Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Global Positioning Systems, Inertial Navigation, and Integration
Global Positioning Systems, Inertial Navigation, and Integration
POP-Yager: A novel self-organizing fuzzy neural network based on the Yager inference
Expert Systems with Applications: An International Journal
Sensor fusion of a railway bridge load test using neural networks
Expert Systems with Applications: An International Journal
An investigation of neuro-fuzzy systems in psychosomatic disorders
Expert Systems with Applications: An International Journal
A combined wavelet analysis-fuzzy adaptive algorithm for radar/infrared data fusion
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fire detection model in Tibet based on grey-fuzzy neural network algorithm
Expert Systems with Applications: An International Journal
An approach based on ANFIS input selection and modeling for supplier selection problem
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In order to improve tracking ability, an adaptive fusion algorithm based on adaptive neuro-fuzzy inference system (ANFIS) for radar/infrared system is proposed, which combines the merits of fuzzy logic and neural network. Fuzzy adaptive fusion algorithm is a powerful tool to make the actual value of the residual covariance consistent with its theoretical value. To overcome the defect of the dependence on the knowledge of the process and measurement noise statistics of Kalman filter, neural network is introduced, which has the ability to learn from examples and extract the statistical properties of the examples during the training sessions. The fusion system mainly consists of Kalman filters, ANFIS sensor confidence estimators (ASCEs) based on contextual information (CI) theory, knowledge base (KB) and track-to-track fusion algorithms. Experimental data are implemented to train ASCEs to obtain sensor confidence degree. Simulation results show that the algorithm can effectively adjust the system to adapt contextual changes and has strong fusion capability in resisting uncertain information.