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1.
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   

2.
In this paper, a dynamically event-triggered filtering problem is investigated for a class of discrete time-varying systems with censored measurements and parameter uncertainties. The censored measurements under consideration are described by the Tobit measurement model. In order to save the communication energy, a dynamically event-triggered mechanism is utilized to decide whether the measurements should be transmitted to the filter or not. The aim of this paper is to design a robust recursive filter such that the filtering error covariance is minimized in certain sense for all the possible censored measurements, parameter uncertainties as well as the effect induced by the dynamically event-triggered mechanism. By means of the mathematical induction, an upper bound is firstly derived for the filtering error covariance in terms of recursive matrix equations. Then, such an upper bound is minimized by designing the filter gain properly. Furthermore, the boundedness is analyzed for the minimized upper bound of the filtering error covariance. Finally, two numerical simulations are exploited to demonstrate the effectiveness of the proposed filtering algorithm.  相似文献   

3.
This paper is concerned with the event-based fusion estimation problem for a class of multi-rate systems (MRSs) subject to sensor degradations. The MRSs under consideration include several sensor nodes with different sampling rates. To facilitate the filter design, the MRSs are transformed into a single-rate system (SRS) by using an augmentation approach. A set of random variables obeying known probability distributions are used to characterize the phenomenon of the sensor degradations. For the purpose of saving the limited communication resources, the event-triggering mechanism (ETM) is adopted to regulate the transmission frequency of the measurements. For the addressed MRSs, we aim to design a set of event-based local filters for each sensor node such that the upper bound of each local filtering error covariance (FEC) is guaranteed and minimized by designing the filter parameter appropriately. Subsequently, the local estimates are fused with the aid of covariance intersection (CI) fusion approach. Finally, a numerical experiment is exploited to demonstrate the usefulness of the developed fusion estimation algorithm.  相似文献   

4.
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   

5.
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   

6.
This paper studies the distributed Kalman consensus filtering problem based on the event-triggered (ET) protocol for linear discrete time-varying systems with multiple sensors. The ET strategy of the send-on-delta rule is employed to adjust the communication rate during data transmission. Two series of Bernoulli random variables are introduced to represent the ET schedules between a sensor and an estimator, and between an estimator and its neighbor estimators. An optimal distributed filter with a given recursive structure in the linear unbiased minimum variance criterion is derived, where solution of cross-covariance matrix (CCM) between any two estimators increases the complexity of the algorithm. In order to avert CCM, a suboptimal ET Kalman consensus filter is also presented, where the filter gain and the consensus gain are solved by minimizing an upper bound of filtering error covariance. Boundedness of the proposed suboptimal filter is analyzed based on a Lyapunov function. A numerical simulation verifies the effectiveness of the proposed algorithms.  相似文献   

7.
This paper investigates the distributed state estimation problem for a linear time-invariant system characterized by fading measurements and random link failures. We assume that the fading effect of the measurements occurs slowly. Additionally, communication failures between sensors can affect the state estimation performance. To this end, we propose a Kalman filtering algorithm composed of a structural data fusion stage and a signal date fusion stage. The number of communications can be decreased by executing signal data fusion when a global estimate is required. Then, we investigate the stability conditions for the proposed distributed approach. Furthermore, we analyze the mismatch between the estimation generated by the proposed distributed algorithm and that obtained by the centralized Kalman filter. Lastly, numerical results verify the feasibility of the proposed distributed method.  相似文献   

8.
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   

9.
A new distributed fusion receding horizon filtering problem is investigated for uncertain linear stochastic systems with time-delay sensors. First, we construct a local receding horizon Kalman filter having time delays (LRHKFTDs) in both the system and measurement models. The key technique is the derivation of recursive error cross-covariance equations between LRHKFTDs in order to compute the optimal matrix fusion weights. It is the first time to present distributed fusion receding horizon filter for linear discrete-time systems with delayed sensors. It has a parallel structure that enables processing of multisensory time-delay measurements, so the calculation burden can be reduced and it is more reliable than the centralized version if some sensors turn faulty. Simulations for a multiple time-delays system show the effectiveness of the proposed filter in comparison with centralized receding horizon filter and non-receding versions.  相似文献   

