Two-stage approach for cooperative multi-vehicle localization using integrated measurements

Abstract

In this article, the distributed filtering of absolute and relative measurements for the cooperative localisation of multiple vehicles is investigated. A novel two-stage approach that uses two unbiased cooperative filters for the sequential processing of integrated measurements is proposed. The first filter sequentially estimates each vehicle state by replacing extra neighbouring states with corresponding estimates obtained only from absolute measurements and by adding relative measurements. The second filter considers extra neighbouring states as auxiliary coloured noise. The proposed filters have low communication loads and computational complexity because of the sequential processing of the absolute and relative measurements. Unlike existing cooperative filters, the proposed two-stage structure makes the filters robust against the presence of unreliable links between neighbouring vehicles. We present simulation results demonstrating the effectiveness and accuracy of the proposed filters when applied to vehicles performing two-dimensional manoeuvres in three network topologies: a ring, line, and mesh.