Unifying Consensus and Covariance Intersection for Efficient Distributed State Estimation Over Unreliable Networks

Amirhossein Tamjidi, Reza Oftadeh, Mohamed Naveed Gul Mohamed, Dan Yu, Suman Chakravorty, Dylan Shell
2021 IEEE Transactions on robotics  
This thesis studies the problem of recursive distributed state estimation over unreliable networks. The main contribution is to fuse the independent and dependent information separately. Local estimators communicate directly only with their immediate neighbors and nothing is assumed about the structure of the communication network, specifically it need not be connected at all times. The proposed estimator is a Hybrid one that fuses independent and dependent (or correlated) information using a
more » ... stributed averaging and iterative conservative fusion rule respectively. It will be discussed how the hybrid method can improve estimators's performance and make it robust to network failures. The content of the thesis is divided in two main parts. In the first part I study how this idea is applied to the case of dynamical systems with continuous state and Gaussian noise. I establish bounds for estimation performance and show that my method produces unbiased conservative estimates that are better than Iterative Covariance Intersection (ICI). I will test the proposed algorithm on an atmospheric dispersion problem, a random linear system estimation and finally a target tracking problem. In the second part, I will discuss how the hybrid method can be applied to distributed estimation on a Hidden Markov Model. I will discuss the notion of conservativeness for general probability distributions and use the appropriate cost function to achieve improvement similar to the first part. The performance of the proposed method is evaluated in a multi-agent tracking problem and a high dimensional HMM and it is shown that its performance surpasses the competing algorithms. ii ACKNOWLEDGEMENTS First, I would like to thank my advisor, Dr. Suman Chakravorty and my coadvisor Dr. Dylan Shell. Suman has been an excellent teacher, mentor, and a trustworthy friend. He was very patient and supportive at the time that I was having several surgeries on my eyes. He always inspires me to get a deeper understanding of my research topic and to become independent. Dylan was a great co-advisor, an encouraging and enthusiastic professor who taught me to look at the same problem from different angles. I greatly value the time and constructive discussions that Suman and Dylan had with me during our meetings. Without their guidance, I could have not finished this thesis. I would like also to thank Dr. Valasek and Dr. Hurtado who accepted to be in my thesis committee. My colleagues and friends in EDPLab and the DNC group of the aerospace department made my experience very pleasant. I had the privilege to work with Saurav Agarwal, Dan Yu, Mohammadhussein Rafieisakhaei, Reza Oftadeh during my studies. Their invaluable friendship will always be remembered. My wife, Yasaman, supported me throughout writing this thesis and her unassuming love remained incessant like always. My old friend Ali and Negar have impacted my life in a very positive way and no word can do justice to my appreciation of their friendship. iii CONTRIBUTORS AND FUNDING SOURCES
doi:10.1109/tro.2021.3064102 fatcat:ofh24d5xpng27nqlhw5yo6kcai