1. MULTISENSOR DATA FUSION FOR DEFENSE APPLICATION Othman Sidek and S.A.Quadri Collaborative µ-electronic Design Excellence Centre Universiti Sains Malaysia

4. DATA FUSION APPLICATION IN ESTIMATION PROBLEMS Application Dynamic system Sensor Types Process control Chemical plant Pressure Temperature Flow rate Gas analyzer Flood predication River system Water level Rain gauge Weather radar Tracking Spacecraft Radar Imaging system Navigation Ship Sextant Log Gyroscope Accelerometer Global Navigation satellite system

8. A surveillance spacecraft may have a set of sensors to track the status of different critical subsystems. It is of great importance to be able to fuse information from these sensors to create a global picture of the health of the spacecraft Which allow to predict an impending failure and correct it before it reaches criticality.

13. SIMULATION OF TARGET TRACKING AND ESTIMATION USING DATA FUSION Objective: Target tracking and estimation of a moving object Sensors required: Multiple sensors => Position estimation sensors => Velocity estimation sensors Need for heterogeneous multi sensors ? =>It is not possible to deduce a comprehensive picture about the entire target scenario from each of the pieces of evidence alone. =>Due to the inherent limitations of technical features characterizing each sensor. Coordinate system Selected : Cartesian coordinate system Technique applied : Multisensor data fusion using Kalman filter

15. Simulation has been carried out by with two-dimensional state model of the moving object along x; y and z directions. The program is executed in Mat lab environment .

16. As shown in figure estimation using state vector fusion method using Kalman filter is more close and accurate to actual track.

17. Sample code % Missile_Launcher tracking Moving_Object using kalman filter clear all %% define our meta-variables (i.e. how long and often we will sample) duration = 10 %how long the Moving_Object flies dt = .1; %The Missile_Launcher continuously looks for the Object-in-motion , %but we'll assume he's just repeatedly sampling over time at a fixed interval %% Define update equations (Coefficent matrices): A physics based model for A = [1 dt; 0 1] ; % state transition matrix: expected flight of the Moving_Object (state prediction) B = [dt^2/2; dt]; %input control matrix: expected effect of the input accceleration on the state. C = [1 0]; % measurement matrix: the expected measurement given %% define main variables u = 1.5; % define acceleration magnitude Q= [0; 0]; %initized state--it has two components: [position; velocity] of the Moving_Object Q_estimate = Q; %x_estimate of initial location estimation of where the Moving_Object Moving_ObjectAccel_noise_mag = 0.05; %process noise: the variability in Q_loc = []; % ACTUAL Moving_Object flight path vel = []; % ACTUAL Moving_Object velocity Q_loc_meas = []; % Moving_Object path that the Missile_Launcher sees %% simulate what the Missile_Launcher sees over time figure(2);clf figure(1);clf % Generate the Moving_Object flight Moving_ObjectAccel_noise = Moving_ObjectAccel_noise_mag * [(dt^2/2)*randn; dt*randn]; Q= A * Q+ B * u + Moving_ObjectAccel_noise; ......................... pause end %plot theoretical path of Missile_Launcher that doesn't use kalman plot(0:dt:t, smooth(Q_loc_meas), '-g.') %plot(0:dt:t, vel, '-b.') %% Do kalman filtering %initize estimation variables ......................... % Plot the results figure(2); plot(tt,Q_loc,'-r.',tt,Q_loc_meas,'-k.', tt,Q_loc_estimate,'-g.'); %data measured by the Missile_Launcher ……………………… .. %combined position estimate mu = Q_loc_estimate(T); % mean sigma = P_mag_estimate(T); % standard deviation y = normpdf(x,mu,sigma); % pdf y = y/(max(y)); hl = line(x,y, 'Color','g'); % or use hold on and normal plot axis([Q_loc_estimate(T)-5 Q_loc_estimate(T)+5 0 1]); %actual position of the Moving_Object plot(Q_loc(T)); ylim=get(gca,'ylim'); line([Q_loc(T);Q_loc(T)],ylim.','linewidth',2,'color','b'); legend('state predicted','measurement','state estimate','actual Moving_Object position') pause end