Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A Hybrid Neural Network System for Pattern Classification Tasks with Missing Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determination of number of sources with multiple arrays incorrelated noise fields
IEEE Transactions on Signal Processing
Source number estimators using transformed Gerschgorin radii
IEEE Transactions on Signal Processing
On determination of the number of signals in spatially correlatednoise
IEEE Transactions on Signal Processing
Image Segmentation Based on Adaptive Cluster Prototype Estimation
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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The research of sources number detection is still open and challenging issue in array signal processing. The accurate estimation may be very essential to those high resolution direction finding algorithms. Three crucial issues are discussed for the application of cluster method to the source-number detection. A detection method based on Fuzzy-c-Means clustering algorithm has been proposed, which uses canonical correlation coefficients of the joint covariance matrix as the feature to be classified. Compared with the classical methods, our algorithm has better performance in low SNR and angular resolution. In the spatially correlated noise fields, the simulation results demonstrate the effectiveness and robustness of the proposed scheme.