Observer-type Kalman innovation filter for uncertain linear systems

An observer-type of Kalman innovation filtering algorithm to find a practically implementable "best" Kalman filter, and such an algorithm based on the evolutionary programming (EP) optima-search technique, are proposed, for linear discrete-time systems with time-invariant unknown-but-hounded plant and noise uncertainties. The worst-case parameter set from the stochastic uncertain system represented by the interval form with respect to the implemented "best" filter is also found in this work for demonstrating the effectiveness of the proposed filtering scheme. The new EP-based algorithm utilizes the global optima-searching capability of EP to find the optimal Kalman filter and state estimates at every iteration, which include both the best possible worst case Interval and the optimal nominal trajectory of the Kalman filtering estimates of the system state vectors. Simulation results are included to show that the new algorithm yields more accurate estimates and is less conservative as compared with other related robust filtering schemes     

Automatic Target Recognition: Some Considerations

Machine analysis of aerial reconnaissance data is desirable. New acquisition techniques, equipment, and sensors, plus a growing demand for rapidly obtained, accurate information necessitate machine performance of tasks presently accomplished by observers and interpreters. Many of these tasks can be described as pattern recognition or information processing functions. This paper discusses significant problems encountered when attempting to identify objects or regions automatically from aerial photography. Problems include sensor distortions, variations in target environment, and concepts of design and ?teaching? of an actual device. Operational requirements such as processing speed and flexibility restrict the types of solutions available. Approaches to target recognition with relative merits and shortcomings are presented; these include shape correlation, feature extraction, and image quantization. The effect of mission requirements on system parameters is discussed. A bibliography is included.     

The finite element approximation of vector fields in curvilinear coordinates

We demonstrate that it is possible to express each component of the displacement vector for the interior point of the finite element (FE) through all components of nodal unknowns in curvilinear coordinates. The effectiveness of the valid technique of vector approximation for displacement fields has been verified on an example.     

STEREO Ground Segment, Science Operations, and Data Archive

Vitally important to the success of any mission is the ground support system used for commanding the spacecraft, receiving the telemetry, and processing the results. We describe the ground system used for the STEREO mission, consisting of the Mission Operations Center, the individual Payload Operations Centers for each instrument, and the STEREO Science Center, together with mission support from the Flight Dynamics Facility, Deep Space Mission System, and the Space Environment Center. The mission planning process is described, as is the data flow from spacecraft telemetry to processed science data to long-term archive. We describe the online resources that researchers will be able to use to access STEREO planning resources, science data, and analysis software. The STEREO Joint Observations Program system is described, with instructions on how observers can participate. Finally, we describe the near-real-time processing of the “space weather beacon” telemetry, which is a low telemetry rate quicklook product available close to 24 hours a day, with the intended use of space weather forecasting.     

Experimental studies of the impulsive ultrasonic machining by a tool with an intermediate striker

Some experimental data on the process of impulsive ultrasonic machining are presented. Also given are the data on variations in the impulsive vibration frequency of a workpiece and a tool.     

Distributed decision fusion using empirical estimation

The problem of optimal data fusion in multiple detection systems is studied in the case where training examples are available, but no a priori information is available about the probability distributions of errors committed by the individual detectors. Earlier solutions to this problem require some knowledge of the error distributions of the detectors, for example, either in a parametric form or in a closed analytical form. Here we show that, given a sufficiently large training sample, an optimal fusion rule can be implemented with an arbitrary level of confidence. We first consider the classical cases of Bayesian rule and Neyman-Pearson test for a system of independent detectors. Then we show a general result that any test function with a suitable Lipschitz property can be implemented with arbitrary precision, based on a training sample whose size is a function of the Lipschitz constant, number of parameters, and empirical measures. The general case subsumes the cases of nonindependent and correlated detectors.     

An algorithm for detection of moving optical targets

An algorithm is described for detecting moving optical targets against spatially nonstationary Poisson background and noise. The algorithm has applications in optical detection of objects such as meteors, asteroids, and satellites against a stellar background. A maximum-likelihood approach is used which results in reducing interference from stars. It is shown that by choosing a detection threshold to provide a constant false alarm rate, the resulting algorithm is independent of the signal strength of the target. An analysis of this algorithm is presented, showing the probability of detection for several false-alarm rates     

Nonlinear deforming of laminated composite shells of revolution under finite deflections and normal’s rotation angles

The discretization of the boundary value problem for laminated composite shells is based on the finite difference approach using the regular mesh with the constant grid step and the difference operators of the second order of accuracy. The dynamic relaxation method is proposed for the solution of the nonlinear problem. The evolutionary equations of the dynamic relaxation are constructed, and the optimum parameters of the converging linear iterative process are estimated.     

Estimation techniques for GMSK using linear detectors in satellite communications

This paper describes data-aided signal level and noise variance estimators for Gaussian minimum shift keying (GMSK) when the observations are limited to the output of a filter matched to the first pulse-amplitude modulation (PAM) pulse in the equivalent PAM representation. The estimators are based on the maximum likelihood (ML) principle and assume burst-mode transmission with known timing and a block of L₀ known bits. While it is well known that ML estimators are asymptotically unbiased and efficient, the analysis quantifies the rate at which the estimators approach these asymptotic properties. It is shown that the carrier phase, amplitude, and noise variance estimators are unbiased and can achieve their corresponding Cramer-Rao bounds with modest combinations of signal-to-noise ratio and observation length. The estimates are used to estimate the signal-to-noise ratio. It is shown that the mean squared error performance of the ratio increases with signal-to-noise ratio while the mean squared error performance of the ratio in decibels decreases with signal-to-noise ratio. Simulation results are provided to confirm the accuracy of the analytic results.