的多贝努利滤波器、“jointglmb”广义标记多贝努利滤波器、“jointlmb”标记的多贝努利滤波器,并对多者的研究进展进行了详细描述和对比。每个滤波器下分别实现线性高斯模型的“gms”高斯混合解、非线性模型的ekf解、非线性模型的ukf解、非线性模型的smc解。
⛄ 部分代码
function model= gen_model
% basic parameters
model.x_dim= 5; %dimension of state vector
model.z_dim= 2; %dimension of observation vector
model.v_dim= 3; %dimension of process noise
model.w_dim= 2; %dimension of observation noise
% dynamical model parameters (CT model)
% state transformation given by gen_newstate_fn, transition matrix is N/A in non-linear case
model.T= 1; %sampling period
model.sigma_vel= 5;
model.sigma_turn= (pi/180); %std. of turn rate variation (rad/s)
model.bt= model.sigma_vel*[ (model.T^2)/2; model.T ];
model.B2= [ model.bt zeros(2,2); zeros(2,1) model.bt zeros(2,1); zeros(1,2) model.T*model.sigma_turn ];
model.B= eye(model.v_dim);
model.Q= model.B*model.B';
% survival/death parameters
model.P_S= .99;
model.Q_S= 1-model.P_S;
% birth parameters (LMB birth model, single component only)
model.T_birth= 1; %no. of LMB birth terms
model.L_birth(1)=1; %no of Gaussians in birth term 1
model.r_birth(1)=0.01; %prob of birth
model.w_birth{1}(1,1)= 1; %weight of Gaussians - must be column_vector
model.m_birth{1}(:,1)= [ 0.1; 0; 0.1; 0; 0.01]; %mean of Gaussians
model.B_birth{1}(:,:,1)= diag([ 100; 10; 100; 10; 1 ]); %std of Gaussians
model.P_birth{1}(:,:,1)= model.B_birth{1}(:,:,1)*model.B_birth{1}(:,:,1)'; %cov of Gaussians
% observation model parameters (noisy r/theta only)
% measurement transformation given by gen_observation_fn, observation matrix is N/A in non-linear case
model.D= diag([ 2*(pi/180); 10 ]); %std for angle and range noise
model.R= model.D*model.D'; %covariance for observation noise
% detection parameters
model.P_D= .98; %probability of detection in measurements
model.Q_D= 1-model.P_D; %probability of missed detection in measurements
% clutter parameters
model.lambda_c= 20; %poisson average rate of uniform clutter (per scan)
model.range_c= [ -pi/2 pi/2; 0 2000 ]; %uniform clutter on r/theta
model.pdf_c= 1/prod(model.range_c(:,2)-model.range_c(:,1)); %uniform clutter density
⛄ 运行结果
⛄ 参考文献
[1]廖小云. 基于随机有限集的多目标跟踪算法研究[D]. 西安工业大学, 2016.
[2]董青, 胡建旺, 吉兵. 基于随机有限集的多目标跟踪算法综述[J]. 飞航导弹, 2019(3):6.
