This paper presents a dynamic 3D Vision system that is able to estimate dense depth maps from an image sequence. The depth maps computed at each time instant are used in an Extended Kalman filtering structure, that integrates all depth measurements over time, reducing uncertainty. Results with images acquired by an underwater camera, are presented.

This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be a...

This paper investigates the use of extended Kalman filtering to train recurrent neural networks with rather general convex loss functions and regularization terms on network parameters, including $\ell _{1}$ -regularization. We show tha...

Two main difficulties in process monitoring are lack of reliable measurements of key process variables and difficulty in defining quantitative relationships between state variables. In this study sensor networks are used to demonstrate an approach based on Kalman filtering to model the specific monitoring systems. Kalman filtering at both local nodes and fusion center are the covariance matrice...

Although several Kalman filtering algorithms have been presented for adaptive multiuser detection, none is “blind” due to requiring training data sequences and/or more knowledge than the spreading waveform and delay of the desired user. This paper proposes a novel blind adaptive multiuser detector based on Kalman filtering and compares it with previously published LMS and RLS algorithms for bli...

An important part of system modeling is determining parameter values, particularly for biomolecular systems, where direct measurements of individual parameters is often hard. While extended Kalman filters have been used for this purpose, the choice of the process noise covariance is generally unclear. Here, we address this issue for biomolecular systems using a combination of Monte Carlo simula...

Kalman filtering is a classic state estimation technique used widely in engineering applications such as statistical signal processing and control of vehicles. It is now being used to solve problems in computer systems, such as controlling the voltage and frequency of processors to minimize energy while meeting throughput requirements. Although there are many presentations of Kalman filtering i...