Decentralized estimation in an inhomogeneous sensing environment
Decentralized estimation in an inhomogeneous sensing environment
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DOI：
10.1109/tit.2005.855580
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发表时间：
2005-10
影响因子：
2.5
通讯作者：
Jinjun Xiao;Z. Luo
中科院分区：
文献类型：
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作者：
Jinjun Xiao;Z. Luo
我们考虑分散估计噪声破坏的确定性参数的带宽受限的传感器网络与融合中心。传感器噪声被假定为是加性的，零均值，空间不相关的，但在其他方面是未知的，并且由于不同的传感器质量和不均匀的感测环境而可能在传感器之间不同。经典的最佳线性无偏估计（BLUE）线性组合的实值传感器的观测值，以最小化均方误差（MSE）。不幸的是，这样的计划不能实现在一个实际的带宽受限的传感器网络，由于其要求传输实值消息。在本文中，我们构建了一个分散的估计方案（DES），每个传感器压缩其观察到一个小数目的比特长度成正比的对数，其本地的信号噪声比（SNR）。来自不同传感器的压缩比特然后由融合中心收集和组合以估计未知参数。建议的DES是通用的，在这个意义上，每个传感器压缩方案只需要本地SNR的知识，而不是噪声概率分布函数（pdf），而最终的融合步骤也是独立的本地噪声pdf。我们表明，建议DES的MSE是在一个常数因子的25/8，实现了经典的集中式BLUE估计。
We consider decentralized estimation of a noise-corrupted deterministic parameter by a bandwidth-constrained sensor network with a fusion center. The sensor noises are assumed to be additive, zero mean, spatially uncorrelated, but otherwise unknown and possibly different across sensors due to varying sensor quality and inhomogeneous sensing environment. The classical best linear unbiased estimator (BLUE) linearly combines the real-valued sensor observations to minimize the mean square error (MSE). Unfortunately, such a scheme cannot be implemented in a practical bandwidth-constrained sensor network due to its requirement to transmit real-valued messages. In this paper, we construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local signal-to-noise ratio (SNR). The resulting compressed bits from different sensors are then collected and combined by the fusion center to estimate the unknown parameter. The proposed DES is universal in the sense that each sensor compression scheme requires only the knowledge of local SNR, rather than the noise probability distribution functions (pdf), while the final fusion step is also independent of the local noise pdfs. We show that the MSE of the proposed DES is within a constant factor of 25/8 of that achieved by the classical centralized BLUE estimator.