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ECE PhD Defense: "Networked Nonlinear Dynamic State Estimation With Time-Stamped Multi-Sensor Observations," Parivash Hajiyani


Dana 442

September 12, 2018 11:00 am
September 12, 2018 11:00 am


The main theme of this dissertation is networked dynamic nonlinear state estimation using EKF or UKF. The acquired sensor observations are time-stamped, and then transmitted over a digital communication network to a central estimation (and control) hub. The use of a communication medium introduces several challenges such as packet drops, irregularly-spaced observations, transmission delay, and bad data. Our continuous-discrete EKF and UKF can, by their very nature, handle arbitrarily-spaced sensor acquisition times. To counter some of the other challenges we augment the EKF/UKF with two modules: (i) a delay mitigation module, which uses additional storage to generate revised state estimates from delayed time-stamped measurements, and (ii) an innovations-based module that can detect the presence of a bad data measurement (and discard it). The dissertation results demonstrate the effectiveness of these modules in handling delays and corrupted sensor measurements.

To implement the continuous-discrete EKF/UKF algorithm in digital hardware, we discretize the continuous part of the system (i.e., the state equation), using Runge-Kutta explicit and implicit discretization schemes to reduce the cumulative error, as compared with the standard (Euler) discretization.  

Finally, we use an ML-based iterative online tuning algorithm to reduce the effects of uncertain process and measurement noise covariance matrices.

  • Professor Hanoch Lev-Ari (Advisor)
  • Professor Bahram Shafai
  • Professor Aleksander Stankovic