Recursive algorithm for the calculation of the adaptive Kalman filter weighting coefficients.
Weighting coefficients calculations by recursive algorithm for designing optimal discrete adaptive Kalman filter
Lainiotis, D. G.↗
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Computation of antenna vibration mode elastic-rigid and effective-weight coefficients
A significant amount of both qualitative and quantitative information about structures can be derived from the elastic-rigid structural coupling matrices, and from the effective modal inertias and masses. Typical finite-element analysis programs do not provide this information as part of the regular output. This report presents an easy way to develop the necessary information from ordinary program output, and shows how to deal with alternative origins for the reference coordinate system.
Levy, R.↗
Computation of vibration mode elastic-rigid and effective weight coefficients from finite-element computer program output
Post-processing algorithms are given to compute the vibratory elastic-rigid coupling matrices and the modal contributions to the rigid-body mass matrices and to the effective modal inertias and masses. Recomputation of the elastic-rigid coupling matrices for a change in origin is also described. A computational example is included. The algorithms can all be executed by using standard finite-element program eigenvalue analysis output with no changes to existing code or source programs.
Levy, R.↗
Ionospheric modeling at Air Force Global Weather Central
The four Dimensional Ionospheric Model (4-D) is described. The 4-D integrates a wide variety of ionospheric data types into a consistent ionospheric specification. At each observing location, the 4-D reduces an entire electron density profile to four weighting coefficients. These weighting coefficients are interconnected in time and space by spectral analysis techniques. The resultant field of spectral coefficients can be used to reconstruct an electron density profile at any latitude, longitude and time.
Tascione, T. F.↗
Aerodynamic Limitations of the UH-60A Rotor
High quality airloads data have been obtained on an instrumented UH-60A in flight and these data provide insight into the aerodynamic limiting behavior of the rotor. At moderate weight coefficients and high advance ratio limiting performance is largely caused by high drag near the blade tip on the advancing side of the rotor as supercritical flow develops on the rotor with moderate to strong, shocks on both surfaces of the blade. Drag divergence data from two-dimensional airfoil tests show good agreement with the development of the supercritical flow regions. Large aerodynamic pitching moments are observed at high advance ratio, as well, and these pitching moments are the source of high torsional moments on the blade and control system loads. These loads occur on the advancing side of the disk and are not related to blade stall which does not occur for these weight coefficients. At high weight coefficients aerodynamic and structural limits are related to dynamic stall cycles that begin on the retreating side of the blade and, for the most severe conditions, carry around to the advancing side of the blade at the presumed first frequency of the blade/control system.
Coleman, Colin P.↗
Optimum combining of residual carrier array signals in correlated noises
An array feed combining system for the recovery of signal-to-noise ratio (SNR) loss due to antenna reflector deformation has been implemented and is currently being evaluated on the Jet Propulsion Laboratory 34-m DSS-13 antenna. The current signal-combining system operates under the assumption that the white Gaussian noise processes in the received signals from different array elements are mutually uncorrelated. However, experimental data at DSS 13 indicate that these noise processes are indeed mutually correlated. The objective of this work is to develop a signal-combining system optimized to account for the mutual correlations between these noise processes. The set of optimum combining weight coefficients that maximizes the combined signal SNR in the correlated noises environment is determined. These optimum weights depend on unknown signal and noise covariance parameters. A maximum-likelihood approach is developed to estimate these unknown parameters to obtain estimates of the optimum weight coefficients based on residual carrier signal samples. The actual combined signal SNR using the estimated weight coefficients is derived and shown to converge to the maximum achievable SNR as the number of signal samples increases. These results are also verified by simulation. A numerical example shows a significant improvement in SNR performance can be obtained, especially when the amount of correlation increases.
Liang, R.↗
Optimum Combining of Residual Carrier Array Signals in Correlated Noises
An array feed combining system for the recovery of signal-to-noise ratio (SNR) loss due to antenna reflector deformation has been implemented and is currently being evaluated on the Jet Propulsion Laboratory 34-m DSS-13 antenna. The current signal-combining system operates under the assumption that the white Gaussian noise processes in the received signals from different array elements are mutually uncorrelated. However, experimental data at DSS 13 indicate that these noise processes are indeed mutually correlated. The objective of this work is to develop a signal-combining system optimized to account for the mutual correlations between these noise processes. The set of optimum combining weight coefficients that maximizes the combined signal SNR in the correlated noises environment is determined. These optimum weights depend on unknown signal and noise covariance parameters. A maximum-likelihood approach is developed to estimate these unknown parameters to obtain estimates of the optimum weight coefficients based on residual carrier signal samples. The actual combined signal SNR using the estimated weight coefficients is derived and shown to converge to the maximum achievable SNR as the number of signal samples increases. These results are also verified by simulation. A numerical example shows a significant improvement in SNR performance can be obtained, especially when the amount of correlation increases.
