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-
This introduction includes a description and some discussion of the basic wiener filter and Kalman filter, and a relatively simple example with simulation results using MATLAB & its simulation results.Mahesh S. KumbharDr. R.H.ChileDr. S.R. Sawant...
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',...
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',...
“Tracking with Classification-Aided Multiframe Data Association.” IEEE Transactions on Aerospace and Electronic Systems, vol. 41, no. 3, July 2005, pp. 868–78. [3] Kuncheva, Ludmila I., et al. “Decision Templates for Multiple Classifier Fusion: An Experimental Comparison.” Pattern ...
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...
data fusion of existing features. We used the in-situ measured surface soil moisture as a response variable. We trained the model by using the input features as predictors and in-situ measured surface soil moisture as response. Finally, we compared the performance of our model with the bench...