Research On Recursive Filtering For Nonlinear Systems Under Low Duty Cycle Transmission Mechanism

In recent years,sensor networks are successfully applied in many practical fields.However,for sensor networks,energy consumption is always been a key issue limiting their service life and efficiency.The designed schemes based on low duty cycle transmission mechanisms can help sensor nodes achieve more efficient energy utilization.In this case,how to accurately estimate the state of nonlinear systems under the low duty cycle transmission mechanism is an extremely challenging problem.The first challenge of this study is the data sparsity problem caused by the low duty cycle transmission mechanism,the second challenge is channel fading and denial of service attacks in the information transmission process,and the third challenge is the linearization problem of nonlinear systems.Therefore,designing suitable recursive filters for the above problems is important theoretical significance and application value.This paper adopts a collaborative prediction algorithm to compensate for sparse data and explores the filtering problem of nonlinear systems under channel fading and denial of service attacks.To improve the filtering accuracy,a nonlinear transformation algorithm is used to improve the linearization process of nonlinear functions.Specifically,the main research contents of this paper are as follows:A novel scheme for recursive filtering of nonlinear systems under low duty cycle transmission mechanisms is proposed in Chapter 3.This scheme combines collaborative prediction algorithms and zero-order holders in the construction of recursive filters and effectively solves the data sparsity problem caused by low duty cycle transmission mechanisms.Then,by designing a suitable filter gain,the desired filtering performance is achieved,and it is proven that the filtering error covariance upper bound matrix is bounded,thereby improving the filtering performance while reducing energy consumption.Finally,a numerical simulation experiment verifies the effectiveness and reliability of the scheme.Therefore,the recursive filtering scheme for nonlinear systems based on collaborative prediction and zero-order holders proposed in Chapter 3 can be applied to situations with sparse data and provides theoretical support for further practical applications.In Chapter 4,in the presence of denial of service attacks,a recursive filtering algorithm is proposed for nonlinear systems under the low duty cycle transmission mechanism.First,the corresponding recursive filter is constructed for low duty cycle transmission mechanisms and denial of service attacks.At the same time,a nonlinear transformation algorithm is used to linearize the observation function and system function,and the estimation error is minimized by designing filtering gain.Then,the boundedness of the error covariance is analyzed,that is,the filtering error is controlled within a certain range,thus ensuring the accuracy and reliability of the filtering algorithm.Finally,in order to verify the effectiveness of the proposed filtering algorithm,a numerical simulation experiment is conducted in the presence of denial of service attacks.The results show that the recursive filtering method for nonlinear systems based on collaborative prediction algorithms and nonlinear transformation algorithms can accurately estimate system states and has a good ability to resist denial of service attacks.Chapter 5 studies the recursive filtering problem of nonlinear systems in the presence of time-varying channel fading under low duty cycle transmission mechanisms.First,the background and importance of the problem are described,and the establishment process of the recursive filter is introduced.Next,the design process of the filter gain and the linearization process of the nonlinear function are discussed in detail.Then,the mean square exponential boundedness of the filtering error is analyzed,and sufficient conditions are given to ensure that the estimation error is within an acceptable range.Finally,two numerical simulation experiments are used to verify the effectiveness of the proposed method.The simulation results show that the method can effectively reduce the impact of time-varying channel fading and improve signal quality and reliability.Therefore,the research results of Chapter 5 provide a novel solution for signal filtering problems under channel fading and data sparsity conditions,which is also of great practical value.