Huaitie Xiao’s research while affiliated with National University of Defense Technology and other places

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Publications (2)


Genetic algorithm-tuned adaptive pruning SVDD method for HRRP-based radar target recognition
  • Article

June 2018

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17 Reads

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2 Citations

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Huaitie Xiao

A novel machine learning method named adaptive pruning support vector data description (APSVDD) is developed to classify the FFT-magnitude feature of complex high-resolution range profile (HRRP), motivated by the problem of radar automatic target recognition (RATR). The APSVDD method not only inherits the advantage of least square support vector machine (LSSVM) model, which owns low computational complexity with linear equality constraints so that it is convenient to prune the boundary of SVDD dynamically and rapidly, but also overcomes the shortcoming of ability to deal with outliers in SVDD so that it can enclose targets and exclude outliers simultaneously. Genetic algorithm (GA) tunes the pruning direction of ‘shear’ dynamically, reducing the empirical risk. And fuzzy membership contributes to decision of classes for multiclass fuzzy areas. Besides, similar to the LSSVM, the distribution information within classes is found by least square method and applied for adjusting the pruning depth of ‘shear’ in APSVDD. Hence, there will be a remarkable improvement in recognition accuracy and generalization performance. Numerical experiments based on two publicly UCI datasets and remotely sensed data of four aircrafts can demonstrate the feasibility, repeatability and superiority of the proposed method. The APSVDD is ideal for HRRP-based radar target recognition.


Fig. 1. Recognition results as the different privileged information (first, second and fourth order, length of radial projection) is chosen
Confusion matrix
Recognition results on HRRPs
Recognition results on Wine
Learning Using Privileged Information for HRRP-based Radar Target Recognition
  • Article
  • Full-text available

September 2017

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114 Reads

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19 Citations

A novel machine learning method named extended support vector data description with negative examples (ESVDDneg) is developed to classify the fast Fourier transform-magnitude feature of complex high-resolution range profile (HRRP), motivated by the problem of radar automatic target recognition. The proposed method not only inherits the close non-linear boundary advantage of support vector data description with negative examples model but also incorporates a new learning paradigm named learning using privileged information into the model. It leads to the appealing application with no assumptions regarding the distribution of data and needs less training samples and prior information. Besides, the second order central moment is selected as privileged information for better recognition performance, weakening the effect of translation sensitivity, and the normalisation contributes to eliminating the amplitude sensitivity. Hence, there will be a remarkable improvement of recognition accuracy not only with small training dataset but also under the condition of low signal-to-noise ratio. Numerical experiments based on two publicly UCI datasets and HRRPs of four aircrafts demonstrate the feasibility and superiority of the proposed method. The noise robust ESVDD-neg is ideal for HRRP-based radar target recognition.

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Citations (2)


... However, the PSO algorithm is prone to falling into local optimal solutions in the optimization process [36], while GA effectively maintains the population's diversity through crossover and mutation operations, which helps the algorithm avoid premature convergence and improves its global search ability. For example, Guo et al. used GA to dynamically adjust the direction of pruning, that is, to determine how to cut off unwanted parts of the SVDD's boundary to better distinguish targets and outliers [37]. ...

Reference:

Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD
Genetic algorithm-tuned adaptive pruning SVDD method for HRRP-based radar target recognition
  • Citing Article
  • June 2018

... Learning using privileged information(LUPI) is proposed for accompanying or hidden information in the learning model (Vapnik et al. 2007). Using privileged information for learning has been widely used in many tasks, such as text clustering (Sinoara et al. 2014) and image recognition (Guo et al. 2018;Yan et al. 2016). In classification tasks, this information can provide an effective supplementary strategy for classification. ...

Learning Using Privileged Information for HRRP-based Radar Target Recognition