Bayesian Recursive Update for Ensemble Kalman Filters

Few real-world systems are amenable to truly Bayesian filtering; nonlinearities and non-Gaussian noises can wreak havoc on filters that rely on linearization and Gaussian uncertainty approximations. This article presents the Bayesian Recursive Update Filter (BRUF), a Kalman filter that uses a recursive approach to incorporate information from nonlinear measurements. The BRUF relaxes the measurement linearity assumption of the Extended Kalman Filter (EKF) by dividing the measurement update into a user-defined number of steps. The proposed technique is extended for ensemble filters in the Bayesian Recursive Update Ensemble Kalman Filter (BRUEnKF). The performance of both filters is demonstrated in numerical examples, and new filters are introduced which exploit the theoretical foundation of the BRUF in different ways. A comparison between the BRUEnKF and Gromov flow, a popular particle flow algorithm, is presented in detail. Finally, the BRUEnKF is shown to outperform the EnKF for a very high-dimensional system

A multiplicative multi-state constraint Kalman filter

A multi-state constraint Kalman filter (MSCKF) is implemented with a multiplicative quaternion update. The filter is tested on data from simulated inertial measurement unit (IMU) and camera measurements. In the simulation, a vehicle views feature points in the environment as it travels along a circular path. The MSCKF is demonstrated to be consistent using Monte Carlo analysisAerospace Engineerin

SPARK: A US Cohort of 50,000 Families to Accelerate Autism Research

The Simons Foundation Autism Research Initiative (SFARI) has launched SPARKForAutism. org, a dynamic platform that is engaging thousands of individuals with autism spectrum disorder (ASD) and connecting them to researchers. By making all data accessible, SPARK seeks to increase our understanding of ASD and accelerate new supports and treatments for ASD