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。%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.
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