To measure system states and local environment directly with high precision, expensive sensors are required. However, highly accurate system states and environmental perception can also be achieved using data fusion techniques and digital maps. One crucial task of multi-sensor state estimation is to project different sensor measurements into the same temporal, spatial and physical domain, estim...

We investigate properties of Bayesian networks (BNs) in the context of state estimation. We introduce a coarse perspective on the inference processes and use this perspective to identify conditions under which state estimation with BNs can be very robust, even if the quality of the model is very low. By making plausible assumptions we can formulate asymptotic properties of the estimation perfor...

Numerous upcoming NASA misions need to land safely and precisely on planetary bodies. Accurate and robust state estimation during the descent phase is necessary. Towards this end, we have developed a new approach for improved state estimation by augmenting traditional inertial navigation techniques with image-based motion estimation (IBME). A Kalman filter that processes rotational velocity and...

Robustness is an important performance index of power system state estimation, which defined as the estimator’s capability to resist interference. However, improving robustness estimation often reduces accuracy. To solve this problem, paper proposes a method for generalized M-estimation optimized parameters based on sampling. Compared with traditional robust estimator, M-estimator projection st...

Kalman filtering has been widely considered for dynamic state estimation in smart grids. Despite its unique merits, the Kalman Filter (KF)-based dynamic state estimation can be undesirably influenced by cyber adversarial attacks that can potentially be launched against the communication links in the Cyber-Physical System (CPS). To enhance the security of KF-based state estimation, in this paper...

In today’s cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares (WLS) one. However, due to nonconvexity and nonsm...

Microalgae cultivation is increasingly applied in view of their various applications, ranging from renewable energy to comestics, food and wastewater treatment. At the industrial level, microalgae are usually cultivated in bioreactors, speci cally designed to ensure an optimal distribution of light and nutrients, and called photobiorecators (PBR). To improve the PBR operation, it is of interest...

Particle filters are becoming increasingly important and useful for state estimation in nonlinear systems. Many filter versions have been suggested, and several results on convergence of filter properties have been reported. However, apparently a result on the convergence of the state estimate itself has been lacking. This contribution describes a general framework for particle filters for stat...

This paper presents the design of an optimal nonlinear controller for trajectory tracking of a quadrotor helicopter based on the well-known sliding mode control method (SMC). To this end, the Lyapunov law analysis has been used regarding nonlinear equations of the quadrotor. The proposed control method is characterized by its robustness against disturbances and aerodynamic effects. One of the m...

A common requirement implicit in current methods for the design of robust state estimators and robust fault detection filters is that the first Markov matrix must be non-zero, and indeed, full rank. We relax both of these restrictions in this paper to allow the applicability to a wider range of systems. The extended results are then applied to an aircraft fault detection for which the restricti...