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1.
《Journal of The Franklin Institute》2021,358(18):10079-10094
This paper is focused on the distributed estimation issue in the form of set-membership (SM) for a class of discrete time-varying systems suffering mix-time-delays and state-saturations. The phenomena of time-delays and state-saturations are introduced to better describe insightful engineering. During local measurements transmission between sensors over a resource-limited sensor network, to prevent data collisions and resource-consumption, a newly dynamic event-triggering strategy (DETS) is designed to dispatch the local measurements transmission for each sensor to its neighbors. Compared with the most existing static ETSs, this DETs can mitigate the total number of triggering times and enlarge interval time between consecutive triggering instants. Then, some novel adequate criteria for designing the desired event-based SM estimators are derived such that the plant’s true state always resides in each sensor’s ellipsoidal region regardless of the simultaneous presence of bounded noises, mixed time delays and state-saturations. Subsequently, a recursive optimization algorithm is formulated such that the minimal ellipsoids, the estimators gains and event-triggering weighted matrices are acquired simultaneously. A verification simulation is presented to illustrate the advantages of the design approach of the developed state estimator.  相似文献   

2.
A robust event-triggered distributed fusion algorithm is investigated in this paper for multi-sensor systems with unknown failure rates. A detection technique based on standard Gaussian distributed filtering innovation is designed and applied to judge whether the measurement is failed. This filtering innovation can also be used to construct the event-triggered condition. Specifically, the event condition is not triggered if the innovation is below the lower event-triggered threshold and the measurement is regarded as the failure measurement if the innovation exceeds the higher threshold. In the above two cases, the sensor measurement data is not transferred to the local estimator; otherwise, it will be transferred. Then, the sequential fast covariance intersection (SFCI) fusion algorithm is used for local estimation fusion. Besides, to analyze the estimation performance, sufficient conditions are given to demonstrate the boundness of the local estimation and fusion estimation covariance. Finally, a simulation example is given to show the usefulness of the presented algorithm.  相似文献   

3.
In this article, a fusion estimation scheme is proposed for stochastic uncertain systems with time-correlated fading channels (TFCs). A batch of random variables obeying Gaussian distributions is employed to describe the parameter uncertainties. The sensor communicates with the local filter through a TFC where the evolution of the channel coefficient is characterized by a certain dynamic process with one-step correlated noises. For further analyzing the effects of TFCs, a class of additional variables is first introduced by augmenting the dynamics of channel coefficients and the concerned system. Then, a new group of modified local filters is developed and the unbiasedness of local filters is examined by means of inductive method. Furthermore, the filter gains which minimize the local filtering error covariances are designed for the modified local filters in the simultaneous presence of stochastic uncertainties and TFCs. Subsequently, the cross-covariances among local estimates are computed iteratively and, based on the obtained cross-covariances as well as the unbiased local estimates and their corresponding filtering error covariances, a fusion estimate is obtained by using weighted least square fusion method. Finally, the effectiveness of the proposed fusion estimation scheme is verified by two examples.  相似文献   

4.
This paper mainly focuses on the event-based state and fault estimation problem for a class of nonlinear systems with logarithmic quantization and missing measurements. The sensors are assumed to have different missing probabilities and a constant fault is considered here. Different from a constant threshold in existing event-triggered schemes, the threshold in this paper is varying in the state-independent condition. With resort to the state augmentation approach, a new state vector consisting of the original state vector and the fault is formed, thus the corresponding state and fault estimation problem is transmitted into the recursive filtering problem. By the stochastic analysis approach, an upper bound for the filtering error covariance is obtained, which is expressed by Riccati difference equations. Meanwhile, the filter gain matrix minimizing the trace of the filtering error covariance is also derived. The developed recursive algorithm in the current paper reflects the relationship among the upper bound of the filtering error covariance, the varying threshold, the linearization error, the probabilities of missing measurements and quantization parameters. Finally, two examples are utilized to verify the effectiveness of the proposed estimation algorithm.  相似文献   