10.
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   

11.
In this paper, a study on the end-to-end performance of dual-hop non-regenerative relaying over independent generalized-K (KG) fading channels is presented. Using a suitable upper bound for the end-to-end signal-to-noise ratio (SNR), novel closed-form expressions for the cumulative distribution function (CDF), probability density function (PDF) and the moments of this bounded SNR are derived. These results can be afterwards used to obtain important performance metrics of the considered system such as the outage probability and the error performance of digital modulation schemes. In the case of independent but non-necessarily identical fading channels, lower bounds for the average bit error probability (ABEP) for different modulation schemes are determined by using the Padé approximants method. For the case of identical fading channels, closed-form lower bounds for the ABEP are derived. Various numerical and computer simulation results illustrate the proposed analysis.  相似文献   

12.
In this paper, the measurement outlier-resistant target tracking problem is investigated in wireless sensor networks (WSNs) with energy harvesting constraints. Each WSN node can acquire energy stochastically from surroundings. No matter whether the WSN node acquires energy or not, the WSN node’s measurement can be transmitted if the energy amount of the WSN node is greater than zero. In such a case, the sensor energy-induced missing measurement (SE-IMM) phenomenon may occur. The objective of this paper is to develop a solution for the considered target tracking problem by devising the filter including a saturation constraint such that, in the simultaneous presence of outliers and the SE-IMM phenomenon, the tracking performance can meet the given performance index. Firstly, the relation between the energy level of the WSN node and its probability distribution is computed recursively. Then, an upper bound of the tracking error covariance is derived which is minimized by appropriately choosing the filter parameter. Finally, the feasibility of the proposed target tracking scheme is validated by conducting a set of comparative experiments and the relationship between the energy of the WSN node and the tracking performance is also disclosed.  相似文献   

13.
This paper studies networked H∞ filtering for Takagi–Sugeno fuzzy systems with multi-output multi-sensor asynchronous sampling. Different output variables in a dynamic system are sampled by multiple sensors with different sampling rates. To estimate the signals of such a system, a continuous multi-rate sampled-data fusion method is proposed to design a novel networked filter. By considering a class of decentralized event-triggered transmission schemes, multi-channel network-induced delays, and the updating modes of the MOMR sampled-data, a networked jumping fuzzy filter is proposed to estimate system signals based on the transmitted multi-rate sampled-data of fuzzy system and the multi-rate sampled states of filter, and the jumping among filter modes is governed by a Markov process which depends on the arrival times of sampled output sub-vectors. To deal with asynchronous membership functions, the networked fuzzy filtering system is modeled as an uncertain fuzzy stochastic system with membership function deviation bounds. Based on stability and H∞ performance analysis, several membership-function-dependent conditions are presented to co-design the event-triggered transmission schemes and the fuzzy filter such that the filtering error system is robustly mean-square exponentially stable with a prescribed H∞ attenuation level. Finally, the improvement in estimation performance and comparison with the existing filtering methods are discussed through simulation examples.  相似文献   

14.
This technical note is concerned with particle filter for the discrete-time nonlinear networked control system. First, modified particle filter algorithm with Markovian packet dropout and time delay is proposed, and its error covariance is benchmarked by Markovian Cramér-Rao lower bound. Second, an upper bound of the Markovian Cramér-Rao lower bound is presented for some special nonlinear networked systems. Third, some necessary conditions for the boundness of error covariance are given by obtaining some sufficient conditions for the bounded Markovian Cramér-Rao lower bound. Finally, numerical examples are presented to illustrate the efficiency of proposed particle filter.  相似文献   

