Classical distributed estimation scenarios typically assume timely and reliable exchanges of information over the sensor network. This paper, in contrast, considers single time-scale distributed estimation via a sensor network subject to transmission time-delays. The proposed discrete-time networked estimator consists of two steps: (i) consensus on (delayed) a-priori estimates, and (ii) measurement update. The sensors only share their a-priori estimates with their out-neighbors over (possibly) time-delayed transmission links. The delays are assumed to be fixed over time, heterogeneous, and known. We assume distributed observability instead of local observability, which significantly reduces the communication/sensing loads on sensors. Using the notions of augmented matrices and Kronecker product, the convergence of the proposed estimator over strongly-connected networks is proved for a specific upper-bound on the time-delay.

翻译：经典分布式估计假设通常假定在传感器网络上进行及时和可靠的信息交流。与此形成对照的是,本文件考虑通过一个可延迟传输时间的传感器网络进行单一的时间尺度分布估计。拟议的离散时间网络估计包括两个步骤:(一) 就(延迟)优先估计达成共识,和(二) 测量更新。传感器仅与(可能)延迟时间传输连接的外围邻居分享其优先估计。这些延迟被假定为随时间而固定、多式和已知。我们假定分布式可观测性,而不是当地可观测性,这大大降低了传感器的通信/感应负荷。利用增强的矩阵和克伦克尔产品的概念,拟议的估计值与紧密连接的网络的趋同在时间交易的具体上限上得到了证明。