Method for processing measurements obtained by an antenna system at a given time index t, including the following different steps, carried out by a calculating unit of a device: • Within a first list of clusters, a cluster being defined as a set of measurements including temporal characteristics, spatial characteristics and signal characteristics, a cluster being created from a first measurement and changing as new measurements are produced, predicting, at the time index t, the spatial characteristics of each cluster contained in the first list of clusters according to the spatial characteristics of the cluster at its latest update to form a second list of clusters (1020, 101); • For each measurement: ∘ Forming a third list of clusters comprised in the second list, each cluster including the spatial characteristics predicted hereinabove; ∘ Calculating a likelihood score between the measurement and each cluster of the third list of clusters (1025, 103), the likelihood score being calculated from two factors, a first factor being a distance between the measurement and at least one spatial characteristic of the cluster and the second factor being the result of a statistical treatment of the characteristics of the cluster. the method characterised in that it further includes: ∘ Selecting the maximum likelihood score (1040, 104). ∘ Comparing the maximum likelihood score with a predetermined threshold (1041, 105): ▪ If the maximum likelihood score is above the threshold, updating the characteristics of each cluster of the third list of clusters to form a fourth list of clusters (1050, 106) integrating the information from the measurement; ▪ Otherwise, creating a new cluster from the measurement and placing the new cluster in the second list of clusters (1020, 107). • Within a first list of clusters, a cluster being defined as a set of measurements including temporal characteristics, spatial characteristics and signal characteristics, a cluster being created from a first measurement and changing as new measurements are produced, predicting, at the time index t, the spatial characteristics of each cluster contained in the first list of clusters according to the spatial characteristics of the cluster at its latest update to form a second list of clusters (1020, 101); • For each measurement: ∘ Forming a third list of clusters comprised in the second list, each cluster including the spatial characteristics predicted hereinabove; ∘ Calculating a likelihood score between the measurement and each cluster of the third list of clusters (1025, 103), the likelihood score being calculated from two factors, a first factor being a distance between the measurement and at least one spatial characteristic of the cluster and the second factor being the result of a statistical treatment of the characteristics of the cluster. the method characterised in that it further includes: ∘ Selecting the maximum likelihood score (1040, 104). ∘ Comparing the maximum likelihood score with a predetermined threshold (1041, 105): ▪ If the maximum likelihood score is above the threshold, updating the characteristics of each cluster of the third list of clusters to form a fourth list of clusters (1050, 106) integrating the information from the measurement; ▪ Otherwise, creating a new cluster from the measurement and placing the new cluster in the second list of clusters (1020, 107). ∘ Forming a third list of clusters comprised in the second list, each cluster including the spatial characteristics predicted hereinabove; ∘ Calculating a likelihood score between the measurement and each cluster of the third list of clusters (1025, 103), the likelihood score being calculated from two factors, a first factor being a distance between the measurement and at least one spatial characteristic of the cluster and the second factor being the result of a statistical treatment of the characteristics of the cluster. ∘ Selecting the maximum likelihood score (1040, 104). ∘ Comparing the maximum likelihood score with a predetermined threshold (1041, 105): ▪ If the maximum likelihood score is above the threshold, updating the characteristics of each cluster of the third list of clusters to form a fourth list of clusters (1050, 106) integrating the information from the measurement; ▪ Otherwise, creating a new cluster from the measurement and placing the new cluster in the second list of clusters (1020, 107). ▪ If the maximum likelihood score is above the threshold, updating the characteristics of each cluster of the third list of clusters to form a fourth list of clusters (1050, 106) integrating the information from the measurement; ▪ Otherwise, creating a new cluster from the measurement and placing the new cluster in the second list of clusters (1020, 107).