2023/09/29 by Burak Boyacıoğlu, Boyacıoğlu, Burak, Mahnoush Babaei +9 · 1 citation
Decision Sciences · Engineering · Physics and Astronomy · #93B07 #Control Systems and Identification #FOS: Electrical engineering #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.00127
openalex publication_date 2023/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Systems in nature are stochastic as well as nonlinear. In traditional applications, engineered filters aim to minimize the stochastic effects caused by process and measurement noise. Conversely, a previous study showed that the process noise can reveal the observability of a system that was initially categorized as unobservable when deterministic tools were used. In this paper, we develop a stochastic framework to explore observability analysis and sensor placement. This framework allows for direct studies of the effects of stochasticity on optimal sensor placement and selection to improve filter error covariance. Numerical results are presented for sensor selection that optimizes stochastic empirical observability in a bioinspired setting.