Decentralized linear estimation in correlated measurement noise

Some results and perspectives are provided on the problem of optimally combining estimates from different sensors when the measurement noise processes are correlated. The authors consider only the static estimation problem and limit the discussion to fusion with two sensors. A necessary and sufficient condition for optimality of the decentralized estimator in the presence of correlated measurement noise processes is presented. The result ties together previously reported work, and yields additional insights.<>     

Autonomous Landing Guidance Systems

All weather tactical aircraft recovery and high sortie generation rates from forward, possibly battle damaged landing areas will reqire autonomous landing guidance systems which are independent of ground-based cooperative aids. A recently completed study has examined the operational requirements and assessed current and near term technology for an answer to this need. The Landing Systems Requirements/Synthesis Study has defined the Pilot/vehicle Interface and imaging sensor suite required, based on the concept of augmenting pilot forward vision with sensor imagery and guidance symbology on a HUD during low visibility landings. Image processing technology was also assessed for potential enhancement of the information presented to the pilot. This paper summarizes the final report of the Study, which is in publication.     

Applications of Mininum Variance Reduced-State Estimators

This paper presents an algorithm for a class of suitably constrained reduced-order filters which minimize the variance of the estimated variables. The algorithm generates both the filter gain history and the true estimation error covariance. The algorithm provides a quantitative criterion which can be used to measure the performance of any reduced-order estimator. Both continuous and discrete estimators are considered. Several examples are treated including an application of the technique to a hybrid navigation system of high order.     

Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm

The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results.     

Estimation of equatorial electrojet from total electron content at geomagnetic equator using Kalman filter

The temporal variation of the equatorial electrojet is estimated utilising a suitably designed Kalman filter and using the established empirical relations between the anomaly component of equatorial TEC and the modified electrojet. TEC data obtained from dual frequency GPS receivers are used for the purpose. Estimation requires the a-priori knowledge of the peak electrojet value of the day and hence can be made in post temporal scenario only. Initial results obtained during a low solar activity time in an equinoctial month shows acceptable accuracy of the proposed algorithm. Limited analysis is done by segregating the results into temporal sessions of pre-attainment and post-attainment of the electrojet peak.     

How the science and engineering of spaceflight contribute to understanding the plasticity of spinal cord injury.

Space programs support experimental investigations related to the unique environment of space and to the technological developments from many disciplines of both science and engineering that contribute to space studies. Furthermore, interactions between scientists, engineers and administrators, that are necessary for the success of any science mission in space, promote interdiscipline communication, understanding and interests which extend well beyond a specific mission. NASA-catalyzed collaborations have benefited the spinal cord rehabilitation program at UCLA in fundamental science and in the application of expertise and technologies originally developed for the space program. Examples of these benefits include: (1) better understanding of the role of load in maintaining healthy muscle and motor function, resulting in a spinal cord injury (SCI) rehabilitation program based on muscle/limb loading; (2) investigation of a potentially novel growth factor affected by spaceflight which may help regulate muscle mass; (3) development of implantable sensors, electronics and software to monitor and analyze long-term muscle activity in unrestrained subjects; (4) development of hardware to assist therapies applied to SCI patients; and (5) development of computer models to simulate stepping which will be used to investigate the effects of neurological deficits (muscle weakness or inappropriate activation) and to evaluate therapies to correct these deficiencies.     

Square root parallel Kalman filtering using reduced-order localfilters

The basic parallel Kalman filtering algorithms derived by H.R. Hashemipour et al. (IEEE Trans. Autom. Control. vol.33, p.88-94, 1988) are summarized and generalized to the case of reduced-order local filters. Measurement-update and time-update equations are provided for four implementations: the conventional covariance filter, the conventional information filter, the square-foot covariance filter, and the square-foot information filter. A special feature of the suggested architecture is the ability to accommodate parallel local filters that have a smaller state dimension than the global filter. The estimates and covariance or information matrices (or their square roots) from these reduced-order filters are collated at a central filter at each step to generate the full-size, globally optimal estimates and their associated error covariance or information matrices (or their square roots). Aspects of computational complexity and the ensuing tradeoff with communication are discussed     

Nonlinear quantum ion acoustic shock wave dynamics with exchange-correlation effects

The dynamics of linear and nonlinear electrostatic shock excitations is studied in homogeneous, unmagnetized, unbounded and dissipative quantum plasma consisting of electrons and ions. The dissipation in the system is taken into account by incorporating the ion kinematic viscosity. The system is modelled using the quantum hydrodynamic equations in which the electrons are significantly affected by the quantum forces, viz., the quantum statistical pressure, the quantum Bohm potential and electron exchange-correlations due to electron spin. In the weakly nonlinear limit, using reductive perturbation method deformed Korteweg-de Vries Burgers’s (KdVB) equation, which elegantly combines the effects of nonlinearity, dispersion and dissipation is derived. It is found that the present model predicts the existence of both nonlinear oscillatory and monotonic shock structures. The temporal evolution, stability and phase-space dynamics of nonlinear ion acoustic shocks are investigated numerically to elucidate the effects of quantum diffraction, electron exchange correlation and ion kinematic viscosity.     

Attenuation prediction for fade mitigation using neural network with in situ learning algorithm

Increasing demand of bandwidth in communication satellites has forced satellite links to be designed in Ku bands and above. But at these frequencies, rain and other tropospheric elements result in large attenuation. To mitigate the tropospheric attenuation of microwave satellite signals above 10 GHz using any standard Fade Mitigation Technique (FMT), it is essential to have a priori knowledge about the level of attenuation. Hence, short-term rain attenuation prediction models play a key role in maintaining the link in which necessary compensation can be applied depending on the early information of attenuation. This paper presents a method of attenuation prediction using Adaptive Artificial Neural Network. Here In situ Learning Algorithm (ILA) has been used to enable the system to track the non-stationary nature of the attenuation. To validate this, Ku Band data, collected at three different sites in India have been used for the purpose of prediction. The performance of the algorithm is determined through the estimation of prediction accuracy by comparing the predicted values with the measured data. Results obtained using the mentioned technique shows considerably good accuracy even up to 20 s of prediction interval with acceptable ratio between the under and over predictions. The prediction performance is evaluated for different prediction intervals. Furthermore the present model is also compared with the persistence model and the relative performance is quantified.