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At least 235 records · Page 13

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NASA NTRS1979

Some effects of quantization on a noiseless phase-locked loop

If the VCO of a phase-locked receiver is to be replaced by a digitally programmed synthesizer, the phase error signal must be sampled and quantized. Effects of quantizing after the loop filter (frequency quantization) or before (phase error quantization) are investigated. Constant Doppler or Doppler rate noiseless inputs are assumed. The main result gives the phase jitter due to frequency quantization for a Doppler-rate input. By itself, however, frequency quantization is impractical because it makes the loop dynamic range too small.

Greenhall, C. A.↗

NASA NTRS1976

Performance analysis of digital FM demodulators for fading channels in the threshold region

The quasi-optimum digital FM demodulators for fading channels reported earlier (1975) reflected the system performance accurately under high signal to noise ratio conditions. In this paper, the prediction of the system performance for low SNR values is considered. A new set of error variance algorithms is developed from the filter algorithm assuming a Gaussian distribution for the state estimation errors and taking the effect of high frequency terms into consideration. Simulation analysis for an FM system with Rician fading channel shows that these algorithms predict the system performance accurately in the threshold region.

Dharamsi, M. T.↗

DOE OSTI2023

Semi-Implicit Computation of Fast Modes in a Scheme Integrating Slow Modes by a Leapfrog Method Based on a Selective Implicit Time Filter

Abstract A scheme for integration of atmospheric equations containing terms with differing time scales is developed. The method employs a filtered leapfrog scheme utilizing a fourth-order implicit time filter with one function evaluation per time step to compute slow-propagating phenomena such as advection and rotation. The terms involving fast-propagating modes are handled implicitly with an unconditionally stable method that permits application of larger time steps and faster computations compared to fully explicit treatment. Implementation using explicit and recurrent formulation is provided. Stability analysis demonstrates that the method is conditionally stable for any combination of frequencies involved in the slow and fast terms as they approach the origin. The implicit filter used in the method damps the computational modes without noticeably sacrificing the accuracy of the physical mode. TheO[(Δt 4 )] accuracy for amplitude errors achieved by the implicitly filtered leapfrog is preserved in applications where terms responsible for fast propagation are integrated with a semi-implicit method. Detailed formulation of the method for soundproof nonhydrostatic anelastic equations is provided. Procedures for implementation in global spectral shallow-water models are also given. Examples comparing numerical and analytical solutions for linear gravity waves demonstrate the accuracy of the scheme. The performance is also shown in more practical nonlinear applications, where numerical solutions accomplished by the method are evaluated against those computed from a scheme where the slow terms are handled by the third-order Runge–Kutta scheme. It demonstrates that the method is able to accurately resolve fine-scale dynamics of Kelvin–Helmholtz shear instabilities, the evolution of density current, and nonlinear drifts of twin tropical cyclones.

Meteorology & Atmospheric Sciences↗

NASA NTRS1992

Rate 3/4 coded 16-QAM for uplink applications

First phase development of an advanced modulation technology which synergistically combines coding and modulation to achieve 2 bits per second per Hertz bandwidth efficiency in satellite demodulators is nearing completion. A proof-of-concept model is being developed to demonstrate technology feasibility, establish practical bandwidth efficiency limitations, and provide a data base for the design and development of engineering model satellite demodulators. The basic considerations leading to the choice of 4 x 4 quadrature amplitude modulation (16-QAM) and its associated coding format are discussed, along with the basic implementation of the carrier and clock recovery, automatic gain control, and decoding process. Preliminary performance results are presented. Spectra for the modulated signal shows the effects of the square root Nyquist filters in the modulation. Bit error rate (BER) results for the encoder/decoder subsystem show near ideal results, although power consumption is high and baseband BER performance of the Nyquist filter set is poor. Recommendations regarding the present system to improve BER performance and acquisition speed are given.

Mrozek, Eric M.↗

NASA NTRS1993

Radio Doppler navigation of interplanetary spacecraft using different data processing modes

Doppler shift measurements derived from closed-loop radio tracking of distant spacecraft by ground stations of the Deep Space Network (DSN) are one of the principal means used for interplanetary navigation. This paper describes an investigation of the navigation accuracy that can be achieved with two-way coherent X-band (7.2 to 8.4 GHz) Doppler phase and frequency data; both theoretical and practical aspects of the two Doppler formulations are addressed. A new filtering strategy is also proposed, which differs from current approaches in that most of the ground system calibration errors affecting the Doppler data are represented as filter parameters, in addition to the spacecraft trajectory parameters. Error covariance computations for application of the proposed filter to a navigation scenario derived from the Mars Observer mission are provided, in order to assess the performance that might be obtained in practice. The results indicate that with a phase formulation of the Doppler data and the new filter, navigation accuracies of 15 km at Mars (0.05 microrad in an angular sense) are possible with the DSN's present X-band Doppler tracking system.