Tan, H. H.↗
Use of scan overlap redundancy to enhance multispectral aircraft scanner data
Two criteria were suggested for optimizing the resolution error versus signal-to-noise-ratio tradeoff. The first criterion uses equal weighting coefficients and chooses n, the number of lines averaged, so as to make the average resolution error equal to the noise error. The second criterion adjusts both the number and relative sizes of the weighting coefficients so as to minimize the total error (resolution error plus noise error). The optimum set of coefficients depends upon the geometry of the resolution element, the number of redundant scan lines, the scan line increment, and the original signal-to-noise ratio of the channel. Programs were developed to find the optimum number and relative weights of the averaging coefficients. A working definition of signal-to-noise ratio was given and used to try line averaging on a typical set of data. Line averaging was evaluated only with respect to its effect on classification accuracy.
Lindenlaub, J. C.↗
Sub-Audible Speech Recognition Based upon Electromyographic Signals
Method and system for processing and identifying a sub-audible signal formed by a source of sub-audible sounds. Sequences of samples of sub-audible sound patterns ("SASPs") for known words/phrases in a selected database are received for overlapping time intervals, and Signal Processing Transforms ("SPTs") are formed for each sample, as part of a matrix of entry values. The matrix is decomposed into contiguous, non-overlapping two-dimensional cells of entries, and neural net analysis is applied to estimate reference sets of weight coefficients that provide sums with optimal matches to reference sets of values. The reference sets of weight coefficients are used to determine a correspondence between a new (unknown) word/phrase and a word/phrase in the database.
Jorgensen, Charles C.↗
ReMU: regional minimal updating for model-based derivative-free optimization
Derivative-free optimization (DFO) problems are optimization problems where derivative information is unavailable or extremely difficult to obtain. Model-based DFO solvers have been applied extensively in scientific computing. Powell's NEWUOA (2004) [Powell, The NEWUOA software for unconstrained optimization without derivatives, in Large-Scale Nonlinear Optimization, Nonconvex Optimization and its Applications Vol. 83, G. Di Pillo and M. Roma, eds., Springer, 2006, pp. 255–297] and Wild's POUNDerS (2014) [Wild, Solving derivative-free nonlinear least squares problems with POUNDERS, in Advances and Trends in Optimization with Engineering Applications, T. Terlaky, M.F. Anjos, and S. Ahmed, eds., SIAM, 2017, pp. 529–540] explore the numerical power of the minimal norm Hessian (MNH) model for DFO and contributed to the open discussion on building better models with fewer data to achieve faster numerical convergence. Another decade later, we propose the regional minimal updating (ReMU) models, and extend the previous models into a broader class, including the H 2 norm models [Xie and Yuan, Least H 2 norm updating of quadratic interpolation models for derivative-free trust-region algorithms, IMA J. Numer. Anal. 46 (2025), pp. 21–50]. This paper shows motivation behind ReMU models, computational details, theoretical and numerical results on particular extreme points and the barycentre of ReMU's weight coefficient region, and the associated KKT matrix error and distance. Novel metrics, such as the truncated Newton step error, are proposed to numerically understand the new models' properties. A new algorithmic strategy, based on iteratively adjusting the ReMU model type, is also proposed, and shows numerical advantages by combining and switching between the barycentric model and the classic least Frobenius norm model in an online fashion.
