Bias Estimation in Sensor Networks
Mingming Shi, Claudio De Persis, Pietro Tesi, Nima Monshizadeh · IEEE Transactions on Control of Network Systems · 2020
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.