共查询到20条相似文献，搜索用时 93 毫秒
1.
《Journal of The Franklin Institute》2023,360(4):2457-2475
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
2.
In this paper, a novel distributed Kalman filter consisting of a bank of interlaced filters is proposed for a signal model whose dynamic equation and measurement equation are coupled. Each of the interlaced filters estimates a part of state rather than the global state using its and its neighbor information, which is different from other distributed filters already existed (e.g., distributed Kalman filter based on diffusion strategy or consensus strategy, distributed fuzzy filter and distributed particle filter with Gaussian mixer approximation, etc). This relieves the calculation and communication burden in networks. In addition, the proposed distributed Kalman filtering contains no consensus strategies, which is useful in some cases since consensus usually requires an infinite number of iterations. 相似文献
3.
Zhe Gao 《Journal of The Franklin Institute》2018,355(2):922-948
This study proposes fractional-order Kalman filers using Tustin generating function and the average value of fractional-order derivative to estimate the state of fractional-order systems involving colored process and measurement noises. By Tustin generating function, a fractional-order differential equation is provided to approximate the dynamics of a continuous-time fractional-order system and colored process and measurement noises. By constructing an augmented system with respect to state, the process noise and the measurement noise to deal with colored noises, the fractional-order Kalman filter using Tustin generating function is proposed to improve the estimation accuracy. Besides, the average value of fractional-order derivative is proposed, and the corresponding fractional-order Kalman filter by the augmented system method is presented to reduce estimation error. Finally, three illustrative examples are given to illustrate that the proposed two kinds of Kalman filters are more effective than fractional-order Kalman filter based on Grnwald–Letnikov definition. 相似文献
4.
This paper considers the identification problem of bilinear systems with measurement noise in the form of the moving average model. In particular, we present an interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle. For unknown states, we formulate a novel bilinear state observer from input-output measurements using the Kalman filter. Then a bilinear state observer based multi-innovation extended stochastic gradient (BSO-MI-ESG) algorithm is proposed to estimate the unknown system parameters. A linear filter is utilized to improve the parameter estimation accuracy and a filtering based BSO-MI-ESG algorithm is presented using the data filtering technique. In the numerical example, we illustrate the effectiveness of the proposed identification methods. 相似文献
5.
Peng Yun Panlong Wu Xingxiu Li Shan He Kai Wu 《Journal of The Franklin Institute》2021,358(8):4503-4527
For target tracking systems, the probability of detecting a target is difficult to determine, and the process noise often has non-Gaussian heavy-tailed characteristics owing to interference from outliers. To address the issues associated with single target tracking within clutters in scenarios with an unknown detection probability and heavy-tailed process noise, this paper presents a variational Bayesian-based adaptive probabilistic data association filter (VB-APDAF). The beta distribution, Pearson type VII distribution and multinomial distribution are used to model the detection probability, the process noise, and the association events, respectively. To guarantee the conjugation, a novel parameter estimation strategy is employed. In this strategy, the previous state is introduced in the state update process to construct the joint probability density function of parameters to be estimated and data set. The VB framework is used to estimate the target state, detection probability, and associated events. An experiment was performed under simulated conditions to demonstrate the effectiveness of the proposed filter. 相似文献
6.
《Journal of The Franklin Institute》2023,360(9):6515-6536
The performance of the current state estimation will degrade in the existence of slow-varying noise statistics. To solve the aforementioned issues, an improved strong tracking maximum correntropy criterion variational-Bayesian adaptive Kalman filter is presented in this paper. First of all, the inverse-Wishart distribution, as the conjugate-prior, is adopted to model the unknown and time-varying measurement and process noise covariances, then the noise covariances and system state are estimated via the variational Bayesian method. Secondly, the multiple fading-factors are obtained and evaluated to modify the prediction error covariance matrix to address the problems associated with inaccurate error estimation. Finally, the maximum correntropy criterion is employed to correct the filtering gain, which improves the filtering performance of the proposed algorithm. Simulation results show that the proposed filter exhibits better accuracy and convergence performance compared to other existing algorithms. 相似文献
7.
《Information processing & management》2022,59(2):102880
Predictive computation now is a more and more popular paradigm for artificial intelligence. In this article, we discuss how to design a privacy preserving computing toolkit for secure predictive computation in smart cities. Predictive computation technology is very important in the management of cloud data in smart cities, which can realize intelligent computing and efficient management of cloud data in the city. Concretely, we propose a homomorphic outsourcing computing toolkit to protect the privacy of multiple users for predictive computation. It can meet the needs of large-scale users to securely outsource their data to cloud servers for storage, management and processing of their own data. This toolkit, using the Paillier encryption system and Lagrangian interpolation law, can implement most commonly basic calculations such as addition, subtraction, multiplication and division etc. It can also implement secure comparison of user data in the encrypted domain. In addition, we discuss how to implement the derivative of polynomial functions using our homomorphic computing encryption tool. We also introduce its application in neural networks. Finally, we demonstrate the security and efficiency of all our protocols through rigorous mathematical analysis and performance analysis. The results show that our toolkit is efficient and secure. 相似文献
8.
