Multiagent estimation and filtering for minimizing team meansquared error
Abstract
Motivated by estimation problems arising in autonomous vehicles and decentralized control of unmanned aerial vehicles, we consider multiagent estimation and filtering problems in which multiple agents generate state estimates based on decentralized information and the objective is to minimize a coupled meansquared error which we call \emph{team meansquare error}. We call the resulting estimates as minimum team meansquared error (MTMSE) estimates. We show that MTMSE estimates are different from minimum meansquared error (MMSE) estimates. We derive closedform expressions for MTMSE estimates, which are linear function of the observations where the corresponding gain depends on the weight matrix that couples the estimation error. We then consider a filtering problem where a linear stochastic process is monitored by multiple agents which can share their observations (with delay) over a communication graph. We derive expressions to recursively compute the MTMSE estimates. To illustrate the effectiveness of the proposed scheme we consider an example of estimating the distances between vehicles in a platoon and show that MTMSE estimates significantly outperform MMSE estimates and consensus Kalman filtering estimates.
 Publication:

arXiv eprints
 Pub Date:
 March 2019
 arXiv:
 arXiv:1903.12018
 Bibcode:
 2019arXiv190312018A
 Keywords:

 Electrical Engineering and Systems Science  Systems and Control
 EPrint:
 16 pages, 7 figures, Submitted to the IEEE Transactions on Signal Processing