一种用于WSN的低功耗稀疏参数估计策略

Low-cost Strategy for Sparse Parameter Estimation in WSN

文中基于自适应算法、分布式扩散估计和压缩感知理论,提出了一种用于稀疏参数分布式估计的压缩融合重构自适应策略(CCRA),应用于所估计的目标参数是稀疏的场景。文中给出了CCRA策略的运行步骤,并给出了推导方法。仿真表明,在目标参数是稀疏的情况下,相比其他低负载策略,CCRA策略在完全不影响网络估计性能的情况下,降低了通信负载。

A compressed combined reconstruction adaptive(CCRA)strategy for sparse parameter estimation was proposed based on adaptive algorithm,distributed diffusion estimation and compressed sensing theory in this paper,which was applied to scenarios where the estimated target parameters are sparse.The operation steps of the CCRA strategy were given in this paper,and the derivation method is also given.The simulation shows that,when the target parameters are sparse,CCRA strategy reduces much more communication load than that in other low-load strategies under the condition that the performance of network estimation is not affected at all.