Optimizing time-frequency kernels for classification

Gillespie, B.W.; Atlas, Les

doi:10.1109/78.905863

Cited by 73 publications

(44 citation statements)

References 22 publications

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“…Literature [7] applied fuzzy functions to radar SEI, and proposed a data-driven fuzzy function kernel function optimization method. The kernel function was used to optimize the average mean-square distance between classes.…”

Section: Other Transform Domainmentioning

confidence: 99%

Overview of radar intra-pulse modulation recognition

Zang¹,

Li²

2018

AIP Conference Proceedings

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Abstract. This paper introduces the current radar intra-pulse modulation method, describes the status quo and development direction of the intentional modulation and unintentional modulation in the pulse, and summarizes the existing problems and prospects for the future. Looking forward to the future, and providing a reference direction for the research on radar signal recognition in the next step.

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Section: Other Transform Domainmentioning

confidence: 99%

Overview of radar intra-pulse modulation recognition

Zang¹,

Li²

2018

AIP Conference Proceedings

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“…In (Gillespie and Atlas, 2001), a special classdependent kernel is computed directly in the plane (τ, ν) by selecting discriminant (τ, ν) locations. The decision rule involves the Mahalanobis distance.…”

Section: Selected Topics Of Classification Based On Time-frequency Rementioning

confidence: 99%

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Swiercz¹

2010

International Journal of Applied Mathematics and Computer Science

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A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requires a priori knowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to the max discriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

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“…Such cross-terms are smoothed using the spectrogram or other general kernels. It has however been argued that these cross-terms in fact hold valuable classification information [6], and hence should not be excluded. Especially when the difference between the classes are small, such that high resolution and accurate locality is needed to separate them.…”

Section: Introductionmentioning

confidence: 99%

Classification of one-dimensional non-stationary signals using the Wigner-Ville distribution in convolutional neural networks

Brynolfsson

Sandsten

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper we argue that the Wigner-Ville distribution (WVD), instead of the spectrogram, should be used as basic input into convolutional neural network (CNN) based classification schemes. The WVD has superior resolution and localization as compared to other time-frequency representations. We present a method where a large-size kernel may be learned from the data, to enhance features important for classification. We back up our claims with theory, as well as application on simulated examples and show superior performance as compared to the commonly used spectrogram.

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Optimizing time-frequency kernels for classification

Cited by 73 publications

References 22 publications

Overview of radar intra-pulse modulation recognition

Overview of radar intra-pulse modulation recognition

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Classification of one-dimensional non-stationary signals using the Wigner-Ville distribution in convolutional neural networks

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