15.
This paper considers the filtering problem for a class of linear cyber-physical systems (CPSs) subject to the Round-Robin protocol (RRP) scheduling, where the RRP is adopted to efficiently avoid data collisions in multi-sensor application scenarios. Unlike most of the existing results concerning the scheduling effects of the RRP under reliable communication channels, the filtering problem over packet-dropping networks is investigated. In such a framework, an optimal Kalman-type recursive filter is derived in the minimum mean square error (MMSE) sense, which is different from the suboptimal filters with bounded error covariances proposed in the previous results. Due to the protocol-induced behaviors and the unreliability of the channels, the estimator may be unstable. Thus, the stability problem of the filter is mainly discussed. It can be proved that the filter is stable when the arrival rate of the measurements exceeds a certain threshold, where the threshold can be obtained by solving a quasi-convex optimization problem. Furthermore, a sufficient condition for the existence of the steady-state error covariance is presented and can be transferred into the feasibility of a certain linear matrix inequality (LMI). Finally, a simulation example is provided to demonstrate the developed results.  相似文献   

16.
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   

17.
This paper is concerned with a security problem about malicious integrity attacks in state estimation system, in which multiple smart sensors locally measure information and transmit it to a remote fusion estimator though wireless channels. A joint constraint is considered for the attacker behaviour in each channel to keep stealthiness under a residual-based detector on the remote side. In order to degrade the estimator performance, the attacker will maximize the trace of the remote state estimation error covariance which is derived based on Kalman filter theory. It is proved that the optimal linear attack strategy design problem is convex and finally turned into a semi-definite programming problem. In addition, the tendency of attack behaviour on recursive and fixed Kalman filter system is analyzed. Several examples are given to illustrate the theoretical results.  相似文献   

18.
In this paper, a theoretical analysis of diversity incorporated Variable Energy Adaptation (VEA) in an Asynchronous Code Division Multiple Access (A-CDMA) system is discussed for Rayleigh, Rician and Nakagami slow fading channels. The adaptation is accomplished by providing the receiver with the capability of measuring the signal energy-to-noise ratio, and controlling the transmitted signal energy by means of a noise-free feedback loop. System parameters such as fading margin, maximum signal-to-noise ratio, and mean transmitter energy gain are derived and plotted for fading channels as a function of the probability of error specification and the probability of unsatisfactory operation. The mean and median probabilities of error are plotted as a function of energy-to-noise ratios for different fading channels. Error probability distribution and density functions are derived and plotted for various signal-fading distributions.  相似文献   

19.
In this paper, the problem of asynchronous H∞ filtering for singular Markov jump systems with redundant channels under the event-triggered scheme is studied. In order to save the resource of bandwidth limited network and improve quality of data transmission, we utilize event-triggered scheme and employ redundant channels. The redundant channels are modeled as two mutually independent Bernoulli distributed random variables. To formulate the asynchronization phenomena between the system modes and the filter modes, the hidden Markov model is proposed so that the filtering error system has become a singular hidden Markov jump system. The criterion of regular, causal and stochastically stable with a certain H∞ performance for the filtering error system has been obtained. The co-design of asynchronous filter and the event-triggered scheme is proposed in terms of a group of feasible linear matrix inequalities. Two examples are given to show the effectiveness of the proposed method.  相似文献   

20.
In this paper, we investigate the optimal local sensor decision rule based on non-ideal transmission channels between local sensors and the fusion center for distributed target detection system. The optimality of a likelihood-ratio test (LRT)-based local decision rule at local sensor, which requires only the knowledge of channel statistics instead of instantaneous channel state information (CSI), is established. The coupled local decision rule at each sensor is derived in a closed-form for coherent BPSK and OOK and non-coherent OOK. The iterative person-by-person optimization (PBPO) algorithm is employed to solve the coupled local thresholds. Simulation analysis reveals that the derived thresholds according to the local decision rule are consistent to the exhaustive searching. Furthermore, the detection performance of the system with the proposed optimal local decision rule for different reception modes and modulations is analyzed and compared.  相似文献   

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