无线传感网中多传感器特征融合算法研究
|
摘要
面向无线传感器网络在地面目标识别方面的应用需求,该文提出了一种基于改进局域判别基(Local Discriminant Bases,LDB)和二进制粒子群优化(Binary Particle Swarm Optimization,BPSO)方法的多传感器特征融合算法。利用新的基于概率密度估计的相对微分熵可分性测度来改进LDB,以提取目标信号的特征频段,然后分别利用一种改进的和一种全新的BPSO来实现特征融合。基于实地采集到的地面目标的声音和震动信号,仿真实验表明,该方法减少了所需分类器的数目,降低了特征维数,并在一定程度上提高了目标的正确识别率,具有实际的应用价值。
A multi-sensor feature fusion algorithm based on improved Local Discriminant Bases (LDB) and Binary Particle Swarm Optimization (BPSO) is proposed in this paper to satisfy the requirement of application on classification of ground targets in wireless sensor networks. LDB is improved by a new discriminant measure using relative differential entropy based on probability density estimation and used to extract the characteristic frequency band of signals. Then an improved and a new BPSO are used for feature fusion respectively. Based on real acoustic and seismic signals of ground targets, experiment results indicate that this method can decrease the classifier number needed, reduce the dimension of features, and improve the performance of classification at a certain extent, so it is practically valuable for application.
A multi-sensor feature fusion algorithm based on improved Local Discriminant Bases (LDB) and Binary Particle Swarm Optimization (BPSO) is proposed in this paper to satisfy the requirement of application on classification of ground targets in wireless sensor networks. LDB is improved by a new discriminant measure using relative differential entropy based on probability density estimation and used to extract the characteristic frequency band of signals. Then an improved and a new BPSO are used for feature fusion respectively. Based on real acoustic and seismic signals of ground targets, experiment results indicate that this method can decrease the classifier number needed, reduce the dimension of features, and improve the performance of classification at a certain extent, so it is practically valuable for application.
引文
[1]Yick J,Mukherjee B,and Ghosal D.Wireless sensor network survey[J].Computer Networks,2008,52(12):2292-2330.
[2]Duarte M and Hu Y H.Vehicle classification in distributed sensor networks[J].Journal of Parallel and Distributed Computing,2004,64(7):826-838.
[3]聂伟荣,朱继南,郭亚军等.地震动信号的分析与目标识别[J].电子科技大学学报,2003,32(1):26-30.Nie Wei-rong,Zhu Ji-nan,and Guo Ya-jun,et al..Seismic signals analysis and identification[J].Journal of UEST of China,2003,32(1):26-30.
[4]Mazarakis G P and Avaritsiotis J N.Vehicle classification in sensor networks using time-domain signal processing and neural networks[J].Microprocessors and Microsystems,2007,31(6):381-392.
[5]Wu H W and Mendel J M.Classification of battlefield ground vehicles using acoustic features and fuzzy logic rule-based classifiers[J].IEEE Transactions on Fuzzy Systems,2007,15(1):56-72.
[6]Malhotra B,Nikolaidis I,and Harms J.Distributed classification of acoustic taargets in wireless audio-sensor networks[J].Computer Networks,2008,52(13):2582-2593.
[7]Kuncheva L I,Bezdek J C,and Duin R P W.Decision templates for multiple classifier fusion:An experimental comparison[J].Pattern Recognition,2001,34(2):299-314.
[8]Pan Q,Wei J M,and Cao H B,et al..Improved DS acoustic-seismic modality fusion for ground-moving target classification in wireless sensor networks.Pattern Recognition Letters,2007,28(16):2419-2426.
[9]Mallat S.A Wavelet Tour of Signal Processing[M].San Diego:Academic Press,1998:322-338.
[10]Saito N and Coifman R R.Local discriminant bases and their applications[J].Journal of Mathematical Imaging Vision,1995,5(4):337-358.
[11]Umapathy K,Krishnan S,and Rao R K.Audio signalfeature extraction and classification using local discriminant bases[J].IEEE Transactions on Audio,Speech,and Language Processing,2007,15(4):1236-1246.
[12]柳革命,孙超,刘兵等.局域判别基空间能量的水声目标特征提取[J].声学技术,2007,26(6):1089-1093.Liu Ge-ming,Sun Chao,and Liu Bing,et al..Feature extraction based on subspace energy of local discriminant basis[J].Technical Acoustics,2007,26(6):1089-1093.
