Discrete-time distributed Kalman filter design for formations of autonomous vehicles

http://www.dem.ist.utl.pt/poliveira/Invest/Aut_2013_DV.pdf

Distributed state estimation for linear multi-agent systems with time-varying measurement topology

http://www.dem.ist.utl.pt/poliveira/Invest/decobs.pdf

Decentralized H2 observers for position and velocity estimation in vehicle formations with fixed topologies

This paper addresses the problem of distributed state estimation in a multi-vehicle framework. In the scenario envisioned in this work, each vehicle aims to estimate its own state by implementing a local state observer which relies on locally available measurements and limited communication with other vehicles in the vicinity. The dynamics of the problem are formulated as a more general discrete-time Kalman filtering problem with a sparsity constraint on the gain and, based on this formulation, a method for computation of steady-state observer gains for arbitrary fixed measurement topologies is introduced. The proposed method consists in the optimization of the time-varying distributed Kalman filter over a finite time window to approximate steady-state behavior and compute well-performing steady-state observer gains. To assess the performance of the proposed solution, simulation results are detailed for the practical case of a formation of Autonomous Underwater Vehicles (AUVs).

Discrete-time distributed Kalman filter design for multi-vehicle systems

This paper addresses the problem of state estimation in formations of autonomous vehicles. The approach considered here consists in the implementation of a local state observer in each vehicle relying only on locally available measurements and data communicated by neighboring agents, resulting in a decentralized state observer which features lower computational and communication loads than comparable centralized solutions. A method for computing observer gains which yield globally asymptotically stable error dynamics is presented for fixed topology formations, as well as an iterative algorithm for improving the decentralized estimator's performance when the measurements are corrupted by noise. The proposed framework is particularized to the practical case of a formation of Autonomous Underwater Vehicles (AUVs), and a continuous-discrete formulation is achieved for the local state observers, to take into account the difference in sampling rates between on-board instrumentation and positioning systems. To assess the performance of the solution, simulation results are presented and discussed for different formation topologies.

GAS decentralized navigation filters in a continuous-discrete fixed topology framework

A group of mobile robots can localize cooperatively, using relative position and absolute orientation measurements, fused through an extended Kalman filter (ekf). The topology of the graph of relative measurements is known to affect the steady-state value of the position error covariance matrix. Classes of sensor graphs are identified, for which tight bounds for the trace of the covariance matrix can be obtained based on the algebraic properties of the underlying relative measurement graph. The string and the star graph topologies are considered, and the explicit form of the eigenvalues of error covariance matrix is given. More general sensor graph topologies are considered as combinations of the string and star topologies, when additional edges are added. It is demonstrated how the addition of edges increases the trace of the steady-state value of the position error covariance matrix, and the theoretical predictions are verified through simulation analysis.

/pdf/sensor-graphs-for-guaranteed-cooperative-localization-443u18ftou.pdf

Ye Yuan

Papers

Sensor graphs for guaranteed cooperative localization performance