Multi-Sensor Data Fusion with MATLAB®. CRC Press, 2009 Dasarathy, B. V. Decision fusion (Vol. 1994). Los Alamitos, CA: IEEE Computer Society Press, 1994 Dasarathy, B. V. Sensor fusion potential exploitation-
A state variable approach to describe the motion characteristics of the target and sensor measurement model is utilized and performance evaluation of tracking filters are investigated. The experimental results in MATLAB show fusion architectures that demonstrate better tracking results with less residual ...
International Journal of Modelling and SimulationHoseini, S.A., Ashraf, M.R.: `Computational complexity comparison of multi-sensor single target data fusion methods by matlab', Int. J. Chaos, Control, Model. Simul., 2013, 2, (2), pp. 1-8...
tracker = fusion.tracker.JIPDATracker with properties: TargetSpecifications: {[1×1 HighwayTruck]} SensorSpecifications: {[1×1 AutomotiveCameraBoxes]} MaxMahalanobisDistance: 5 ConfirmationExistenceProbability: 0.9000 DeletionExistenceProbability: 0.1000 Create and Configure Specification for Marine Targets...
Multi-Sensor Data Fusion with MATLAB Using MATLABexamples wherever possible, explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The aut... CRC Press - ...
Update the tracker with two detections having nonzero ObjectClassID. The detections immediately create confirmed tracks. Get detections = {objectDetection(1,[10;0],'SensorIndex',1, ... 'ObjectClassID',5,'ObjectAttributes',{struct('ID',1)}); ... objectDetection(1,[0;10],'SensorIndex',...
For numerical evaluation of the optimization problem (Eq. (5)), the MATLAB fsolve function with the Levenberg–Marquardt optimization algorithm was used. For eight involved sensors, three artifact positions and Ncp ≈ 500 points per sensor and position, the process takes Experimental verification As...
Update the tracker with two detections having nonzero ObjectClassID. The detections immediately create confirmed tracks. Get detections = {objectDetection(1,[10;0],'SensorIndex',1, ... 'ObjectClassID',5,'ObjectAttributes',{struct('ID',1)}); ... objectDetection(1,[0;10],'SensorIndex',...
This paper presents an approach to multi-sensory and multi-modal fusion in which computer vision information obtained from calibrated cameras is integrated with a large-scale sentient computing system known as “SPIRIT”. The SPIRIT system employs an ultrasonic location infrastructure to track people an...
Update the tracker with two detections both having nonzero ObjectClassID. These detections immediately create confirmed tracks. Get detections = {objectDetection(1,[10;0],'SensorIndex',1, ... 'ObjectClassID',5,'ObjectAttributes',{struct('ID',1)}); ... objectDetection(1,[0;10],'Sensor...