Thurman, S. W.↗

NASA NTRS2003

An optimal modification of a Kalman filter for time scales

The Kalman filter in question, which was implemented in the time scale algorithm TA(NIST), produces time scales with poor short-term stability. A simple modification of the error covariance matrix allows the filter to produce time scales with good stability at all averaging times, as verified by simulations of clock ensembles.

time scale Kalman filter↗

NASA NTRS1975

The use of Whatman-41 filters for high volume air sampling

The feasibility of using W41 filter media on a routine TSP high-volume monitoring network was determined by comparison with glass fiber (GF) filtering. Results indicate that suspended particulate samples from GF filters averaged slightly, but not significantly, higher than those from Whatman-41 filters. Some extra handling procedures were required to avoid errors due to the hygroscopic nature of W41 filters; these added procedures are not overly burdensome, however, and they allow the performance of analytical work, thus extending the capabilities of high-volume sampling. It was demonstrated that W41 filters are practical for air quality monitoring and elemental analysis in environments similar to Cleveland's.

Neustadter, H. E.↗

NASA NTRS2009

Photometric Studies of Orbital Debris at GEO

Orbital debris represents a significant and increasing risk to operational spacecraft. Here we report on photometric observations made in standard BVRI filters at the Cerro Tololo Inter-American Observatory (CTIO) in an effort to determine the physical characteristics of optically faint debris at geosynchronous Earth orbit (GEO). Our sample is taken from GEO objects discovered in a survey with the University of Michigan s 0.6-m Curtis-Schmidt telescope (known as MODEST, for Michigan Orbital DEbris Survey Telescope), and then followed up in real-time with the CTIO/SMARTS 0.9-m for orbits and photometry. For a sample of 50 objects, calibrated sequences in RB- V-I-R filters have been obtained with the CTIO/SMARTS 0.9-m. For objects that do not show large brightness variations, the colors are largely redder than solar in both B-R and R-I. The width of the color distribution may be intrinsic to the nature of the surfaces, but also could imply that we are seeing irregularly shaped objects and measuring the colors at different times with just one telescope. For irregularly shaped objects tumbling at unknown orientations and rates, such sequential filter measurements using one telescope are subject to large errors for interpretation. If all observations in all filters in a particular sequence are of the same surface at the same solar and viewing angles, then the colors are meaningful. Where this is not the case, interpretation of the observed colors is impossible. For a smaller sample of objects we have observed with synchronized CCD cameras on the two telescopes. The CTIO/SMARTS 0.9-m observes in B, and the Schmidt in R. The CCD cameras are electronically linked together so that the start time and duration of observations are both the same to better than 50 milliseconds. Now the observed B-R color is a true measure of the scattered illuminated area of the debris piece for that observation.

Seitzer, Patrick↗

NASA NTRS1990

Multistage estimation of received carrier signal parameters under very high dynamic conditions of the receiver

A multistage estimator is provided for the parameters of a received carrier signal possibly phase-modulated by unknown data and experiencing very high Doppler, Doppler rate, etc., as may arise, for example, in the case of Global Positioning Systems (GPS) where the signal parameters are directly related to the position, velocity and jerk of the GPS ground-based receiver. In a two-stage embodiment of the more general multistage scheme, the first stage, selected to be a modified least squares algorithm referred to as differential least squares (DLS), operates as a coarse estimator resulting in higher rms estimation errors but with a relatively small probability of the frequency estimation error exceeding one-half of the sampling frequency, provides relatively coarse estimates of the frequency and its derivatives. The second stage of the estimator, an extended Kalman filter (EKF), operates on the error signal available from the first stage refining the overall estimates of the phase along with a more refined estimate of frequency as well and in the process also reduces the number of cycle slips.

Kumar, Rajendra↗

NASA NTRS1991

Multistage estimation of received carrier signal parameters under very high dynamic conditions of the receiver

Kumar, Rajendra↗

NASA NTRS1997

Accurate Magnetometer/Gyroscope Attitudes Using a Filter with Correlated Sensor Noise

