Data-Driven Adaptive Distributed Localization of Multi-Agent Systems With Sensor Failure

This work solves the localization estimation of dynamic multi-agent systems (MASs) with sensor multiplicative failures, which is more general yet challenging to address than static sensor networks with ideal conditions. Barycentric coordinate is introduced to characterize the relative positions between agents. A new linear data model is constructed to represent the relationship between barycentric coordinates and relative distance. Based on the linear model, an adaptive parameter estimation algorithm is designed, and then it is applied to solve the relative distance compensation problem of the MASs with sensor multiplicative failures. Using the estimated parameter, a data-driven adaptive distributed localization estimation scheme based on iterative learning is proposed, in which only the measured relative distance data are available instead of the system model information. A key to obtaining accurate localization is overcoming the difficulties from inaccurate relative distance variables due to sensor failure via the data-driven adaptive relative distance compensation method. The numerical examples and experimental results verify the effectiveness of the proposed methods.