[13]Kennedy J and Eberhart R C.Particle swarm optimization[C].IEEE1st International Conference on Neural Networks,IV.Piscataway,NJ:IEEE Service Center,1995:1942-1948.
[14]Eberhart R C and Kennedy J.A discrete binary version of the particle swarm algorithm[C].IEEE Conference on System,Man,and Cybernetics,Orlando,FL,1997,5:4104-4109.
[15]Chuang L,Chang H,and Tu C,et al..Improved binary PSO for feature selection using gene expression data[J].Computational Biology and Chemistry,2008,32(1):29-38.
[16]Huang C and Dun J.A distributed PSO-SVM hybrid system with feature selection and parameter optimization[J].Applied Soft Computing,2008,8(4):1381-1391.
[17]Seekings P and Potter J.Classification of marine acoustic signals using wavelets and neural networks[C].Proceedings of Underwater Defense Technology Conference,Singapore,2003:1-6.
[18]Clerc M and Kennedy J.The particle swarm explosion,stability,and convergence in a multidimensional complex space[J].IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
[2]Duarte M and Hu Y H.Vehicle classification in distributed sensor networks[J].Journal of Parallel and Distributed Computing,2004,64(7):826-838.
[3]聂伟荣,朱继南,郭亚军等.地震动信号的分析与目标识别[J].电子科技大学学报,2003,32(1):26-30.Nie Wei-rong,Zhu Ji-nan,and Guo Ya-jun,et al..Seismic signals analysis and identification[J].Journal of UEST of China,2003,32(1):26-30.
[4]Mazarakis G P and Avaritsiotis J N.Vehicle classification in sensor networks using time-domain signal processing and neural networks[J].Microprocessors and Microsystems,2007,31(6):381-392.
[5]Wu H W and Mendel J M.Classification of battlefield ground vehicles using acoustic features and fuzzy logic rule-based classifiers[J].IEEE Transactions on Fuzzy Systems,2007,15(1):56-72.
[6]Malhotra B,Nikolaidis I,and Harms J.Distributed classification of acoustic taargets in wireless audio-sensor networks[J].Computer Networks,2008,52(13):2582-2593.
[7]Kuncheva L I,Bezdek J C,and Duin R P W.Decision templates for multiple classifier fusion:An experimental comparison[J].Pattern Recognition,2001,34(2):299-314.
[8]Pan Q,Wei J M,and Cao H B,et al..Improved DS acoustic-seismic modality fusion for ground-moving target classification in wireless sensor networks.Pattern Recognition Letters,2007,28(16):2419-2426.
[9]Mallat S.A Wavelet Tour of Signal Processing[M].San Diego:Academic Press,1998:322-338.
[10]Saito N and Coifman R R.Local discriminant bases and their applications[J].Journal of Mathematical Imaging Vision,1995,5(4):337-358.
[11]Umapathy K,Krishnan S,and Rao R K.Audio signalfeature extraction and classification using local discriminant bases[J].IEEE Transactions on Audio,Speech,and Language Processing,2007,15(4):1236-1246.
[12]柳革命,孙超,刘兵等.局域判别基空间能量的水声目标特征提取[J].声学技术,2007,26(6):1089-1093.Liu Ge-ming,Sun Chao,and Liu Bing,et al..Feature extraction based on subspace energy of local discriminant basis[J].Technical Acoustics,2007,26(6):1089-1093.
[13]Kennedy J and Eberhart R C.Particle swarm optimization[C].IEEE1st International Conference on Neural Networks,IV.Piscataway,NJ:IEEE Service Center,1995:1942-1948.
[14]Eberhart R C and Kennedy J.A discrete binary version of the particle swarm algorithm[C].IEEE Conference on System,Man,and Cybernetics,Orlando,FL,1997,5:4104-4109.
[15]Chuang L,Chang H,and Tu C,et al..Improved binary PSO for feature selection using gene expression data[J].Computational Biology and Chemistry,2008,32(1):29-38.
[16]Huang C and Dun J.A distributed PSO-SVM hybrid system with feature selection and parameter optimization[J].Applied Soft Computing,2008,8(4):1381-1391.
[17]Seekings P and Potter J.Classification of marine acoustic signals using wavelets and neural networks[C].Proceedings of Underwater Defense Technology Conference,Singapore,2003:1-6.
[18]Clerc M and Kennedy J.The particle swarm explosion,stability,and convergence in a multidimensional complex space[J].IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心