Distributed UAV State Estimation in UTM context

This paper proposes the application of a hybrid consensus on information (CI) and consensus on measurements (CM) algorithm, named HCMCI, to deal with the Distributed State Estimation (DSE) problem for a set of cooperating UAVs equipped with heterogeneous on board sensors and limited range communication devices. The benefit of the proposed algorithm is to allow to each vehicle to estimate position and velocity of the entire set of vehicles using only its own on board sensors and the information received only from the adjacent vehicles. The meaning of the theoretical conditions required to guarantee the stability of the estimate are discussed, with respect to the particular application. Numerical simulation are presented of different flight scenarios meaningful for the UAS Traffic Management (UTM) context.

[1]  Yan Jin,et al.  Information Sharing in Cooperative Unmanned Aerial Vehicle Teams , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[2]  Ronan Arraes Jardim Chagas,et al.  A Novel Linear, Unbiased Estimator to Fuse Delayed Measurements in Distributed Sensor Networks with Application to UAV Fleet , 2012 .

[3]  Marcus Johnson,et al.  Unmanned Aircraft System Traffic Management (UTM) Concept of Operations , 2016 .

[4]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[5]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

[6]  Giorgio Battistelli,et al.  Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability , 2014, Autom..

[7]  Robert Babuska,et al.  Distributed nonlinear estimation for robot localization using weighted consensus , 2010, 2010 IEEE International Conference on Robotics and Automation.

[8]  Randy A. Freeman,et al.  Estimation and control of UAV swarms for distributed monitoring tasks , 2011, Proceedings of the 2011 American Control Conference.

[9]  Giorgio Battistelli,et al.  Consensus-based algorithms for distributed filtering , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[10]  Giorgio Battistelli,et al.  Consensus-Based Linear and Nonlinear Filtering , 2015, IEEE Transactions on Automatic Control.

[11]  E. D'Amato,et al.  Attitude and position estimation for an UAV swarm using consensus Kalman filtering , 2015, 2015 IEEE Metrology for Aerospace (MetroAeroSpace).

[12]  K.H. Johansson,et al.  Distributed and Collaborative Estimation over Wireless Sensor Networks , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[13]  R. Olfati-Saber,et al.  Distributed Kalman Filter with Embedded Consensus Filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[14]  Y. Bar-Shalom Tracking and data association , 1988 .

[15]  Jérôme Cieslak,et al.  Fault diagnosis and monitoring of oscillatory failure case in aircraft inertial system , 2012 .

[16]  Eric Bonabeau,et al.  Control of UAV Swarms: What the Bugs Can Teach Us , 2003 .

[17]  Massimiliano Mattei,et al.  Fault tolerant low cost IMUS for UAVs , 2017, 2017 IEEE International Workshop on Measurement and Networking (M&N).

[18]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.