SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The use of the magnetooptic filter to observe solar magnetic fields in the potassium line at 7699 A is described. The filter has been used in the Big Bear videomagnetograph since October 23. It gives a high sensitivity and dynamic range for longitudnal magnetic fields and enables measurement of transverse magnetic fields using the sigma component. Examples of the observations are presented.
The synthetic estimation filter (SEF) was previously designed for implementation in an optical correlator. The intent was to find a simple method that would reduce the number of reference images necessary to track six degrees of freedom of an object, while avoiding the more complicated computations necessary in other methods of similar intent. Initial laboratory results are shown for estimating in-plane rotation with a minimal number of filters.
Soft X-ray telescopes require filters that block visible and infrared light and have good soft X-ray transmission. The optical properties of possible materials are discussed, and the fabrication and testing methods for the filters used in a 10-inch normal incidence telescope for 63 A are described. The best performances in the 44-114-A wavelength range are obtained with foils of carbon and rhodium.
While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.
A general form for the first-order representation of the continuous second-order linear structural-dynamics equations is introduced to derive a corresponding form of first-order continuous Kalman filtering equations. Time integration of the resulting equations is carried out via a set of linear multistep integration formulas. It is shown that a judicious combined selection of computational paths and the undetermined matrices introduced in the general form of the first-order linear structural systems leads to a class of second-order discrete Kalman filtering equations involving only symmetric sparse N x N solution matrices.
The results of the transmission calibrations of the seven sets of flight integration filters for the Extreme Ultraviolet Explorer (EUVE) satellite are presented. The Berkely EUV Calibration Facility was used to determine absolute transmissions over six orders of magnitue at 28 wavelengths. A lifetesting program designed to monitor changes in the transmission and mechanical properties of the EUVE filters over the lifetime of the mission is described.
Microscope-stage holder keeps filter pad flat to keep it in focus. Specimen holder is special vacuum chuck that applies suction through flat screen. Suction keeps filter pad flat against specimen holder while microscope stage moving to scan areas denoted by grid. In microscope system equipped with video camera, image-analyzing/particle-counting computer, and automatic focus, use of stage speeds count considerably by eliminating need to stop frequently for manual refocusing. Technician free to perform other tasks while computer controls translation of stage and takes count automatically.
Paper presents historical view of adaptation of Kalman filtering techniques to aerospace applications and eventually to fields as diverse as exploration for oil and control of powerplants. Describes scientific breakthroughs and reformulations that transformed Kalman filtering techniques into fundamental tool for analyzing and solving broad class of estimation problems.
A new method to efficiently downdate an estimate and covariance generated by a discrete time Square Root Information Filter (SRIF) is presented. The method combines the QR factor downdating algorithm of Gill and the decentralized SRIF algorithm of Bierman. Efficient removal of either measurements or a priori information is possible without loss of numerical integrity. Moreover, the method includes features for detecting potential numerical degradation. Performance on a 300 parameter system with 5800 data points shows that the method can be used in real time and hence is a promising tool for interactive data analysis. Additionally, updating a time-varying SRIF filter with either additional measurements or a priori information proceeds analogously.
Open loop frequency acquisition performance is discussed for suppressed carrier binary phase shift keyed signals in terms of the probability of detecting the carrier frequency offset when the arms of the Costas loop detector have one pole filters. The approach, which does not require symbol timing, uses fast Fourier transforms (FFTs) to detect the carrier frequency offset. The detection probability, which depends on both the 3 dB arm filter bandwidth and the received symbol signal to noise ratio, is derived and is shown to be independent of symbol timing. It is shown that the performance of this technique is slightly better that other open loop acquisition techniques which use integrators in the arms and whose detection performance varies with symbol timing.
The existence of one-sided filters, for spectral Fourier approximations of discontinuous functions, which can recover spectral accuracy up to discontinuity from one side, was proved. A least square procedure was also used to construct such a filter and test it on several discontinuous functions numerically.
