Attitude estimation is a core problem in many robotic systems that perform automated or semi automated navigation. The configuration space of the attitude motion is naturally modelled on the Lie group of special orthogonal matrices SO(3). Many current attitude estimation methods are based on non-matrix parameterization of attitude. Non-matrix parameterization schemes sometimes lead to modelling...

Radar tracking plays an important role in the area of early warning and detection system, whose precision is closely connected with filtering algorithm. With the development of noise jamming technology in radar echo signal, linear filtering becomes more and more difficult to satisfy the demands of radar tracking, while nonlinear filtering can solve problems such as non-Gaussian noises. There ex...

The analysis of protein-protein interactions (PPIs) is crucial to the understanding of cellular organizations, processes and functions. The reliability of the current experimental approaches interaction data is prone to error. Thus, a variety of computational methods have been developed to supplement the interactions that have been detected experimentally. The present paper’s main objective is ...

We establish the equivalence between the optimal least-squares state estimator for a linear time-invariant dynamic system with noise corrupted input and output, and an appropriately modified Kalman filter. The approach used is algebraic and the result shows that the noisy input/output filtering problem is not fundamentally different from the classical Kalman filtering problem. The result is ill...

This paper proposes an adaptive two-stage extended Kalman filter (ATEKF) for estimation of unknown fault bias in an INS-GPS loosely coupled system. The Kalman filtering technique requires complete specifications of both dynamical and statistical model parameters of the system. However, in a number of practical situations, these models may contain parameters, which may deviate from their nominal...