5.
In this paper, we study a distributed state estimation problem for Markov jump systems (MJS) over sensor networks, in which each sensor node connects with each other through wireless networks with communication delays. We assume that each sensor node maintains a buffer to store delayed data transmitted from neighbor nodes. A distributed multiple model filter is designed by using the interacting multiple model methods (IMM) and a recursive delays compensation method. In order to ensure the stability, two stability conditions are derived for boundedness of estimation errors and boundedness of error covariance. Finally, the effectiveness of the proposed methods is illustrated by simulations and experiments of maneuvering target tracking.  相似文献   

6.
This paper focus on the distributed fusion estimation problem for a multi-sensor nonlinear stochastic system by considering feedback fusion estimation with its variance. For any of the feedback channels, an event-triggered scheduling mechanism is developed to decide whether the fusion estimation is needed to broadcast to local sensors. Then event-triggered unscented Kalman filters are designed to provide local estimations for fusion. Further, a recursive distributed fusion estimation algorithm related with the trigger threshold is proposed, and sufficient conditions are builded for boundedness of the fusion estimation error covariance. Moreover, an ideal compromise between fusion center-to-sensors communication rate and estimation performance is achieved. Finally, validity of the proposed method is confirmed by a numerical simulation.  相似文献   

7.
This paper focuses on a state estimation problem on networked systems with Markovian packets dropout. An event-based nonuniform sampling scheme is applied in intelligent samplers to save resources of the samplers and networks. Another sampling scheme combined with time-trigger and event-trigger is applied in a Kalman filter to detect the packets dropout. A delta operator Kalman filter is designed for the nonuniform sampling networked system. Two sufficient conditions of peak covariance stability and usual covariance stability are given to guarantee convergence of the delta operator Kalman filter. Numerical examples are shown to illustrate effectiveness of the developed techniques.  相似文献   

8.
《Journal of The Franklin Institute》2019,356(17):10335-10354
This paper is devoted to investigate the designs of the event-based distributed state estimation and fault detection of the nonlinear stochastic systems over wireless sensor networks (WSNs). The nonlinear stochastic systems as well as the filters corresponding to the multiple sensors are represented by interval type-2 Takagi–Sugeno (T–S) fuzzy models. (1) A new type of fuzzy distributed filters based on event-triggered mechanism is established corresponding to the nodes of the WSN. (2) The overall stability and performance, that is mean-square asymptotic stability in H∞ sense, of the event-driven fault detection system is analyzed based on Lyapunov stability theory. (3) New techniques are developed to cope with the problem of parametric matrix decoupling for solving the distributed filter gains. (4) Finally, the desired event-based distributed filter matrices are designed subject to the numbers of the fuzzy rules and a series of matrix inequalities. A simulation case is detailed to show the effectiveness of the presented event-based distributed fault detection filtering scheme.  相似文献   

9.
For state estimation of high accuracy, prior knowledge of measurement noise is necessary. In this paper, a method for solving the joint state estimation problem of jump Markov nonlinear systems (JMNSs) without knowing the measurement noise covariance is developed. By using the Inverse-Gamma distribution to describe the dynamics of measurement noise covariance, the joint conditional posterior distribution of the state variable and measurement noise covariance is approximated by a product of separable variational Bayesian (VB) marginals. In the newly constructed approach, the interacting multiple model (IMM) algorithm, as well as the particle-based approximation strategy, is employed to handle the computationally intractable problem and the nonlinear characteristics of systems, respectively. An interesting feature of the proposed method is that the distribution of states is spanned by a set of particles with weights, while the counterpart of measurement noise covariance is obtained analytically. Moreover, the number of particles is fixed under each mode, indicating a reasonable computational cost. Simulation results based on a numerical example and a tunnel diode circuit (TDC) system are presented to demonstrate that the proposed method can estimate the measurement noise covariance well and provide satisfied state estimation when the statistics of the measurement are unavailable.  相似文献   

