论文标题
降低线性回归中的分散状态估计
Decentralized State Estimation In A Dimension-Reduced Linear Regression
论文作者
论文摘要
考虑了通信受限的传感器网络中的分散状态估计。交换的估计值是减少尺寸的，以减少使用线性映射到较低维空间的通信负载。平均误差最佳线性映射取决于所使用的特定估计方法。提出了几种降低维度的算法，其中每种算法对应于常用的分散估计方法。除一种算法外，所有算法都显示为最佳。对于剩余的算法，我们提供了收敛分析，从理论上讲，该算法会收敛到固定点，并在数值上表明收敛速率很快。提出了一种消息编码解决方案，该解决方案允许使用建议的降低技术时有效的通信。我们还从提出的框架中得出了不同的特性，并显示了其与基线方法相关的优越性。使用简单的融合示例和更现实的目标跟踪方案来证明不同算法的适用性。
Decentralized state estimation in a communication-constrained sensor network is considered. The exchanged estimates are dimension-reduced to reduce the communication load using a linear mapping to a lower-dimensional space. The mean squared error optimal linear mapping depends on the particular estimation method used. Several dimension-reducing algorithms are proposed, where each algorithm corresponds to a commonly applied decentralized estimation method. All except one of the algorithms are shown to be optimal. For the remaining algorithm, we provide a convergence analysis where it is theoretically shown that this algorithm converges to a stationary point and numerically shown that the convergence rate is fast. A message-encoding solution is proposed that allows for efficient communication when using the proposed dimension reduction techniques. We also derive different properties from the proposed framework and show its superiority in relation to baseline methods. The applicability of the different algorithms is demonstrated using a simple fusion example and a more realistic target tracking scenario.
