Comparison of half-bridge off-line, ZCS-QRC and ZVS-MRC

The performance of the half-bridge (HB) zero-current-switched (ZCS) quasi-resonant converter (QRC) and zero-voltage-switched (ZVS) multiresonant converter (MRC) is compared with respect to their efficiency, input voltage range, semiconductor stresses, power density, and reliability. The efficiency of the HB ZVS-MRC at a given nominal input is shown to be highly dependent on the range of the input voltage, and it suffers when the converter has to be designed to cover a wide range. However, this is not the case for the HB ZCS-QRC. Experimental versions of the HB ZCS-QRC and HB ZVS-MRC were designed for the input voltage range from 150 to 350 V and a maximum output power of 100 W, under the same constraints, to facilitate their comparison     

Current Issues and Future Trends: DVB-RCS Satellite Systems

The telecommunications market is reaching new types of customers who require their applications anywhere and anyway. Therefore, the DVB-RCS standard, which had great success in the fixed version is changing in this direction. The main objective of this work is to give an overview of architectures and technologies that can be used for implementation of a first-generation of advanced mobile satellite systems for the provision of broadband multimedia services.     

Electronic flight progress strip prototype for en route ATC

An electronic flight progress strip (FPS) display and a color plan view display (CPVD) as a laboratory prototype are described. The purposes of prototype development are to examine the effectiveness and problems of the electronic FPS display in comparison to the current system and to identify the controller-workstation requirements, which are necessary to decide a future planning of a production prototype development. The evaluation and additional test were conducted for a typical en route transition sector. It was found that electronic FPS was useful in the normal traffic load. Some possible improvements were identified in the heavy traffic load situations represented in the additional test     

An alternate algorithm for discrete-time filtering

The discrete-time Kalman filter is an optimal estimator for the states of a linear, stochastic system. It assumes that measurements are linear combinations of the states, and all disturbances are Gaussian. The influence diagram, a decision analysis tool that provides an algorithm for discrete-time filtering equivalent to the Kalman filter when the influence diagram represents Gaussian random variables, is discussed. The influence diagram algorithm is a factored form of the Kalman filter, similar to other factored forms such as the U-D filter. Compared with the Kalman filter, it offers improved numerical properties. Compared with other factored forms, it offers a reduced computational load     

Current Injected Equivalent Circuit Approach to Modeling Switching DC-DC Converters

A new current injected equivalent circuit approach (CIECA) to modeling switching dc-dc converter power stages is developed, which starts with the current injected approach and results in either a set of equations which completely describe input and out-put properties or an equivalent linear circuit model valid at small signal, low frequency levels. This approach to modeling switching dc-dc converter power stages has the merits but not the demerits of both the electronic equivalent circuit state space average approach and the current injected control type approach, namely, 1) the modeling is very clear and is simple whether the converter operates in continuous or discontinuous inductor conduction modes, 2) the modeling results in an equivalent circuit which is very close to the actual converter, and 3) the equivalent circuit can be used directly in the computer for theoretical predictions like SPICE, etc.     

Characterization of Kalman filter residuals in the presence ofmismodeling

The mean and covariance of a Kalman filter residual are computed for specific cases in which the Kalman filter model differs from a linear model that accurately represents the true system (the truth model). Multiple model adaptive estimation (MMAE) uses a bank of Kalman filters, each with a different internal model, and a hypothesis testing algorithm that uses the residuals from this bank of Kalman filters to estimate the true system model. At most, only one Kalman filter model will exactly match the truth model and will produce a residual whose mean and standard deviation have already been analyzed. All of the other filters use internal models that mismodel the true system. We compute the effects of a mismodeled input matrix, output matrix, and state transition matrix on these residuals. The computed mean and covariance are compared with simulation results of flight control failures that correspond to mismodeled input matrices and output matrices     

Ion Acoustic Waves in the Heliosphere

Observations of ion acoustic waves in the solar wind during the first and second orbit of the Ulysses spacecraft are presented. The observations show variations of the wave activity with the heliolatitude and with the phase of the solar cycle. The interrelationships between the wave intensity and the electron heat flux and the ratio of electron to proton temperature, T _e/T _p, are examined. This revised version was published online in August 2006 with corrections to the Cover Date.     

Attitude Determination Via Nonlinear Filtering

The theory of nonlinear filtering has been applied to the problem of the attitude determination of a satellite. Two approximation methods have been studied with a view to practical implementation. Computer simulation results are included.     

Clutter Map CFAR Analysis

An analysis of the probability of target detection for a clutter map CFAR using digital exponential filtering has been performed. General performance equations are derived. The probability of detection versus signal-to-noise ratio is plotted for a false alarm probability of 1.E-06 for several weight values. The CFAR loss is plotted for a detection probability of 0.9 and false alarm probabilities of 1.E-06 and 1.E-08.