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In this article, the recursive Bayesian estimation problem is investigated for a class of linear discrete-time systems subject to state-dependent packet dropouts. During the transmission to a remote estimator, the data packets carrying the local measurements might be dropped if the system state is located within certain occlusion region, and this gives rise to a nonstationary dropout process relying on real system states. In this scenario, due to the exponential growth of the computational cost, it is almost impossible to calculate the exact posterior distribution of the system state for the purpose of optimal state estimation. To address this issue, we propose a novel cross-coupled estimation framework consisting of two interactively working estimators, namely, a region-label estimator and a state estimator, where the former is utilized to obtain the optimal estimates of the region-label sequence in the maximum a posteriori sense, while the latter is adopted to achieve the optimal estimates of the system states in the minimum mean-square error sense. Moreover, a sufficient condition is obtained to ensure the mean-square boundedness of the resultant estimation error. The effectiveness of the proposed cross-coupled estimation framework is verified by a numerical simulation example.

This article is concerned with the secure state estimation problem for a networked system with multirate measurements. The measurement data are transmitted to the estimator over a communication network, where an eavesdropper could intercept the transmitted information and generate the state estimates based on the overheard signals. In order to protect the privacy of the system state from information leakage, a novel encryptor is proposed to encrypt the measurement signals into codewords before being transmitted, and a decryptor is then adopted at the user side to decrypt the received codewords into the desired measurement signals, based on which a recursive state estimator is developed whose parameter is calculated by solving several coupled difference equations. Here, the secret key for decryption is available to the user but is unknown to the eavesdropper. The encryptor and the estimator are codesigned such that 1) at the user side, the estimation error covariance has an upper bound that is subsequently minimized in the sense of the matrix trace, where the uniform boundedness of the error covariance is also examined; and 2) at the eavesdropper side, the corresponding state estimation error is divergent. Two simulation examples are provided to demonstrate the effectiveness of our developed secure state estimation scheme.

This technical note is concerned with the finite-horizon \cal H\infty fault estimation problem for a class of nonlinear stochastic time-varying systems with both randomly occurring faults and fading channels. The system model (dynamical plant) is subject to Lipschitz-like nonlinearities and the faults occur in a random way governed by a set of Bernoulli distributed white sequences. The system measurements are transmitted through fading channels described by a modified stochastic Rice fading model. The purpose of the addressed problem is to design a time-varying fault estimator such that, in the presence of channel fading and randomly occurring faults, the influence from the exogenous disturbances onto the estimation errors is attenuated at the given level quantified by a \cal H\infty-norm in the mean square sense. By utilizing the stochastic analysis techniques, sufficient conditions are established to ensure that the dynamic system under consideration satisfies the prespecified performance constraint on the fault estimation, and then a recursive linear matrix inequality approach is employed to design the desired fault estimator gains. Simulation results demonstrate the effectiveness of the developed fault estimation design scheme. [ABSTRACT FROM PUBLISHER]

In this paper, a new H\infty filtering approach is developed for a class of discrete time-varying systems subject to missing measurements and quantization effects. The missing measurements are modeled via a diagonal matrix consisting of a series of mutually independent random variables satisfying certain probabilistic distributions on the interval [0,1]. The measured output is quantized by a logarithmic quantizer. Attention is focused on the design of a stochastic H\infty filter such that the H\infty estimation performance is guaranteed over a given finite-horizon in the simultaneous presence of probabilistic missing measurements, quantization effects as well as external non-Gaussian disturbances. A necessary and sufficient condition is first established for the existence of the desired time-varying filters in virtue of the solvability of certain coupled recursive Riccati difference equations (RDEs). Owing to its recursive nature, the proposed RDE approach is shown to be suitable for online application without the need of increasing the problem size. The simulation experiment is carried out for the mobile robot localization problem with non-Gaussian disturbances, missing measurements and quantization effects. The effectiveness of the proposed method is demonstrated in the numerical example. [ABSTRACT FROM PUBLISHER]