This article investigates the problem of estimating biases affecting relative state measurements in a sensor network. Each sensor measures the relative states of its neighbors, and this measurement is corrupted by a constant bias. We analyze under what conditions on the network topology and the maximum number of biased sensors the biases can be correctly estimated. We show that, for nonbipartite graphs, the biases can always be determined even when all the sensors are corrupted, whereas for bipartite graphs, more than half of the sensors should be unbiased to ensure the correctness of the bias estimation. If the biases are heterogeneous, then the number of unbiased sensors can be reduced to two. Based on these conditions, we propose three algorithms to estimate the biases.

中文翻译：

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传感器网络的偏差估计

本文研究估计影响传感器网络中相对状态测量的偏差的问题。每个传感器都测量其邻居的相对状态，并且该测量值会受到恒定偏差的破坏。我们分析了在网络拓扑和偏置传感器的最大数量的什么条件下可以正确估计偏置。我们表明，对于非二分图，即使所有传感器都损坏，也始终可以确定偏差，而对于二分图，应确保超过一半的传感器无偏差，以确保偏差估计的正确性。如果偏置不均一，则可以将无偏置传感器的数量减少到两个。基于这些条件，我们提出了三种算法来估计偏差。