An algorithm to compute Markov parameters of an observer or Kalman filter from experimental input and output data is discussed. The Markov parameters can then be used for identification of a state space representation, with associated Kalman gain or observer gain, for the purpose of controller design. The algorithm is a non-recursive matrix version of two recursive algorithms developed in previous works for different purposes. The relationship between these other algorithms is developed. The new matrix formulation here gives insight into the existence and uniqueness of solutions of certain equations and gives bounds on the proper choice of observer order. It is shown that if one uses data containing noise, and seeks the fastest possible deterministic observer, the deadbeat observer, one instead obtains the Kalman filter, which is the fastest possible observer in the stochastic environment. Results are demonstrated in numerical studies and in experiments on an ten-bay truss structure.
An optical correlation-based system is demonstrated which recognizes an object and determines its angular orientation by traversing a hierarchical data base of binary filters. The data-base architecture is made possible by the development of binary synthetic discriminant function filters.
The NASA Scatterometer, NSCAT, is an active spaceborne radar designed to measure the normalized radar backscatter coefficient (sigma0) of the ocean surface. These measurements can, in turn, be used to infer the surface vector wind over the ocean using a geophysical model function. Several ambiguous wind vectors result because of the nature of the model function. A median-filter-based ambiguity removal algorithm will be used by the NSCAT ground data processor to select the best wind vector from the set of ambiguous wind vectors. This process is commonly known as dealiasing or ambiguity removal. The baseline NSCAT ambiguity removal algorithm and the method used to select the set of optimum parameter values are described. An extensive simulation of the NSCAT instrument and ground data processor provides a means of testing the resulting tuned algorithm. This simulation generates the ambiguous wind-field vectors expected from the instrument as it orbits over a set of realistic meoscale wind fields. The ambiguous wind field is then dealiased using the median-based ambiguity removal algorithm. Performance is measured by comparison of the unambiguous wind fields with the true wind fields. Results have shown that the median-filter-based ambiguity removal algorithm satisfies NSCAT mission requirements.
The ability to detect and locate obstacles using on-board sensors and modify the nominal trajectory is necessary for safe landing of an autonomous lander on Mars. This paper examines some of the issues in the location of objects using a sequence of images from a passive sensor, and describes a Kalman filter approach to improve the range estimation to obstacles. The filter is also used to track features in the images leading to a significant reduction of search effort in the feature extraction step of the algorithm. The lack of suitable flight imagery data presents a problem in the verification of concepts for obstacle detection. An experiment is designed to acquire a sequence of images along with sensor motion parameters and the range estimation results using this imagery are presented.
The observation of photon-assisted resonant tunneling in a multiple quantum well structure composed of doped quantum wells separated by variably space superlattice energy filters is reported. Electrons confined in the quantum wells are excited to the second quantized state by intersubband absorption of incident infrared radiation and are subsequently emitted through the filters under appropriate bias conditions. This is manifested by a distinct peak, with an associated negative differential photoconductance, in the photocurrent versus bias voltage characteristic at low temperatures.
An algorithm to compute Markov parameters of an observer or Kalman filter from experimental input and output data is discussed. The Markov parameters can then be used for identification of a state space representation, with associated Kalman gain or observer gain, for the purpose of controller design. The algorithm is a non-recursive matrix version of two recursive algorithms developed in previous works for different purposes. The relationship between these other algorithms is developed. The new matrix formulation here gives insight into the existence and uniqueness of solutions of certain equations and gives bounds on the proper choice of observer order. It is shown that if one uses data containing noise, and seeks the fastest possible deterministic observer, the deadbeat observer, one instead obtains the Kalman filter, which is the fastest possible observer in the stochastic environment. Results are demonstrated in numerical studies and in experiments on a ten-bay truss structure.
Experiments demonstrated feasibility of synthetic-discriminant-function filter encoded in binary spatial light modulator. Filter is part of optoelectronic apparatus indicating degree of correlation between input image and any number of training images of same object at various positions and orientations. Used in optical correlator to recognize rotated images, or in tracking system to recognize moving vehicle or other substantially rigid object from any direction.