Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Characterized by sudden structural changes, fault-prone systems are modeled using the framework of systems with switching parameters or hybrid systems. Since a closed-form mean-square optimal filtering algorithm for this class of systems does not exist, it is of particular interest to derive a lor bound on the state estimation error covariance. The ll known Crameacuter-Rao bound is not applicable to fault-prone systems because of the discrete distribution of the fault indicators, which violates the regularity conditions associated with this bound. On the other hand, the iss-instein lor bound is essentially free from regularity conditions. Moreover, a sequential version of the iss-instein bound, suitable for Markovian dynamic systems, is presented by the authors in a companion paper. In the present paper, this sequential version is applied to several classes of fault-prone dynamic systems. The resulting bounds can be used to examine fault detectability and identifiability in these systems. Moreover, it is shown that several recently reported lor bounds for fault-prone systems are special cases of, or closely related to, the sequential version of the iss-instein lor bound
['Ilia Rapoport', 'Yaakov Oshman']
I am writing a research paper on a idea Weiss–Weinstein Lower Bounds for Markovian Systems. I am writing a research paper on a idea Part 2: Applications to Fault-Tolerant Filtering
[{"from": "human", "value": "I am writing a research paper on a idea Weiss\u2013Weinstein Lower Bounds for Markovian Systems. I am writing a research paper on a idea Part 2: Applications to Fault-Tolerant Filtering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Characterized by sudden structural changes, fault-prone systems are modeled using the framework of systems with switching parameters or hybrid systems. Since a closed-form mean-square optimal filtering algorithm for this class of systems does not exist, it is of particular interest to derive a lor bound on the state estimation error covariance. The ll known Crameacuter-Rao bound is not applicable to fault-prone systems because of the discrete distribution of the fault indicators, which violates the regularity conditions associated with this bound. On the other hand, the iss-instein lor bound is essentially free from regularity conditions. Moreover, a sequential version of the iss-instein bound, suitable for Markovian dynamic systems, is presented by the authors in a companion paper. In the present paper, this sequential version is applied to several classes of fault-prone dynamic systems. The resulting bounds can be used to examine fault detectability and identifiability in these systems. Moreover, it is shown that several recently reported lor bounds for fault-prone systems are special cases of, or closely related to, the sequential version of the iss-instein lor bound"}]