An Introduction to CMEs and Energetic Particles

Energetic particle observations in the interplanetary medium provide fundamental information about the origin, development and structure of coronal mass ejections. This paper reviews the status of our understanding of the ways in which particles are energised at the Sun in association with CMEs. This understanding will remain incomplete until the relationship between CMEs and flares is determined and we know the topology of the associated magnetic fields. The paper also discusses the characteristics of interplanetary CMEs that may be probed using particle observations.     

A method for representing the thermodynamic and thermophysical functions in simulation of powerplant processes

A method for representing thermodynamic and thermophysical functions is presented; the functions make it possible to simulate thermal and gasodynamic processes in powerplants that use different individual substances or their mixtures as a working fluid. The method also involves consideration of real gas properties.     

The Electric Antennas for the STEREO/WAVES Experiment

The STEREO/WAVES experiment is designed to measure the electric component of radio emission from interplanetary radio bursts and in situ plasma waves and fluctuations in the solar wind. Interplanetary radio bursts are generated from electron beams at interplanetary shocks and solar flares and are observed from near the Sun to 1 AU, corresponding to frequencies of approximately 16 MHz to 10 kHz. In situ plasma waves occur in a range of wavelengths larger than the Debye length in the solar wind plasma λ _D ≈10 m and appear Doppler-shifted into the frequency regime down to a fraction of a Hertz. These phenomena are measured by STEREO/WAVES with a set of three orthogonal electric monopole antennas. This paper describes the electrical and mechanical design of the antenna system and discusses efforts to model the antenna pattern and response and methods for in-flight calibration.     

Neutral Atmospheres

This paper summarizes the understanding of aeronomy of neutral atmospheres in the solar system, discussing most planets as well as Saturn’s moon Titan and comets. The thermal structure and energy balance is compared, highlighting the principal reasons for discrepancies amongst the atmospheres, a combination of atmospheric composition, heliocentric distance and other external energy sources not common to all. The composition of atmospheres is discussed in terms of vertical structure, chemistry and evolution. The final section compares dynamics in the upper atmospheres of most planets and highlights the importance of vertical dynamical coupling as well as magnetospheric forcing in auroral regions, where present. It is shown that a first order understanding of neutral atmospheres has emerged over the past decades, thanks to the combined effects of spacecraft and Earth-based observations as well as advances in theoretical modeling capabilities. Key gaps in our understanding are highlighted which ultimately call for a more comprehensive programme of observation and laboratory measurements.     

Kalman filtering with state equality constraints

Kalman filters are commonly used to estimate the states of a dynamic system. However, in the application of Kalman filters there is often known model or signal information that is either ignored or dealt with heuristically. For instance, constraints on state values (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. A rigorous analytic method of incorporating state equality constraints in the Kalman filter is developed. The constraints may be time varying. At each time step the unconstrained Kalman filter solution is projected onto the state constraint surface. This significantly improves the prediction accuracy of the filter. The use of this algorithm is demonstrated on a simple nonlinear vehicle tracking problem     

Missions to Mercury

Mercury is a very difficult planet to observe from the Earth, and space missions that target Mercury are essential for a comprehensive understanding of the planet. At the same time, it is also difficult to orbit because it is deep inside the Sun’s gravitational well. Only one mission has visited Mercury; that was Mariner 10 in the 1970s. This paper provides a brief history of Mariner 10 and the numerous imaginative but unsuccessful mission proposals since the 1970s for another Mercury mission. In the late 1990s, two missions—MESSENGER and BepiColombo—received the go-ahead; MESSENGER is on its way to its first encounter with Mercury in January 2008. The history, scientific objectives, mission designs, and payloads of both these missions are described in detail.     

PHD filters of higher order in target number

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In this paper we show that this is indeed possible. We derive a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number.     

Exploiting GNSS signal structure to enhance observability

There are a number of different error sources, such as multipath and thermal noise, which corrupt satellite navigation waveforms from their theoretical structure. However, even under ideal conditions the broadcast signals have some degree of deformation as a result of the practical individual hardware implementation. For the most demanding users of satellite navigation, such as aircraft navigation and landing systems, it is important to characterize the nominal signal structure in order to detect minimal variations resulting from hardware-based errors. Thus far such precorrelation Global Navigation Satellite System (GNSS) signal quality monitoring has been performed through high gain antennas, which allow for raising the GNSS spectrum above the thermal noise floor and observing the structure of the signal directly at the front end output. This paper describes a new approach to achieve such observability based on signal processing techniques, such as dithering and averaging, which leverage the repetitive nature of the GNSS signal. The paper presents how these techniques can drastically improve the signal-to-noise ratio (SNR) in postprocessing, allowing for the direct analysis of GNSS signals using traditional front end designs and conventional antennas. Results are predicted using the appropriate theory and validated using data collected from the Global Positioning System (GPS).