Scalable Distributed State Estimation in UTM Context

Abstract

This article proposes a novel approach to the Distributed State Estimation (DSE) problem for a set of co-operating UAVs equipped with heterogeneous on board sensors capable of exploiting certain characteristics typical of the UAS Traffic Management (UTM) context, such as high traffic density and the presence of limited range, Vehicle-to-Vehicle communication devices. The proposed algorithm is based on a scalable decentralized Kalman Filter derived from the Internodal Transformation Theory enhanced on the basis of the Consensus Theory. The general benefit of the proposed algorithm consists of, on the one hand, reducing the estimation problem to smaller local sub-problems, through a self-organization process of the local estimating nodes in response to the time varying communication topology; and on the other hand, of exploiting measures carried out nearby in order to improve the accuracy of the local estimates. In the UTM context, this enables each vehicle to estimate both its own position and velocity, as well as those of the neighboring vehicles, using both on board measurements and information transmitted by neighboring vehicles. A numerical simulation in a simplified UTM scenario is presented, in order to illustrate the salient aspects of the proposed algorithm.

Distributed and Decentralized State Estimation Applied to Complex Epidemic Networks

“…Differences in the RMSE values are due to numerical errors and nonlinear approximations. Indeed, as previously argued (Mutambara, 1998;Cicala et al, 2020), if the network is connected then the DDEIF is equivalent to the EIF centralized algorithm. Observe that a network is said to be connected if there is a path between every pair of nodes.…”

“…Thus, each network node has its own processor, which can use both global and distributed models. In this paper, the term distributed refers to the use of scalable models, where the local models involve only locally relevant states to each node and the local filters perform a n i reduced-order operation, with n i n × N (Mutambara, 1998;Cicala et al, 2020).…”

“…Let us consider F i,k−1 the Jacobian of f k−1 in (4) evaluated at x i,k−1 . By using the scalability and internodal transformation relationships, we can present the DDEIF described in the Algorithm 2 (Mutambara, 1998;Cicala et al, 2020). Remark 5.…”

“…However, most of these researches are focused on large-scale sensor networks for monitoring low-dimension systems. Indeed, usually, a scalable solution is addressed based only on the observation equation and not on the state transition matrix (Cicala et al, 2020). In order to overcome this issue, an earlier study (Mutambara, 1998) inspired Cicala et al (2020) to propose the distributed and decentralized information filter (DDEIF) algorithm combined with a consensus strategy using the internodal transformation theory on the state estimation.…”

“…Indeed, usually, a scalable solution is addressed based only on the observation equation and not on the state transition matrix (Cicala et al, 2020). In order to overcome this issue, an earlier study (Mutambara, 1998) inspired Cicala et al (2020) to propose the distributed and decentralized information filter (DDEIF) algorithm combined with a consensus strategy using the internodal transformation theory on the state estimation. It is important to point out that the DDEIF is a variation of the distributed and decentralized extended Kalman filter (DDEKF) algorithm (Mutambara, 1998).…”