A fault tolerant control scheme for actuator and sensor faults is proposed for a tilt-rotor unmanned aerial vehicle (UAV) system. The tilt-rotor UAV has a vertically take-off and landing (VTOL) capability like a helicopter during the take-off & landing while it could cruise with a high speed as a conventional airplane flight mode. A dual system in the flight control computer (FCC) and the sensor is proposed in this study. To achieve a high reliability, a fault tolerant flight control system is required for the case of actuator or sensor fault. For the actuator fault, the fault tolerant control scheme based on model error control synthesis is presented. A designed fault tolerant control scheme does not require system identification process and it provides an effective reconfigurability without fault detection and isolation (FDI) process. For the sensor fault, the fault tolerant federated Kalman filter is designed for the tilt-rotor UAV system. An FDI algorithm is applied to the federated Kalman filter in order to improve the accuracy of the state estimation even when the sensor fails. For a linearized six-degree-of-freedom linear model and nonlinear model of the tilt-rotor UAV, numerical simulation and process-in-the-loop simulation (PILS) are performed to demonstrate the performance of the proposed fault tolerant control scheme. 相似文献
9.
Accurate and effective state estimation is essential for nonlinear fractional system, since it can provide some vital operation information about the system. However, inevitably missing measurements and additive uncertainty in the gain will affect the performance of estimation result. Thus, in this paper, in order to deal with these problems, a novel robust extended fractional Kalman filter (REFKF) is developed for states estimation of nonlinear fractional system, by which the states can be estimated accurately even with missing measurements. Finally, simulation results are provided to demonstrate that the proposed method can achieve much better estimation performance than the conventional extended fractional Kalman filter (EFKF). 相似文献
10.
Nonuniform sampling Kalman filter for networked systems with Markovian packets dropout 总被引：1，自引：1，他引：0
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
11.
12.
《Journal of The Franklin Institute》2023,360(4):3407-3435
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
13.
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
14.
《Journal of The Franklin Institute》2023,360(7):4626-4639
This study considers state and fault estimation for a switched system with a dual noise term. A zonotopic and Gaussian Kalman filter for state estimation is designed to obtain state estimation interval in the presence of both stochastic and unknown but bounded (UBB) uncertainties. The switching state and fault state of the system are distinguished by detecting whether the system measurement date is within the bounds of its predicted output. Once the switched time is detected in the system, the filter zonotopic and Gaussian Kalman functions are initialized. Once the fault time is detected, a zonotopic and Gaussian Kalman filter-based fault estimator is constructed to estimate the corresponding faults. Finally, a numerical simulation is presented to demonstrate the accuracy and effectiveness of the proposed algorithm. 相似文献
15.
《Journal of The Franklin Institute》2022,359(16):9220-9240
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
16.
《Journal of The Franklin Institute》2022,359(2):1649-1666
This paper investigates the event-based state and fault estimation problem for stochastic nonlinear system with Markov packet dropout. By introducing the fictitious noise, the fault is augmented to the system state. Then combining the unscented Kalman filter (UKF) with event-triggered and Markov packet dropout, the modified UKF is proposed to estimate the state and fault. Meanwhile, the stochastic stability of the proposed filter is also discussed. Finally, two simulation results illustrate the performance of the proposed method. 相似文献
17.
《Journal of The Franklin Institute》2022,359(18):10726-10740
In this paper, the secure transmission issue of a remote estimation sensor network against eavesdropping is studied. A powerful eavesdropper overhears the measurement data sent through the communication channels between the sensors and the remote estimator, and estimates system state illegally, which threatens the system information security. Different from the existing anti-eavesdropping design approaches, a stealthy artificial noise (AN) strategy is proposed to prevent eavesdroppers from deciphering encryption policy by hiding the encryption process from eavesdroppers. It has the same dynamical process with each sensor’s measurement to guarantee that the estimation error of the eavesdropper is unbounded while its observation residual variance keeps in certain bound and converges to 0, and further ensure system security without alerting the eavesdropper. It is proved that the strategy is feasible whenever the eavesdropper starts to wiretap. The selection of sensors that needs to be encrypted is further given by solving an optimization problem. The effectiveness of the proposed algorithm is verified by two simulation examples. 相似文献
18.
《Journal of The Franklin Institute》2023,360(5):3575-3591
In this paper, we study the problem of remote state estimation on networks with random delays and unavailable packet sequence due to malicious attacks. Two maximum a posteriori (MAP) schemes are proposed to detect the unavailable packet sequence. The first MAP strategy detects the packet sequence using data within a finite time horizon; the second MAP strategy detects the packet sequence by a recursive structure, which effectively reduces the computation time. With the detected packet sequence, we further design a linear minimum mean-squared error (LMMSE) estimation algorithm based on smoothing techniques, rather than using the classic prediction and update structure. A wealth of information contained in the combined measurements is utilized to improve the estimation performance. Finally, the effectiveness of the proposed algorithms is demonstrated by simulation experiments. 相似文献
19.
《Journal of The Franklin Institute》2022,359(3):1303-1326
The desensitized Kalman filter Karlgaard and Shen (2013)[1] is a practical and intuitive robust filtering method. However, a thorough analysis of its stability and impact of assumptions is missing. This paper expands the theory of desensitized Kalman filtering by proposing a stochastic approach to reduce estimation error sensitivity to parameters. The novel approach leads to the exact desensitized Kalman filter that does not neglect the gain sensitivity to a parameter. The suboptimal form equivalent to the original desensitized Kalman filter in a special form is proposed. The stability analysis and the definition of stability conditions are possible due to the proposed form that can be interpreted as the Kalman filter with correlated process and measurement noise with time-variant statistics. Furthermore, adaptive normalization of objectives is introduced, which improves the desensitizing performance. 相似文献
20.
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