The Earth's magnetosphere is formed as a consequence of the interaction between the planet's magnetic field and the solar wind, a continuous plasma stream from the Sun. A number of different solar wind phenomena have been studied over the past forty years with the intention of understandingand forcasting solar behavior and space weather. In particular, Earth-bound interplanetary coronal mass ejections (CMEs) can significantly disturb the Earth's magnetosphere for a short time and cause geomagnetic storms. We present a mission concept consisting of six spacecraft that are equally spaced in a heliocentric orbit at 0.72 AU. These spacecraft will monitor the plasma properties, the magnetic field's orientation and magnitude, and the 3D-propagation trajectory of CMEs heading for Earth. The primary objective of this mission is to increase space weather forecasting time by means of a near real-time information service, that is based upon in-situ and remote measurements of the CME properties. The mission secondary objective is the improvement of scientific space weather models. In-situ measurements are performed using a Solar Wind Analyzer instrumentation package and flux gate magnetometers. For remote measurements, coronagraphs are employed. The proposed instruments originate from other space missions with the intention to reduce mission costs and to streamline the mission design process. Communication with the six identical spacecraft is realized via a deep space network consisting of six ground stations. This network provides an information service that is in uninterrupted contact with the spacecraft, allowing for continuos space weather monitoring. A dedicated data processing center will handle all the data, and forward the processed data to the SSA Space Weather Coordination Center. This organization will inform the general public through a space weather forecast. The data processing center will additionally archive the data for the scientific community. This concept mission allows for major advances in space weather forecasting and the scientific modeling of space weather. @en

The cubature Kalman filter (CKF) is well-known for a decade as a derivative-free nonlinear Kalman filter that is well-suited for high-dimensional nonlinear estimation problems. This paper further develops this classical CKF in order to cope with time-varying noise statistics as well as inequality constraints on the estimated states. The resulting adaptive filter is suggested to provide more accurate state estimates and to be more robust against filter divergence. Moreover, this contribution proposes an automated filter design based on numerical optimization which uses the normalized estimation error squared (NEES) and the normalized innovation squared (NIS) as part of the objective function. The novel adaptive CKF is applied to wind turbines in order to assess the potential improvement for state and parameter estimation. The simulation results for an illustrative acid test scenario with time-varying measurement noise show the superiority of the novel adaptive CKF since it compensates the noise robustly and thereby outperforms the classical filter.@en