【摘要】 在无线传感器网络背景下的分布式估计中,由于传输网络对发送功率和传输带宽的限制,压缩信源冗余、降低通信数据量便成为一个重要的课题.为此,本文提出了一种基于多比特量化观测的分布式估计方法(MQS),利用渐进性能作为优化准则构造量化阈值优化问题,运用粒子群算法对其进行求解得到最优量化阈值,给出了克拉美罗下界的解析表达式,并与均匀量化方法(UQS)和未量化方法(NQS)进行对比.理论分析和仿真实验表明,MQS的性能优于UQS.当量化深度增大到3时,MQS的估计性能十分接近NQS的估计性能.更多还原

【Abstract】 In the context of distributed estimation in w ireless sensor netw orks( WSN),due to transmission pow er /bandw idth constrains,it is significant to reduce size of transmitted data. In this paper,a distributed estimation scheme,namely,multi-level quantization scheme( M QS) is proposed. The quantization threshold optimization problem is formulated by using asymptotic performance as an optimality criterion. The optimum quantization thresholds are obtained by resorting to particle sw arm optimization algorithm. The explicit expression of the Cramér-Rao low er bound is derived. The proposed method is compared w ith uniform quantization scheme( UQS) and no quantization scheme( NQS). Theoretical analysis and simulation results demonstrate that the M QS scheme outperforms the UQS. M oreover,w ith 3-bit quantization,the M QS can provide estimation performance very close to that of the NQS.更多还原