Magnetometers and gyroscopes have been shown to provide very accurate attitudes for a variety of spacecraft. These results have been obtained, however, using a batch-least-squares algorithm and long periods of data. For use in onboard applications, attitudes are best determined using sequential estimators such as the Kalman filter. When a filter is used to determine attitudes using magnetometer and gyroscope data for input, the resulting accuracy is limited by both the sensor accuracies and errors inherent in the Earth magnetic field model. The Kalman filter accounts for the random component by modeling the magnetometer and gyroscope errors as white noise processes. However, even when these tuning parameters are physically realistic, the rate biases (included in the state vector) have been found to show systematic oscillations. These are attributed to the field model errors. If the gyroscope noise is sufficiently small, the tuned filter 'memory' will be long compared to the orbital period. In this case, the variations in the rate bias induced by field model errors are substantially reduced. Mistuning the filter to have a short memory time leads to strongly oscillating rate biases and increased attitude errors. To reduce the effect of the magnetic field model errors, these errors are estimated within the filter and used to correct the reference model. An exponentially-correlated noise model is used to represent the filter estimate of the systematic error. Results from several test cases using in-flight data from the Compton Gamma Ray Observatory are presented. These tests emphasize magnetometer errors, but the method is generally applicable to any sensor subject to a combination of random and systematic noise.

Sedlak, J.↗

NASA NTRS2003

Nonlinear Observers for Gyro Calibration

High precision estimation and control algorithms, to achieve unprecedented levels of pointing accuracy, will be required to support future formation flying missions such as interferometry missions. Achieving high pointing accuracy requires precise knowledge of the spacecraft rotation rate. Typically, the rotation rate is measured by a gyro. The measured rates can be corrupted by errors in alignment and scale factor, gyro biases, and noise. In this work, we present nonlinear observers for gyro calibration. Nonlinear observers are superior to extended or pseudo-linear Kalman filter type approaches for large errors and global stability. Three nonlinear gyro calibration observers are developed. The first observer estimates a constant gyro bias. The second observer estimates scale factor errors. The third observer estimates the gyro alignment for three orthogonal gyros. The convergence properties of all three observers are discussed. Additionally, all three observers are coupled with a nonlinear control algorithm. The stability of each of the resulting closed loop systems is analyzed. The observers are then combined, and the gyro calibration parameters are estimated simultaneously. The stability of the combined observers is addressed, as well as the stability of the resulting closed loop systems. Simulated test results are presented for each scenario. Finally, the nonlinear observers are compared to a pseudo-linear Kalman filter.

Thienel, Julie↗

NASA NTRS1995

Estimation of attitude sensor timetag biases

This paper presents an extended Kalman filter for estimating attitude sensor timing errors. Spacecraft attitude is determined by finding the mean rotation from a set of reference vectors in inertial space to the corresponding observed vectors in the body frame. Any timing errors in the observations can lead to attitude errors if either the spacecraft is rotating or the reference vectors themselves vary with time. The state vector here consists of the attitude quaternion, timetag biases, and, optionally, gyro drift rate biases. The filter models the timetags as random walk processes: their expectation values propagate as constants and white noise contributes to their covariance. Thus, this filter is applicable to cases where the true timing errors are constant or slowly varying. The observability of the state vector is studied first through an examination of the algebraic observability condition and then through several examples with simulated star tracker timing errors. The examples use both simulated and actual flight data from the Extreme Ultraviolet Explorer (EUVE). The flight data come from times when EUVE had a constant rotation rate, while the simulated data feature large angle attitude maneuvers. The tests include cases with timetag errors on one or two sensors, both constant and time-varying, and with and without gyro bias errors. Due to EUVE's sensor geometry, the observability of the state vector is severely limited when the spacecraft rotation rate is constant. In the absence of attitude maneuvers, the state elements are highly correlated, and the state estimate is unreliable. The estimates are particularly sensitive to filter mistuning in this case. The EUVE geometry, though, is a degenerate case having coplanar sensors and rotation vector. Observability is much improved and the filter performs well when the rate is either varying or noncoplanar with the sensors, as during a slew. Even with bad geometry and constant rates, if gyro biases are independently known, the timetag error for a single sensor can be accurately estimated as long as its boresight is not too close to the spacecraft rotation axis.

Sedlak, J.↗

NASA NTRS1993

Filtering Interpolators for Image Comparison Algorithms

Comparing two or more images, either by differencing or ratioing, is important to many remote sensing problems. Because the pixel sample points for the images are (almost) always separated by some nonzero shift, a resampling, or interpolation, process must be performed if one image is to be accurately compared to another. Considered in Fourier space, an interpolator acts as a filter that attenuates some frequencies (usually high) of the image. Thus, when the shifted and unshifted images are compared, the former has been filtered, while the latter has not; the effect of this difference is called interpolation error. The key idea of this paper is to apply a filter to the unshifted image that matches the filtering effect of applying the interpolator to the shifted image, thereby drastically reducing interpolation error. The resulting interpolators, called filtering interpolators, are derived and discussed in detail elsewhere. Basic results will be given in this presentation.

