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中文摘要：
这份报纸在无线传感器网络为随机的地评价学习传感器选择问题。作者首先证明选择在 D 最佳的标准下面最小化评价错误的一套 l 传感器是 NP 完全的。作者建议一个反复的算法追求一个非最优的答案。而且，联网以便改进带宽和无线传感器的精力效率，作者与使量子化的大小和研究为一个 Gaussian 随机的领域建议一个最好的线性不偏的评估者相应传感器选择问题。在未知协变性矩阵的情况中，作者用大小为协变性矩阵建议一个评估者并且也分析这个评估者的敏感。模拟结果显示出建议算法的好表演。
英文摘要：
This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal criterion is NP-complete. The authors propose an iterative algorithm to pursue a suboptimal solution. Furthermore, in order to improve the bandwidth and energy efficiency of the wireless sensor networks, the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem. In the case of unknown covariance matrix, the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator. Simulation results show the good performance of the proposed algorithms.
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