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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.
| Autorius: | Robinson Paul, Vishvjit Thakar, Hetal Patel, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2023 |
| Knygos puslapių skaičius: | 104 |
| ISBN-10: | 6206787133 |
| ISBN-13: | 9786206787136 |
| Formatas: | 220 x 150 x 7 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Filtering Techniques for Stochastic Hybrid Systems: Advanced approch for Estimation and Control of SHS“
