A new sense-through-foliage target recognition method based on hybrid particle swarm optimization-based wavelet twin support vector machine. (February 2016)

• Record Type: Journal Article
• Authors: Zhai, Shijun; Pan, Juan; Luo, Hongwei; Fu, Shan; Chen, Hongji
• Is Part Of: Measurement. Volume 80(2016:Feb.)
• Page Start: 58
• Page End: 70
• Publication Date: 2016-02
• Subjects: Target recognition -- Sparse representation -- Feature extraction -- Wavelet twin support vector machine -- Hybrid particle swarm optimization; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2015.11.027
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A new target recognition method based on HPSO-trained WTSVM and sparse representation is proposed. A novel HPSO algorithm is proposed to selected appropriate parameters for WTSVM. Sparse representation and dictionary learning are applied to extracted features from target echo signals. The proposed method has more excellent performance in target recognition. Abstract: In order to improve the accuracy of sense-through-foliage target recognition, a new recognition method based on sparse representation-based adaptive feature extraction and hybrid particle swarm optimization (HPSO)-optimized wavelet twin support vector machine (WTSVM) is proposed in this paper. First, an adaptive feature extraction approach based on sparse representation is applied to extract the target features from the measured radar echo waveforms, the target feature set is constructed by sparse coefficients that contain most target information. Then, a new recognition method based optimized WTSVM is developed to perform target recognition. Twin SVM (TSVM) is a powerful tool in the field of machine learning, but the kernel and parameters selection problem still affects the performance of TSVM directly. A novel HPSO is developed in this study to determine the optimal parameters for WTSVM with the highest accuracy and generalization ability. As a hybridization strategy, local search is integrated in the PSO algorithm to further refine the performance of individuals and accelerate their convergence toward the global optimality. Finally, the performance of the proposed method is verified by experiments taken in the forest, and the results conform the improved accuracy of target recognition.