Filtering Techniques for Stochastic Hybrid Systems. Advanced approch for Estimation and Control of SHS

      Аннотация: Stochastic Hybrid Systems (SHS) blend continuous and discrete dynamics, relevant to communication, vehicle control, finance, and tracking. Our research focuses on state estimation for linear and non-linear SHS, emphasizing solutions for missing measurements. Historically, SHS state estimation leaned towards deterministic models, overlooking issues like measurement loss. Researchers now explore probabilistic and guard condition-based state transitions. For example, in flying objects, SHS captures discrete flight modes and continuous dynamics. We introduce the Data Loss Detection Kalman Filter for linear SHS, bolstered by Chi-square statistics for measurement loss. In non-linear SHS, the Reallocation Resample Particle Filter and Systematic Resample Particle Filter excel in handling missing measurements. Our research illuminates state estimation intricacies, offering practical solutions.

Ключевые слова: Stochastic Hybrid System (SHS), continuous dynamics, discrete dynamics, State estimation, missing measurements, flying vehicle dynamics control, Deterministic models, measurement loss, guard condition, discrete state transition, Kalman Filter, Particle Filter, Data Loss Detection Kalman Filter, Chi-square statistics, Reallocation Resample Particle Filter