10.
We consider a remote state estimation process under an active eavesdropper for cyber-physical system. A smart sensor transmits its local state estimates to a remote estimator over an unreliable network, which is eavesdropped by an adversary. The intelligent adversary can work in passive eavesdropping mode and active jamming mode. An active jamming mode enables the adversary to interfere the data transmission from sensor to estimator, and meanwhile improve the data reception of itself. To protect the transmission data from being wiretapped, the sensor with two antennas injects noise to the eavesdropping link with different power levels. Aiming at minimizing the estimation error covariance and power cost of themselves while maximizing the estimation error covariance of their opponents, a two-player nonzero-sum game is constructed for sensor and active eavesdropper. For an open-loop case, the mixed Nash equilibrium is obtained by solving an one-stage nonzero-sum game. For a long term consideration, a Markov stochastic game is introduced and a Nash Q-learning method is given to find the Nash equilibrium strategies for two players. Numerical results are provided to show the effectiveness of our theoretical conclusions.  相似文献   

11.
This paper focuses on the dynamic event-based recursive filtering problem for a class of time-varying networked systems under the encoding-decoding mechanism. For the purpose of saving energy consumption, a dynamic event-triggered protocol is applied to determine whether the measurement of the sensor is transmitted or not. In the transmission process of the measurement, a dynamic-quantization-based encoding-decoding mechanism is introduced to encrypt the transmitted measurement. In specific, the measurement outputs are first encoded into codewords which are then transmitted from the encoder to the decoder. After received by the decoder, the codewords are first decoded and then sent to the filter. A bounded uncertainty is introduced to characterize the difference between the original measurement and the decoded measurement. This paper is devoted to developing a recursive filtering algorithm for the considered system such that a minimal upper bound on the filtering error covariance is derived through appropriately designing filter gain. Moreover, the mean-square exponential boundedness of the filtering error is analyzed. Finally, the efficiency and superiority of the proposed algorithm are verified through two simulation examples.  相似文献   

12.
This paper deals with the distributed estimation problem for networked sensing system with event-triggered communication schedules on both sensor-to-estimator channel and estimator-to-estimator channel. Firstly, an optimal event-triggered Kalman consensus filter (KCF) is derived by minimizing the mean squared error of each estimator based on the send-on-delta triggered protocol. Then, the suboptimal event-triggered KCF is proposed in order to reduce the computational complexity in covariance propagation. Moreover, the formal stability analysis of the estimation error is provided by using the Lyapunov-based approach. Finally, simulation results are presented to demonstrate the effectiveness of the proposed filter.  相似文献   

13.
Unpredictable packet loss that occurs in the channel connecting a local sensor and a remote estimator will deteriorate the performance of state estimation. To relieve this detrimental impact, an online linear temporal coding scheme is studied in this paper. If the packet of the last step is lost, a linear combination of the current and the last measurements with proper weights is transmitted; otherwise, only the current data is sent. By virtue of the innovation sequence approach, a linear minimum mean-squared error estimation algorithm is designed. To optimize performance, a novel estimator is also proposed which provides a recursive expression of the error covariances. The proposed two algorithms are proved to be equivalent via a set of transformations. With the aid of some optimization techniques, a recursive algorithm is presented to obtain the optimal coding weight in terms of minimizing the average estimation error covariance.  相似文献   

14.
This paper is concerned with the event-based fault detection for the networked systems with communication delay and nonlinear perturbation. We propose an event-triggered scheme, which has some advantages over existing ones. The sensor data is transmitted only when the specified event condition involving the sampled measurements of the plant is violated. An event-based fault detection model is firstly constructed by taking the effect of event-triggered scheme and the network transmission delay into consideration. The main purpose of this paper is to design an event-based fault detection filter such that, for all unknown input, communication delay and nonlinear perturbation, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions for the existence of the desired fault detection filter are established in terms of linear matrix inequalities. Based on these conditions, the explicit expression is given for the designed fault detection filter parameters. A numerical example is employed to illustrate the advantage of the introduced event-triggered scheme and the effectiveness of the proposed method.  相似文献   

