🍁🥬🕒摘要🕒🥬🍁
卡尔曼滤波是一种利用系统状态方程和观测数据来估计线性系统状态信息的最优估计算法。
✨🔎⚡运行结果⚡🔎✨
💂♨️👨🎓Matlab代码👨🎓♨️💂
clc;
clear all;
close all;
%% Video Initialization
video_name = 'sample3.mp4'; %Video name
vid = VideoReader(video_name);
nframes = vid.NumberOfFrames; %Number of frames
Height = vid.Height; % Height :)
Width = vid.Width; % Width :)
thr = 10; % Threshold for generating binary image of the noise
%% Kalman Filter Definition
% First, we define the state of interest. In this case, we define the
% following variables for our states: state(t) = [X Y dx dy (d^2)x (d^2)y](t)
% X(t+1) = 1/2(a)T^2 + V(t)T + X(t); where a and V denotes the acceleration
% and velocity respectively.
% V(t+1) = aT + V(t)
% a(t+1) = a(t) ; assuming constant acceleration
%State(t+1) = A.State(t) + B.u + <State Uncertainty|State Noise>
dt=0.5;
% A = [1 0 dt 0 (dt^2)/2 0;
% 0 1 0 dt 0 (dt^2)/2;
% 0 0 1 0 dt 0;
% 0 0 0 1 0 dt;
% 0 0 0 0 1 0;
% 0 0 0 0 0 1];
A = [1 0 dt 0;
0 1 0 dt;
0 0 1 0 ;
0 0 0 1 ;
];
B = [(dt^2)/2 (dt^2)/2 dt dt]';
%B = [(dt^2)/2 (dt^2)/2 dt dt 1 1]';
% B=0;
% Input to the system (here acceleration).
u = 4e-3;
%We are observing the X, and Y position. So our observation is: y = [X Y]
%y(t) = H.State(t) + <Measurement Noise>
% H = [1 0 0 0 0 0
% 0 1 0 0 0 0];
H = [1 0 0 0;
0 1 0 0];
📜📢🌈参考文献🌈📢📜
[1]刘义,杨鹏.基于卡尔曼滤波的云台自适应姿态优化算法[J].自动化与仪表,2022,37(11):80-86.DOI:10.19557/j.cnki.1001-9944.2022.11.017.