derivative-free trust-region methods↗
Hardware Implementation of a Bilateral Subtraction Filter
A bilateral subtraction filter has been implemented as a hardware module in the form of a field-programmable gate array (FPGA). In general, a bilateral subtraction filter is a key subsystem of a high-quality stereoscopic machine vision system that utilizes images that are large and/or dense. Bilateral subtraction filters have been implemented in software on general-purpose computers, but the processing speeds attainable in this way even on computers containing the fastest processors are insufficient for real-time applications. The present FPGA bilateral subtraction filter is intended to accelerate processing to real-time speed and to be a prototype of a link in a stereoscopic-machine- vision processing chain, now under development, that would process large and/or dense images in real time and would be implemented in an FPGA. In terms that are necessarily oversimplified for the sake of brevity, a bilateral subtraction filter is a smoothing, edge-preserving filter for suppressing low-frequency noise. The filter operation amounts to replacing the value for each pixel with a weighted average of the values of that pixel and the neighboring pixels in a predefined neighborhood or window (e.g., a 9 9 window). The filter weights depend partly on pixel values and partly on the window size. The present FPGA implementation of a bilateral subtraction filter utilizes a 9 9 window. This implementation was designed to take advantage of the ability to do many of the component computations in parallel pipelines to enable processing of image data at the rate at which they are generated. The filter can be considered to be divided into the following parts (see figure): a) An image pixel pipeline with a 9 9- pixel window generator, b) An array of processing elements; c) An adder tree; d) A smoothing-and-delaying unit; and e) A subtraction unit. After each 9 9 window is created, the affected pixel data are fed to the processing elements. Each processing element is fed the pixel value for its position in the window as well as the pixel value for the central pixel of the window. The absolute difference between these two pixel values is calculated and used as an address in a lookup table. Each processing element has a lookup table, unique for its position in the window, containing the weight coefficients for the Gaussian function for that position. The pixel value is multiplied by the weight, and the outputs of the processing element are the weight and pixel-value weight product. The products and weights are fed to the adder tree. The sum of the products and the sum of the weights are fed to the divider, which computes the sum of products the sum of weights. The output of the divider is denoted the bilateral smoothed image. The smoothing function is a simple weighted average computed over a 3 3 subwindow centered in the 9 9 window. After smoothing, the image is delayed by an additional amount of time needed to match the processing time for computing the bilateral smoothed image. The bilateral smoothed image is then subtracted from the 3 3 smoothed image to produce the final output. The prototype filter as implemented in a commercially available FPGA processes one pixel per clock cycle. Operation at a clock speed of 66 MHz has been demonstrated, and results of a static timing analysis have been interpreted as suggesting that the clock speed could be increased to as much as 100 MHz.
Huertas, Andres↗
Petrogenesis of KREEP
Solid/liquid distribution coefficients (weight basis) were experimentally determined for a number of trace elements for olivine, orthopyroxene, plagioclase and ilmenite. Values of distribution coefficients were measured at 1200 C and a f sub O2 of 10 to the -13.0 power for liquids similar in composition to the olivine-opx-plagioclase peritectic in the pseudoternary system (Fe,Mg)2SiO4-CaAl2Si2O8-SiO2. Values were also measured at 1140 C and a f sub O2 of 10 to the -12.8 power for liquids similar in composition to high-Ti mare basalts. Major and trace element partitioning and relevant phase equilibria were used to investigate possible parent-daughter relationships between a number of highland samples and highly evolved KREEP-rich materials. Out of about 80 highlands samples tested, 33 were found to be possible parents to the KREEP-rich materials. The average composition of these samples is very similar to that of the Low-K Fra Mauro basalt (LKFM). A model is proposed to explain the production of LKFM-type material and more evolved members of the KREEP suite.
Mckay, G. A.↗
Analysis of MIR-18 results for physical and biological dosimetry: radiation shielding effectiveness in LEO
We compare models of radiation transport and biological response to physical and biological dosimetry results from astronauts on the Mir space station. Transport models are shown to be in good agreement with physical measurements and indicate that the ratio of equivalent dose from the Galactic Cosmic Rays (GCR) to protons is about 3/2:1 and that this ratio will increase for exposures to internal organs. Two biological response models are used to compare to the Mir biodosimetry for chromosome aberration in lymphocyte cells; a track-structure model and the linear-quadratic model with linear energy transfer (LET) dependent weighting coefficients. These models are fit to in vitro data for aberration formation in human lymphocytes by photons and charged particles. Both models are found to be in reasonable agreement with data for aberrations in lymphocytes of Mir crew members: however there are differences between the use of LET dependent weighting factors and track structure models for assigning radiation quality factors. The major difference in the models is the increased effectiveness predicted by the track model for low charge and energy ions with LET near 10 keV/micrometers. The results of our calculations indicate that aluminum shielding, although providing important mitigation of the effects of trapped radiation, provides no protective effect from the galactic cosmic rays (GCR) in low-earth orbit (LEO) using either equivalent dose or the number of chromosome aberrations as a measure until about 100 g/cm 2 of material is used.
