How Operational Oceanography Can Benefit from Dynamic Topography Estimates as Derived from Altimetry and Improved Geoid

With a precise geoid, GOCE will allow an estimation of absolute dynamic topography from altimetry. The projected benefits to operational oceanography and its applications are analyzed herein. After a brief overview of operational oceanography, we explain how the new geoids will be used in the future to improve real time altimeter products and to better constrain modelling and data assimilation systems. A significant impact is expected both for mesoscale (e.g. better estimations and forecasts of currents for pollution monitoring, marine safety, offshore industry) and climate (better initialization of coupled ocean/atmosphere models) applications. This revised version was published online in August 2006 with corrections to the Cover Date.     

Combined Use of Altimetry and In Situ Gravity Data for Coastal Dynamics Studies

Accurate local geoids derived from in situ gravity data will be valuable in the validation of GOCE results. In addition it will be a challenge to use GOCE data in an optimal way, in combination with in situ gravity, to produce better local geoid solutions. This paper discusses the derivation of a new geoid over the NW European shelf, and its comparison with both tide gauge and altimetric sea level data, and with data from ocean models. It is hoped that over the next few years local geoid methods such as these can be extended to cover larger areas and to incorporate both in situ and satellite measured gravity data. This revised version was published online in August 2006 with corrections to the Cover Date.     

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.     

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.     

Solar,geomagnetic and long term effects on thermospheric neutral kinetic temperatures at midlatitude

Measurements of midlatitude thermospheric neutral kinetic temperatures obtained from 1972 to 1979 have been used to investigate the effects of solar and geomagnetic activity, as well as long term effects, on the thermosphere. With these data a simple power law relationship between the temperature and solar activity (expressed as the 2.8 GHz solar radio flux) has been found to give a high correlation. In addition, a linear relationship between temperature changes and geomagnetic activity (expressed as Ap), as well as annual and semiannual effects have been found. The annual variation is found to be indistinguishable in phase from the annual variation of the solar declination angle. The present four parameter formulation gives a better fit to the data than is obtained with available empirical models of the thermosphere, and this has allowed us to investigate the properties and postulates of some of these models.     

Viral attack to Internet banking applications

The PC Bank may be the most timesaving banking innovation since the ATM (Automatic Teller Machine). However, it also brings new possibilities for thieves. This is mainly because we have not completely solved the growing problem of computer viruses and Trojans that can act on our computers against our will. Several methods are used against them, although none can be considered 100% effective. Even more, most personal computers have little or no virus protection, so they are very easy to attack. This can become a problem as nearly all PC banking systems rely on authentication with identification passwords. This system is vulnerable and we will show that it does not provide enough protection for the users that want to make transfer operations from their accounts     

Multisensor deployment using PCRLBS, incorporating sensor deployment and motion uncertainties

Recently, a general framework for sensor resource deployment (Hernandez, et. al. 2004) has been shown to allow efficient and effective utilization of a multisensor system. The basis of this technique is to use the posterior Cramer-Rao lower bound (PCRLB) to quantify and control the optimal achievable accuracy of target state estimation. In the original formulation (Hernandez, et. al. 2004) it was assumed that the sensor locations were known without error. In the current paper, the authors extend this framework by addressing the issues of imperfect sensor placement and uncertain sensor movement (e.g., sensor drift). The crucial consideration is then how these two forms of uncertainty are factored into the sensor management strategy. If unaccounted for, these uncertainties will render the output of the resource manager inaccurate and overoptimistic. The authors adjust the PCRLB to account for sensor location uncertainty, and we also allow for measurement origin uncertainty due to missed detections and false alarms. The work is motivated by the problem of tracking a submarine by adaptively deploying sonobuoys from a helicopter. Simulation results are presented to show the advantages of accounting for sensor location uncertainty within this focal domain of antisubmarine warfare. The authors note that the generic nature of the technique allows it to be utilized within other problem domains, including tracking ground-based targets using unattended ground sensors (UGSs) or unmanned aerial vehicles (UAVs)