pdaf算法matlab程序.txt

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% 程序功能:采用数据关联算法实现单个匀速运动目标的点迹与航迹的关联
% 输入变量:
% -target_position: 目标的初始位置 target_position=[1500 500 1500 400];
% - n: 采样次数 50
% - T: 采样间隔 1
% -MC_number:仿真次数 5
%状态方程: X(n)=A*X(n-1)+G*Q
%观测方程: Z(n)=C*X(n)+R
% 输出变量:
% 无
% 作者: 冯洋
% 日期: 05-28-2007
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%function PDAF(target_position,n,T,MC_number)
tic
clear all
close all
n=50; %采样次数
T=1; %T为采样间隔
MC_number=5; %monte carlos run times
target_position=[1500 500 1500 400]; %目标的起始位置和速度(km,km/s) (初始状态向量)
Pd=; %检测概率
Pg=; %正确量测落入跟踪门内得概率
g_sigma=; %门限
gamma=7*10^(-6); %每一个单位面积(km^2)内产生2个杂波????????(虚假量测的空间密度lamda)
A = [1 T 0 0;0 1 0 0;0 0 1 T;0 0 0 1]; %状态转移矩阵(F)
C = [1 0 0 0;0 0 1 0]; %观测矩阵
target_delta=100; %目标对应的观测标准差
R=[target_delta^2 0;0 target_delta^2]; %观测协方差矩阵(Q2)
Q=[4 0;0 4]; %过程噪声协方差(Q1)
G=[T^2/2 0;T 0;0 T^2/2;0 T]; %系统过程噪声矩阵(H)
P=[target_delta^2 target_delta^2/T 0 0;target_delta^2/T 2*target_delta^2/T^2 0 0;0 0 target_delta^2 target_delta^2/T;0 0 target_delta^2/T 2*target_delta^2/T^2];
%初始协方差矩阵%(状态估计误差自相关矩阵)
x_filter=zeros(4,n); %存储目标的各时刻的滤波值(状态)
x_filter1=zeros(4,n,MC_number); %MC_number次montle carlo仿真所得全部结果存储矩阵
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%产生目标的实际位置
%