Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Kalman filter is a porful tool in target tracking and self-localization across wireless sensor networks with many constraints.  considers the filters design for networked systems with combined constraints of bandwidth and random delay, and proposes a kind of universal networked Kalman estimator for given linear time invariant (LTI) or determinate parameters (DP) systems, which are also called linear and determinate parameters (LDP) systems jointly. Firstly, two modeling ways to treat bit quantization error vector are given and the corresponding LDP systems taken after bit quantization are established. Secondly,  introduce an equivalent ighted summation form of the conventional linear Kalman filter, which fully uses two essential characteristics, namely offline computation property from the LDP systems and linear ighted summation property from the linear minimum mean square error estimate. Finally, by adopting the replacement and innovation compensation operations, a kind of integrated networked estimator, involving two estimation algorithms with different modeling ways to bit quantization error actually, is presented based on the ighted summation filter for random delay systems. The proposed universal networked estimation algorithms have some outstanding advantages such as extensive application, concise algorithm structure, high estimate accuracy, and good running performance. Several examples are demonstrated to validate the proposed networked estimation algorithms.
['Quanbo Ge', 'Chenglin Wen', 'Tingliang Xu']
I am writing a research paper on a idea Networked Kalman filtering with combined constraints of bandwidth and random delay
[{"from": "human", "value": "I am writing a research paper on a idea Networked Kalman filtering with combined constraints of bandwidth and random delay"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Kalman filter is a porful tool in target tracking and self-localization across wireless sensor networks with many constraints.  considers the filters design for networked systems with combined constraints of bandwidth and random delay, and proposes a kind of universal networked Kalman estimator for given linear time invariant (LTI) or determinate parameters (DP) systems, which are also called linear and determinate parameters (LDP) systems jointly. Firstly, two modeling ways to treat bit quantization error vector are given and the corresponding LDP systems taken after bit quantization are established. Secondly,  introduce an equivalent ighted summation form of the conventional linear Kalman filter, which fully uses two essential characteristics, namely offline computation property from the LDP systems and linear ighted summation property from the linear minimum mean square error estimate. Finally, by adopting the replacement and innovation compensation operations, a kind of integrated networked estimator, involving two estimation algorithms with different modeling ways to bit quantization error actually, is presented based on the ighted summation filter for random delay systems. The proposed universal networked estimation algorithms have some outstanding advantages such as extensive application, concise algorithm structure, high estimate accuracy, and good running performance. Several examples are demonstrated to validate the proposed networked estimation algorithms."}]