Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the constructi...Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the construction of measurement matrix or sensing matrix.A new deterministic sensing matrix,named as OOC-B,is proposed by exploiting optical orthogonal codes（OOCs）,Bernoulli matrix and Singer structure,which has the entries of 0,＋1 and-1 before normalization.We have proven that the designed deterministic matrix is asymptotically optimal.In addition,the proposed deterministic sensing matrix is applied to direction of arrival（DOA） estimation of narrowband signals by CS arrays（CSA）processing and CS recovery.Theoretical analysis and simulation results show that the proposed sensing matrix has good performance for DOA estimation.It is very effective for simplifying hardware structure and decreasing computational complexity in DOA estimation by CSA processing.Besides,lower root mean square error（RMSE） and bias are obtained in DOA estimation by CS recovery.展开更多
An energy-saving algorithm for wireless sensor networks based on network coding and compressed sensing （CS-NCES） is proposed in this paper. Along with considering the correlations of data spatial and temporal, the a...An energy-saving algorithm for wireless sensor networks based on network coding and compressed sensing （CS-NCES） is proposed in this paper. Along with considering the correlations of data spatial and temporal, the algorithm utilizes the similarities between the encoding matrix of network coding and the measurement matrix of compressed sensing. The source node firstly encodes the data, then compresses the coding data by cot-npressed sensing over finite fields. Compared with the network coding scheme, simulation results show that CS-NCES reduces the energy consumption about 25.30/0-34.50/0 and improves the efficiency of data reconstruction about 1.56%- 5.98%. The proposed algorithm can not only enhance the usability of network coding in wireless sensor networks, but also improve the network performance.展开更多
基金supported by the National Natural Science Foundation of China(6117119761371045+2 种基金61201307)the Shandong Provincial Promotive Research Fund for Excellent Young and Middle-aged Scientists(BS2010DX001)the Shandong Provincial Natural Science Foundation (ZR2011FM005)
文摘Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the construction of measurement matrix or sensing matrix.A new deterministic sensing matrix,named as OOC-B,is proposed by exploiting optical orthogonal codes（OOCs）,Bernoulli matrix and Singer structure,which has the entries of 0,＋1 and-1 before normalization.We have proven that the designed deterministic matrix is asymptotically optimal.In addition,the proposed deterministic sensing matrix is applied to direction of arrival（DOA） estimation of narrowband signals by CS arrays（CSA）processing and CS recovery.Theoretical analysis and simulation results show that the proposed sensing matrix has good performance for DOA estimation.It is very effective for simplifying hardware structure and decreasing computational complexity in DOA estimation by CSA processing.Besides,lower root mean square error（RMSE） and bias are obtained in DOA estimation by CS recovery.
文摘An energy-saving algorithm for wireless sensor networks based on network coding and compressed sensing （CS-NCES） is proposed in this paper. Along with considering the correlations of data spatial and temporal, the algorithm utilizes the similarities between the encoding matrix of network coding and the measurement matrix of compressed sensing. The source node firstly encodes the data, then compresses the coding data by cot-npressed sensing over finite fields. Compared with the network coding scheme, simulation results show that CS-NCES reduces the energy consumption about 25.30/0-34.50/0 and improves the efficiency of data reconstruction about 1.56%- 5.98%. The proposed algorithm can not only enhance the usability of network coding in wireless sensor networks, but also improve the network performance.
