Kalman-type filtering tends to become one of the favorite approaches for solving joint input-state estimation problems in the structural dynamics community. This article focuses on the applicability of the Augmented Kalman Filter (AKF) for reconstructing mechanical sources, addressing a set of practical issues that are frequently encountered in the engineering practice. In particular, this paper aims to help the reader to better apprehend some of the advantages and limitations of the application of the AKF in the context of purely input estimation problems. The present paper is not a simple collection of test cases, since it introduces a novel state-space representation of dynamical systems, based on the generalized-α method, as well as further insights in the tuning of Kalman filters from the Bayesian perspective. In this work, the various practical situations considered lead us to recommend to employ collocated acceleration measurements, when reconstructing exci-tation sources from the AKF. It is also demonstrated that the violation of some of the feasibility conditions proposed in the literature doesn't necessarily imply the failure of the estimation process.