由未知自回归过程产生的未知位置采样的空间场估计
Spatial Field estimation from Samples taken at Unknown Locations generated by an Unknown Autoregressive Process
摘要：
用移动传感器对物理场进行采样是一个即将到来的感兴趣的领域。这在成本方面提供了更大的优势，因为通常仅单个传感器可以用于该目的，并且这几乎可以在没有感测站的任何地方使用，并且具有标称的操作成本。在移动感测设置中，采样位置的准确知识可能导致成本的多方面增加。此外，移动车辆的惯性约束样本间距离之间的独立性，使它们相关。因此，这项工作的目的是估计空间带限字段的样本，损坏的测量噪声，收集的采样位置上获得的样本间距离的自回归模型。自回归模型用于捕获样本间距离之间的相关性。在这种设置中的采样在未知的采样位置从样本间距离的自回归模型获得，字段和其估计版本之间的均方误差已被证明为O（$1/n$），其中$n$是移动传感器所采取的样本的平均数减少。
英文摘要：
Sampling of physical fields with mobile sensors is an upcoming field of interest. This offers greater advantages in terms of cost as often just a single sensor can be used for the purpose and this can be employed almost everywhere without sensing stations and has nominal operational costs. In a mobile sensing setup, the accurate knowledge of sampling locations may lead to a manifold increase in the costs. Moreover, the inertia of the moving vehicle constrains the independence between the intersample distances, making them correlated. This work, thus, aims at estimating spatially bandlimited fields from samples, corrupted with measurement noise, collected on sampling locations obtained from an autoregressive model on the intersample distances. The autoregressive model is used to capture the correlation between the intersample distances. In this setup of sampling at unknown sampling locations obtained from an autoregressive model on intersample distances, the mean squared error between the field and its estimated version has been shown to decrease as O($1/n$), where $n$ is the average number of samples taken by the moving sensor.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
