电子与信息学报
電子與信息學報
전자여신식학보
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY
2014年
1期
27-33
,共7页
许晓荣%姚英彪%包建荣%陆宇
許曉榮%姚英彪%包建榮%陸宇
허효영%요영표%포건영%륙우
认知无线传感器网络%能量有效性%梯度投影稀疏重构%自适应压缩%加权能量子集函数
認知無線傳感器網絡%能量有效性%梯度投影稀疏重構%自適應壓縮%加權能量子集函數
인지무선전감기망락%능량유효성%제도투영희소중구%자괄응압축%가권능양자집함수
Cognitive Wireless Sensor Networks (C-WSN)%Energy-efficiency%Gradient Projection Sparse Reconstruction (GPSR)%Adaptive compression%Weighted energy subset function
针对认知无线传感器网络中传感器节点侧的模拟信息转换器对本地感知数据进行稀疏表示与压缩测量,该文提出一种基于能量有效性观测的梯度投影稀疏重构(GPSR)方法。该方法根据事件区域内认知节点对实际感知到的非平稳信号空时相关性结构,映射到小波正交基级联字典进行稀疏变换,通过加权能量子集函数进行自适应观测,以能量有效的方式获取合适的观测值,同时对所选观测向量进行正交化构造测量矩阵。汇聚节点采用 GPSR 算法进行自适应压缩重构。仿真比较了GPSR自适应重构与正交匹配追踪(OMP)重构算法。仿真结果表明,在压缩比小于0.2的区域内,基于能量有效性观测的 GPSR 自适应重构效果优于传统随机高斯测量信号重构。在相同节点数情况下,GPSR自适应压缩重构方法在低信噪比区域内具有较小的重构均方误差,且该方法所需观测数明显低于随机高斯观测,同时有效保障了感知节点的能耗均衡。
針對認知無線傳感器網絡中傳感器節點側的模擬信息轉換器對本地感知數據進行稀疏錶示與壓縮測量,該文提齣一種基于能量有效性觀測的梯度投影稀疏重構(GPSR)方法。該方法根據事件區域內認知節點對實際感知到的非平穩信號空時相關性結構,映射到小波正交基級聯字典進行稀疏變換,通過加權能量子集函數進行自適應觀測,以能量有效的方式穫取閤適的觀測值,同時對所選觀測嚮量進行正交化構造測量矩陣。彙聚節點採用 GPSR 算法進行自適應壓縮重構。倣真比較瞭GPSR自適應重構與正交匹配追蹤(OMP)重構算法。倣真結果錶明,在壓縮比小于0.2的區域內,基于能量有效性觀測的 GPSR 自適應重構效果優于傳統隨機高斯測量信號重構。在相同節點數情況下,GPSR自適應壓縮重構方法在低信譟比區域內具有較小的重構均方誤差,且該方法所需觀測數明顯低于隨機高斯觀測,同時有效保障瞭感知節點的能耗均衡。
침대인지무선전감기망락중전감기절점측적모의신식전환기대본지감지수거진행희소표시여압축측량,해문제출일충기우능량유효성관측적제도투영희소중구(GPSR)방법。해방법근거사건구역내인지절점대실제감지도적비평은신호공시상관성결구,영사도소파정교기급련자전진행희소변환,통과가권능양자집함수진행자괄응관측,이능량유효적방식획취합괄적관측치,동시대소선관측향량진행정교화구조측량구진。회취절점채용 GPSR 산법진행자괄응압축중구。방진비교료GPSR자괄응중구여정교필배추종(OMP)중구산법。방진결과표명,재압축비소우0.2적구역내,기우능량유효성관측적 GPSR 자괄응중구효과우우전통수궤고사측량신호중구。재상동절점수정황하,GPSR자괄응압축중구방법재저신조비구역내구유교소적중구균방오차,차해방법소수관측수명현저우수궤고사관측,동시유효보장료감지절점적능모균형。
Cognitive sensor local information sparse representation and compressive measurement are investigated, which are conducted by Analog-to-Information Converters (AIC) at each sensor in Cognitive Wireless Sensor Networks (C-WSN). Gradient Projection Sparse Reconstruction (GPSR) scheme based on energy-efficiency measurement is proposed. According to the spatial-temporal correlation structure of non-stationary signals perceived by massive cognitive sensors in Event Region (ER), these signals are mapped to wavelet orthogonal basis concatenate dictionaries to perform sparse representation. Adaptive measurement is implemented via weighted energy subset function, which could obtain the proper observation in energy-efficiency approach. The corresponding measurement matrix is constructed by the orthogonalization of these selected measurement vectors. Adaptive compressive reconstruction is performed at sink via GPSR algorithm, which is compared with conventional Orthogonal Matching Pursuit (OMP) algorithm. Simulation results indicate that, signal reconstruction effect based on energy-efficiency measurement GPSR adaptive compression is superior to Gaussian random measurement in the region where compression ratio is less than 0.2. With the same sensor numbers, the proposed GPSR adaptive compression approach has small reconstruction Mean Square Error (MSE) at low Signal-to-Noise Ratio (SNR) region, and the required measurement number is less than Gaussian random measurement, which guarantees sensors’ energy balance effectively.