Finite-Horizon $H_{\infty} $ Filtering With Missing Measurements and Quantization Effects

Variance-constrainedH∞control for a class of nonlinear stochastic discrete time-varying systems: The event-triggered design

“…It is worth pointing out that most results concerning the CC theory have focused on the steady-state behaviors for time-invariant systems over an infinite horizon. However, virtually almost all real-time control processes are time-varying especially when the noise inputs are nonstationary [10,12]. In such cases, it would make more sense to consider the covariance control problems for time-varying systems over a finite-horizon in order to provide a better transient performance.…”

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

“…During the past two decades, among the probabilistic ways for modeling the missing measurements, the Bernoulli probability distribution has been extensively employed due to its simplicity and practicality, where the Bernoulli random variable takes value on 1 representing the perfect signal delivery and it takes value on 0 standing for the measurement missing. Accordingly, many important papers have been published concerning on the estimation, filtering and fusion for networked systems based on several methods such as the linear matrix inequality method [25], difference linear matrix inequality method [27], innovation analysis approach [30], Hamilton-Jacobi-Isaacs inequality approach [35], and backward/forward Riccati difference equation method [7], [37]. When comparing between different approaches, it is worth mentioning that the linear matrix inequality (difference linear matrix inequality) method is applicable for the analysis problem of time-invariant (time-varying) linear/nonlinear networked complex dynamical systems and gives the feasible solutions, the innovation analysis approach is suitable for handling the analysis problem of linear time-invariant/time-varying networked systems and can provide the optimal solutions in the minimum mean-square error sense, the Hamilton-JacobiIsaacs inequality approach is helpful for addressing the analysis and synthesis problems of time-invariant networked systems with general nonlinearities but it is commonly difficult to obtain the feasible solution, and backward/forward Riccati difference equation method has the advantage to deal with the analysis and synthesis problem for time-varying linear/nonlinear networked systems and provide the sub-optimal solutions.…”

“…Linear matrix inequality method time-invariant complex dynamical systems feasible [25], [40]- [44] Difference linear matrix inequality method time-varying complex dynamical systems feasible [21], [27], [45], [46] Innovation analysis approach linear time-invariant/time-varying systems optimal [30]- [32], [39], [47], [48] Hamilton-Jacobi-Isaacs inequality approach general nonlinear time-invariant systems feasible [35] Backward Riccati difference equation method nonlinear time-varying systems sub-optimal [37], [49], [50] Forward Riccati difference equation method nonlinear time-varying systems sub-optimal [7], [16], [51]- [54] [49], [56], [57], the N-order Rice fading channel has been modeled by sequences of independent and identically distributed Gaussian random variables with known means and variances, where the multi-path induced fading stemming mainly from multi-path propagation has been considered when dealing with the control and estimation problems for networked systems and the impact from the fading measurements onto the control/estimation performance has been examined.…”

“…In [147], an optimal filtering algorithm has been given for linear time-varying system in the presence of the stochastic sensor gain degradations. Very recently, by using the backward Riccati equation method, an effective H ∞ filtering scheme has been presented in [37] to handle the missing measurements and quantization effects in a same framework, and the developed result has been applied to address the mobile robot localization problem.…”

Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms

“…To overcome the challenges from unreliable network environments, we modify the broadcast gossip algorithm [6]- [8] to estimate the unknown parameter associated with incompletely informative samples [4] with noise measured by local sensors and communicated through quantization channels. In a round, each randomly selected sensor broadcasts its quantized estimate to its one-hop neighbors, then each neighbor processes its H. Wang measurement and updates its estimate through local information exchange if it receives only one broadcasted message, and the remaining sensors sustain their estimates.…”