A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements

Abstract

In this paper, the recursive state estimation problem is investigated for an array of discrete time-varying coupled stochastic complex networks with missing measurements. A set of random variables satisfying certain probabilistic distributions is introduced to characterize the phenomenon of the missing measurements, where each sensor can have individual missing probability. The Taylor series expansion is employed to deal with the nonlinearities and the high-order terms of the linearization errors are estimated. The purpose of the addressed state estimation problem is to design a time-varying state estimator such that, in the presence of the missing measurements and the random disturbances, an upper bound of the estimation error covariance can be guaranteed and the explicit expression of the estimator parameters is given. By using the Riccatilike difference equations approach, the estimator parameter is characterized by the solutions to two Riccati-like difference equations. It is shown that the obtained upper bound is minimized by the designed estimator parameters and the proposed state estimation algorithm is of a recursive form suitable for online computation. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the developed state estimation scheme.

Constrained anti-disturbance control for a quadrotor based on differential flatness

“…When there are missing measurements and random disturbances, the state estimation problems with variance-constraints become concern. The new methods reported in Hu, Wang, Shen, & Gao (2013) and Hu, Wang, Liu, & Gao (2016) would be helpful for further extensions.…”

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

“…It is worth mentioning that, in [41], the authors have made the first attempt to discuss the uncertainties entering into the inner coupling matrix and introduce a new measurement model which can characterize the sensor saturations, signal quantization, and missing measurements in a unified framework. Very recently, in [153], the recursive state estimation problem has been investigated for an array of discrete time-varying coupled stochastic complex networks with missing measurements. By using the Riccati-like difference equations approach, new state estimation algorithm with covariance constraint has been developed for the first time and the estimator parameter has been characterized by the solutions to two Riccati-like difference equations.…”

Multiple Model Ballistic Missile Tracking With State-Dependent Transitions and Gaussian Particle Filtering

“…From the algorithm development aspect, we will investigate the combination of the state-dependent model switching-based multiple model framework with other filtering techniques to deal with the particle loss problem, such as the particle flow algorithm as in [29] or exploiting various numbers of particles in every mode for filtering. Finally, we will consider a more challenging scenario as in [30] and [31], to track the BM by a sensor-networked system considering the possible network-induced phenomena such as missing/fading measurements, sensor saturations, communication delays, and randomly occurring incomplete information.…”