Flight Experiment↗
Quantitative Estimation of Some Possible Errors Associated With TRMM GV Monthly Rain Maps and a Scheme for Spatial Averaging of Radar Accumulations
The Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) rain products are generated from ground-based radar observations using a bulk adjusted FACE radar reflectivity (Ze) - rain rate (R) relationship. Monthly Ze-R relationships are obtained by extracting the Ze data with a horizontal resolution of 2 km from an altitude of 1.5 km (from Constant Altitude PPIS, CAPPIS) over the locations of the gauges. The gauge and radar data are merged, and a quality control procedure is applied that eliminates poorly correlated gauge-radar data. Bulk adjusted Ze-R relations are then derived from this quality-controlled merged data base. These Z-R relations are applied to the base scan reflectivity to produce rain maps. The present study examines the effect of these different planes on the total accumulations from the radar and the associated effect on the bulk adjustment coefficient. In order to evaluate the credibility of these GV products, the effect of the different planes involved in the bulk adjustment method will be discussed, The gauge data will also be used to assess the impact of missing radar data (i.e., data gaps) upon the monthly radar-derived rainfall estimates. Experiments were also performed varying both the area of the radar data extracted over the gauges and the use of different weighting coefficients. Preliminary results from these sensitivity tests indicate improved agreement and consistency between the gauge and radar data.
Datta, Saswati↗
Dynamic Model Development of a Wind Power Plant Using Neural Net Method to Forecast Wind Power Output (CRADA Final Report)
This project is intended to model wind power plant based on monitored data at the wind power plant. This project will promote the university research in Renewable Energy area and trains the future highly qualified engineers. The dynamic model will be based on neural net model with the input from the two met towers (12 inputs), and the number of turbines in operation (one input). The overall input will be 13 inputs to drive the simulations. The output power at the point of interconnection will be used to tune the neural net weight coefficients. Two neural net concepts will be investigated (the back propagation neural net and the dynamic recurrent neural net with feedback).
17 WIND ENERGY↗
Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures
Increased data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present new challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different continuous wavelet transform (CWT) reconstruction formulas are presented and tested under different signal to noise ratio (SNR) conditions. A sparse superposition of Nth order Gabor atoms worked well against a synthetic blast transient using the wavelet entropy and an entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for sparse feature extraction and dictionary-based machine learning across multiple sensor modalities.
46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
Preprocessing of multispectral data and simulation of ERTS data channels to make computer terrain maps of a Yellowstone National Park test site
The possibility of improving the accuracy of terrain classification by preprocessing spectral data was investigated. Terrain maps were made using the following techniques: 1) preprocessing by scan angle function transformation, using the computer-selected best set of three channels; and 2) preprocessing by ratio transformation, using the specified ERTS data channels, simulated by fitting the spectral response of each of the 12 data channels to the ERTS channels by a set of weighting coefficients. By using a simple technique during printout, the maps were produced in color. The normalized scan angle function transformation resulted in the most accurate classification. The best ratio transformation for the Yellowstone Park data was the ratio of each channel to the sum of all channels. A supervised training program involving maximum likelihood decision for selecting the best spectrometer channels and similar techniques for digitizing the data of the analog magnetic tapes were used. Cloud shadows were recognized in addition to eight classes of terrain. Preprocessing of data resulted in more accurate maps, required fewer training areas (hence less preparation and computer time), and enabled much of the area formerly classified as shadow to be reclassified according to actual terrain type.
Smedes, H. W.↗
Applications of charge-coupled device transversal filters to communication
The paper discusses the computational power of state-of-the-art charged-coupled device (CCD) transversal filters in communications applications. Some of the performance limitations of CCD transversal filters are discussed, with attention given to time delay and bandwidth, imperfect charge transfer efficiency, weighting coefficient error, noise, and linearity. The application of CCD transversal filters to matched filtering, spectral filtering, and Fourier analysis is examined. Techniques for making programmable transversal filters are briefly outlined.
Buss, D. D.↗