15.
In this paper, the distributed fusion filtering issue is investigated for multi-sensor systems with the constraints from both time-correlated fading channels and energy harvesters. A specific scenario is considered where the sensors can harvest energy from the natural environment and may consume a certain amount of energy when transmitting measurements to the filters. In order to properly deal with the energy supply relationship between a battery and multiple sensors, a dynamic energy-allocated rule is proposed in this paper, i.e., the storage battery provides energy to sensors in order of different sensors’ priorities. Additionally, the channel fading phenomenon is also taken into consideration and the fading coefficient is described by a dynamic process. In this paper, we are committed to designing a local filter such that, under the effects of the time-correlated fading channels and energy harvesters, an upper bound on the local filtering error covariance is firstly derived by using the mathematical induction and then the upper bound is minimized by designing the local filter gain. Next, the covariance intersection approach is employed to obtain the fusion estimates. Finally, a simulation is provided to verify that the presented filtering strategy is feasible and effective.  相似文献   

16.
The problem of event-based H∞ filtering for discrete-time Markov jump system with network-induced delay is investigated in this paper. For different jumping modes, different event-triggered communication schemes are constructed to choose which output signals should be transmitted. Through the analysis of network-induced delay’s intervals, the discrete-time system, the event-triggered scheme and network-induced delay are unified into a discrete-time Markov jump filter error system with time-delay. Based on time-delay system analysis method, criteria are derived to guarantee the discrete-time Markov jump error system stochastically stable with an H∞ norm bound. The correspondent filter and the event-based parameters are also given. A numerical example is given to show that the proposed filter design techniques are effective and event-triggered communication scheme can save limited network resources greatly.  相似文献   

17.
Multi-sensor data fusion over one channel is studied in this paper. The communication constraint considered here is medium access constraint. When the synchronous time division multiplexing (STDM) mechanism is used to address this problem, collective delay emerges. Collective delay time depends upon the channel capacity and traffic flow assigned to the communication channel, causing contradiction between traffic flow and delay time (the number of transmitted sensors and delay steps). A new model is developed that can truly reflect this contradiction by introducing a stochastic process θθ. Based on the obtained system model, the optimal data fusion filter is designed. It also gives the upper bounds of the expected estimation error covariance and estimation error covariance with one-step delay. Two illustrative examples are given in the last section to show the influence of θθ on estimation performance.  相似文献   

18.
In a multimodal, system, the growth in the number of possible modal paths makes state estimation difficult. Practical algorithms bound complexity by merging estimates that are conditioned on different modal path fragments. Commonly, the weight given to these local estimates is inversely related to the normalized magnitude of the residuals generated by each local filter. This paper presents a novel dual-sensor estimator that uses a merging formula that is based upon a different function of the residuals. Its performance is contrasted with an estimator using a single sensor and with another dual-sensor algorithm that requires fewer on-line calculations.  相似文献   

19.
This paper studies centralized fusion estimation over a wireless sensor-actuator network, where packet dropouts cannot be observed by the fusion estimator. For such a system, we obtain an optimal linear fused estimation of system states, also known as optimal linear estimator. Then, we establish a necessary and sufficient condition for the stability of the optimal linear estimator. Finally, we show that the estimation performance is monotonically decreasing with respect to the observation packet-arrival rate. By analyzing a sequence that converges to the covariance of the optimal linear estimator, an analytical relationship between the estimation performance and the control packet-arrival rate is obtained. Simulation examples are given to illustrate the main results.  相似文献   

20.
This paper considers the consensus disturbance rejection problem of networks of linear agents with event-triggered communications in the presence of matched disturbances. Based on the disturbance observer, distributed event-based consensus protocols are proposed and constructed for both the cases of neutrally stable and general linear agents. Under the proposed event-based consensus protocol, it is shown that the consensus errors are asymptotically stable and the Zeno behavior can be excluded. Compared to the previous related works, our main contribution is that the proposed event-based protocol can achieve consensus and meanwhile reject disturbance, without the need of continuous communications among neighboring agents. For the case of neutrally stable agents, the event-based protocol is fully distributed, using only the local information of each agent and its neighbors. Simulation results are presented to illustrate the effectiveness of the theoretical results.  相似文献   

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