One-up on L1: Can X-rays provide longer advanced warning of solar wind flux enhancements than upstream monitors?

Observations of strong solar wind proton flux correlations with ROSAT X-ray rates along with high spectral resolution Chandra observations of X-rays from the dark Moon show that soft X-ray emission mirrors the behavior of the solar wind. In this paper, based on an analysis of an X-ray event observed by XMM-Newton resulting from charge exchange of high charge state solar wind ions and contemporaneous neutral solar wind data, we argue that X-ray observations may be able to provide reliable advance warning, perhaps by as much as half a day, of dramatic increases in solar wind flux at Earth. Like neutral atom imaging, this provides the capability to monitor the solar wind remotely rather than in situ.     

Plasma transfer processes at the magnetopause

Space Science Reviews -     

Performance measure for Markovian switching systems using best-fitting Gaussian distributions

This paper considers the theoretical posterior Cramer-Rao lower bound (PCRLB) for the case of tracking a manoeuvring target with Markovian switching dynamics. In a recent article [2] it was proposed to calculate the PCRLB conditional on the manoeuvre sequence and then determine the bound as a weighted average, giving an unconditional PCRLB. However, we demonstrate that this approach can produce an overly optimistic lower bound, because the sequence of manoeuvres is implicitly assumed known. Motivated by this, we develop a general approach and derive a closed-form estimate of the PCRLB in the case of Markovian switching systems. The basis of the approach is to, at each time step, replace the multi-modal prior target probability density function (pdf) with a best-fitting Gaussian (BFG) approximation. We present a recursive formula for calculating the mean and covariance of this Gaussian distribution, and demonstrate how the covariance increases as a result of the potential manoeuvres. We are then able to calculate the PCRLB for this BFG model using an existing Riccati-like recursion. Because of the BFG approximation, we are no longer guaranteed a bound and so we refer to our estimate as an error performance measure rather than a bound. The presented approach is applied both to filtering and smoothing cases. The simulation results indicate a very close agreement between the proposed performance measure and the error performance of an interacting multiple model estimator.     

The dynamics of chromospheric spicules

We present the observational results on chromospheric spicules obtained at the Sayan observatory 50 cm coronograph. To investigate the evolution of chromospheric spicules, we analysed spicule spectra of strong chromospheric lines measured simultaneously at three altitudes above the solar limb during 5–60 min with a time resolution of 10 to 20 s. The spatial resolution was better than 1, and the spectral resolution was 0.03Å in 6563Å. The appearance of a spicule at a given altitude is preceded by an sharp increase in line-of-sight velocity and/or in line half-width at a lower level. Generally, the evolution has a non-monotonous impulsive character. Changes of line-of-sight velocities and other parameters of the line profile can be represented as the superposition of slow, evolutionary changes and fluctuations with periods of about 80 to 120 s. The amplitude of line-of-sight velocity fluctuations is 2–3 km/sec and tends to increase with height. By studying the phase delays of the fluctuations at different heights, we found that the propagation velocity exceeds 300 km s^–1, and that the disturbances do not necessarily propagate upwards.     

Multisensor resource deployment using posterior Cramer-Rao bounds

The development of a general framework for the systematic management of multiple sensors in target tracking in the presence of clutter is described. The basis of the technique is to quantify, and subsequently control, the accuracy of target state estimation. The posterior Cramer-Rao lower bound (PCRLB) provides the means of achieving this aim by enabling us to determine a bound on the performance of all unbiased estimators of the unknown target state. The general approach is then to use optimization techniques to control the measurement process in order to achieve accurate target state estimation. We are concerned primarily with the deployment and utilization of limited sensor resources. We also allow for measurement origin uncertainty, with sensor measurements either target-generated or false alarms. An example in which the aim is to track a submarine by deploying a series of constant false-alarm rate passive sonobuoys is presented. We show that by making some standard assumptions, the effect of the measurement origin uncertainty can be expressed as a state-dependent information reduction factor which can be calculated off-line. This enables the Fisher information matrix (FIM) to be calculated quickly, allowing Cramer-Rao bounds to be utilized for real-time, dynamic sensor management. The sensor management framework is shown to determine deployment strategies that enable the target to be accurately localized, and at the same time efficiently utilize the limited sensor resources.     

Tracking with classification-aided multiframe data association

In most conventional tracking systems, only the target kinematic information from, for example, a radar or sonar or an electro-optical sensor, is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. The use of target class information in data association can improve discrimination by yielding purer tracks and preserving their continuity. In this paper, we present the simultaneous use of target classification information and target kinematic information for target tracking. The approach presented integrates target class information into the data association process using the 2-D (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The multiframe association likelihood is developed to include the classification results based on the "confusion matrix" that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparisons with and without the use of class information in data association are presented on a ground target tracking problem. Simulation results quantify the benefits of classification-aided data association for improved target tracking, especially in the presence of association uncertainty in the kinematic measurements. Also, the benefit of 5-D (or multiframe) association versus 2-D association is investigated for different quality classifiers. The main contribution of this paper is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association.