Multirate, multiresolution, recursive Kalman filter
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
IMM fuzzy probabilistic data association algorithm for tracking maneuvering target
Expert Systems with Applications: An International Journal
An adaptive fusion algorithm based on ANFIS for radar/infrared system
Expert Systems with Applications: An International Journal
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
Characterization of signals by the ridges of their wavelettransforms
IEEE Transactions on Signal Processing
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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This paper presents a new algorithm that combines a fuzzy adaptive fusion and wavelet analysis to form an efficient data fusion technique for the target tracking system. The 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 extended Kalman filter (EKF), wavelet analysis is introduced, which needs no prior knowledge of the process and measurement noise. And fuzzy inference system is applied for its simplicity of the approach and its capability of processing imprecise information. In addition, the paper highlights the use of a new wavelet-based thresholding method to enhance the computational ability of wavelet coefficients. The simulation experiments on the novel adaptive fusion algorithm have been performed. The experimental results show that the proposed algorithm can effectively strengthen the system robustness and improve the tracking precision. It is obvious that the algorithm has significant advantages over the traditional EKF algorithm in tracking application via comparison of data.