Lucke, R. L.↗

NASA NTRS1996

Canonical Signed Digit Study: FIR Digital Filter Simulation Results - Part 2

Finite Impulse Response digital filter using Canonical Signed-Digit (CSD) number representation for the coefficients has been studied and its computer simulation results are presented here. Minimum Mean Square Error (MMSE) criterion is employed to optimize filter coefficients into the corresponding CSD numbers. To further improve coefficients optimization process, an extra non-zero bit is added for any filter coefficients exceeding 1/2. This technique improves frequency response of filter without increasing filter complexity almost at all. The simulation results show outstanding performance in bit-error-rate (BER) curve for all CSD implemented digital filters included in this presentation material.

Kim, Heechul↗

NASA NTRS1998

A transition matrix approach to the Davenport gryo calibration scheme

The in-flight gyro calibration scheme commonly used by NASA Goddard Space Flight Center (GSFC) attitude ground support teams closely follows an original version of the Davenport algorithm developed in the late seventies. Its basic idea is to minimize the least-squares differences between attitudes gyro- propagated over the course of a maneuver and those determined using post- maneuver sensor measurements. The paper represents the scheme in a recursive form by combining necessary partials into a rectangular matrix, which is propagated in exactly the same way as a Kalman filters square transition matrix. The nontrivial structure of the propagation matrix arises from the fact that attitude errors are not included in the state vector, and therefore their derivatives with respect to estimated a parameters do not appear in the transition matrix gyro defined in the conventional way. In cases when the required accuracy can be achieved by a single iteration, representation of the Davenport gyro calibration scheme in a recursive form allows one to discard each gyro measurement immediately after it was used to propagate the attitude and state transition matrix. Another advantage of the new approach is that it utilizes the same expression for the error sensitivity matrix as that used by the Kalman filter. As a result the suggested modification of the Davenport algorithm made it possible to reuse software modules implemented in the Kalman filter estimator, where both attitude errors and gyro calibration parameters are included in the state vector. The new approach has been implemented in the ground calibration utilities used to support the Tropical Rainfall Measuring Mission (TRMM). The paper analyzes some preliminary results of gyro calibration performed by the TRMM ground attitude support team. It is demonstrated that an effect of the second iteration on estimated values of calibration parameters is negligibly small, and therefore there is no need to store processed gyro data. This opens a promising opportunity for onboard implementation of the suggested recursive procedure by combining, it with the Kalman filter used to obtain necessary attitude solutions at the beginning and end of each maneuver.

Natanson, G. A.↗

NASA NTRS1987

A model-free method for mass spectrometer response correction

A new method for correction of mass spectrometer output signals is described. Response-time distortion is reduced independently of any model of mass spectrometer behavior. The delay of the system is found first from the cross-correlation function of a step change and its response. A two-sided time-domain digital correction filter (deconvolution filter) is generated next from the same step response data using a regression procedure. Other data are corrected using the filter and delay. The mean squared error between a step response and a step is reduced considerably more after the use of a deconvolution filter than after the application of a second-order model correction. O2 consumption and CO2 production values calculated from data corrupted by a simulated dynamic process return to near the uncorrupted values after correction. Although a clean step response or the ensemble average of several responses contaminated with noise is needed for the generation of the filter, random noise of magnitude not above 0.5 percent added to the response to be corrected does not impair the correction severely.

Shykoff, Barbara E.↗

DOE OSTI2023

Benefits and Cautions in Data Assimilation Strategies: An Example of Modeling Groundwater Recharge

Assimilating recent observations improves model outcomes for real-time assessments of groundwater processes. This is demonstrated in estimating time-varying recharge to a shallow fractured-rock aquifer in response to precipitation. Results from estimating the time-varying water-table altitude (h) and recharge, and their error covariances, are compared for forecasting, filtering, and fixed-lag smoothing (FLS), which are implemented using the Kalman Filter as applied to a data-driven, mechanistic model of recharge. Forecasting uses past observations to predict future states and is the current paradigm in most groundwater modeling investigations; filtering assimilates observations up to the current time to estimate current states; and FLS estimates states following a time lag over which additional observations are collected. Results for forecasting yield a large error covariance relative to the magnitude of the expected recharge. With assimilating recent observations of h, filtering and FLS produce estimates of recharge that better represent time-varying observations of h and reduce uncertainty in comparison to forecasting. Although model outcomes from applying data assimilation through filtering or FLS reduce model uncertainty, they are not necessarily mass conservative, whereas forecasting outcomes are mass conservative. Mass conservative outcomes from forecasting are not necessarily more accurate, because process errors are inherent in any model. Improvements in estimating real-time groundwater conditions that better represent observations need to be weighed for the model application against outcomes with inherent process deficiencies. In conclusion, results from data assimilation strategies discussed in this investigation are anticipated to be relevant to other groundwater processes models where system states are sensitive to system inputs.

aquifer recharge↗