A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Radar HRRP statistical recognition based on hypersphere model
Signal Processing
A Novel Feature Vector Using Complex HRRP for Radar Target Recognition
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Large margin nearest local mean classifier
Signal Processing
IEEE Transactions on Signal Processing
Missile target automatic recognition from its decoys based on image time-series
Pattern Recognition
Generalized re-weighting local sampling mean discriminant analysis
Pattern Recognition
Modeling recognizing behavior of radar high resolution range profile using multi-agent system
WSEAS Transactions on Information Science and Applications
Graph dual regularization non-negative matrix factorization for co-clustering
Pattern Recognition
Radar HRRP recognition based on discriminant information analysis
WSEAS Transactions on Information Science and Applications
Multi-polarized HRRP classification by SVM ensemble
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
FPGA-Based architecture for extended associative memories and its application in image recognition
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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Radar high-resolution range profile (HRRP) is very sensitive to time-shift and target-aspect variation; therefore, HRRP-based radar automatic target recognition (RATR) requires efficient time-shift invariant features and robust feature templates. Although higher order spectra are a set of well-known time-shift invariant features, direct use of them (except for power spectrum) is impractical due to their complexity. A method for calculating the Euclidean distance in higher order spectra feature space is proposed in this paper, which avoids calculating the higher order spectra, effectively reducing the computation complexity and storage requirement. Moreover, according to the widely used scattering center model, theoretical analysis and experimental results in this paper show that the feature vector extracted from the average profile in a small target-aspect sector has better generalization performance than the average feature vector in the same sector when both of them are used as feature templates in HRRP-based RATR. The proposed Euclidean distance calculation method and average profile-based template database are applied to two classification algorithms [the template matching method (TMM) and the radial basis function network (RBFN)] to evaluate the recognition performances of higher order spectra features. Experimental results for measured data show that the power spectrum has the best recognition performance among higher order spectra.