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At least 163 records · Page 9

The Effects of Noise on Polarimetric SAR Data

Polarimetric SAR data can provide a great deal of information about the scattering behavior of the surface under observation. Polarimetric SAR systems often measure the scattering matrices of the areas under observation in linear polarizations (H and V). From the scattering matrix commonly used forms such as the covariance matrix and the Stokes matrix can be easily derived. Other measures derived from polarimetric SAR data include correlation coefficients between scattering matrix terms and the mode and variance of phase differences between scattering matrix terms. The effects of additive system noise on these measurements is not often considered in the literature on this subject. In this paper, the effects of additive system noise on measurements derived from polarimetric SAR data will be examined. It will be shown how first-order noise effects can be removed and how second-order noise effects can be reduced for some measurements...

Freeman, A.↗

Kalman Filter for Spinning Spacecraft Attitude Estimation

This paper presents a Kalman filter using a seven-component attitude state vector comprising the angular momentum components in an inertial reference frame, the angular momentum components in the body frame, and a rotation angle. The relatively slow variation of these parameters makes this parameterization advantageous for spinning spacecraft attitude estimation. The filter accounts for the constraint that the magnitude of the angular momentum vector is the same in the inertial and body frames by employing a reduced six-component error state. Four variants of the filter, defined by different choices for the reduced error state, are tested against a quaternion-based filter using simulated data for the THEMIS mission. Three of these variants choose three of the components of the error state to be the infinitesimal attitude error angles, facilitating the computation of measurement sensitivity matrices and causing the usual 3x3 attitude covariance matrix to be a submatrix of the 6x6 covariance of the error state. These variants differ in their choice for the other three components of the error state. The variant employing the infinitesimal attitude error angles and the angular momentum components in an inertial reference frame as the error state shows the best combination of robustness and efficiency in the simulations. Attitude estimation results using THEMIS flight data are also presented.

Markley, F. Landis↗

Kalman Filter for Spinning Spacecraft Attitude Estimation

This paper presents a Kalman filter using a seven-component attitude state vector comprising the angular momentum components in an inertial reference frame, the angular momentum components in the body frame, and a rotation angle. The relatively slow variation of these parameters makes this parameterization advantageous for spinning spacecraft attitude estimation. The filter accounts for the constraint that the magnitude of the angular momentum vector is the same in the inertial and body frames by employing a reduced six-component error state. Four variants of the filter, defined by different choices for the reduced error state, are tested against a quaternion-based filter using simulated data for the THEMIS mission. Three of these variants choose three of the components of the error state to be the infinitesimal attitude error angles, facilitating the computation of measurement sensitivity matrices and causing the usual 3x3 attitude covariance matrix to be a submatrix of the 6x6 covariance of the error state. These variants differ in their choice for the other three components of the error state. The variant employing the infinitesimal attitude error angles and the angular momentum components in an inertial reference frame as the error state shows the best combination of robustness and efficiency in the simulations. Attitude estimation results using THEMIS flight data are also presented.

Markley, F. Landis↗

On estimating the Venus spin vector

The improvement in spin vector and probe position estimates one may reasonably expect from the processing of such data is indicated. This was done by duplicating the ensemble calculations associated with a weighed least squares with a priori estimation technique applied to range rate data that were assumed to be unbiased and uncorrelated. The weighting matrix was assumed to be the inverse of the covariance matrix of the noise on the data. Attention is focused primarily on the spin vector estimation.

Argentiero, P. D.↗

Space shuttle guidance, navigation and control equation document no. 4: Precision state and filter weighting matrix extrapolation

The Precision State and Filter Weighting Matrix Extrapolation Routine is described which provides the capability to extrapolate any spacecraft geocentric state vector either backwards or forwards in time through a force field consisting of the earth's primary central-force gravitational attraction and a superimposed perturbing acceleration. The routine also provides the capability of extrapolating the filter-weighting matrix along the precision trajectory. This matrix is a square root form of the error covariance matrix and contains statistical information relative to the accuracies of the state vectors and certain other optionally estimated quantities. The routine is a cooled algorithm for the numerical solution of modified forms of the basic differential equations which are satisfied by the geocentric state vector of the spacecraft's center of mass and by the filter-weighting matrix.

Robertson, W. M.↗

Upper and lower covariance bounds for perturbed linear systems

Both upper and lower bounds are established for state covariance matrices under parameter perturbations of the plant. The motivation for this study lies in the fact that many robustness properties of linear systems are given explicitly in terms of the state covariance matrix. Moreover, there exists a theory for control by covariance assignment. The results provide robustness properties of these covariance controllers.

Xu, J.-H.↗

Gravity field improvement using global positioning system data from TOPEX/Poseidon - A covariance analysis

The TOPEX/Poseidon satellite data can be used to improve the knowledge of the earth's gravitational field. The GPS data are especially useful for improving the gravity field over the world's oceans, where the current tracking data are sparse. Using realistic scenario for processing 10 days of GPS data, a covariance analysis is performed to obtain the expected improvement to the GEM-T2 gravity field. The large amount of GPS data and the large number of parameters (1979 parameters for the gravity field, plus carrier-phase biases, etc.) required special filtering techniques for efficient solution. The gravity-bin technique is used to compute the covariance matrix associated with the spherical harmonic gravity field. The covariance analysis shows that the GPS data from one 10-day arc of TOPEX/Poseidon with no a priori constraints can resolve medium degree and order (3-26) parameters with sigmas (standard deviations) that are an order of magnitude smaller than the corresponding sigmas of GEM-T2. When the information from GEM-T2 is combined with the TOPEX/Poseidon GPS measurements, an order-of-magnitude improvement is observed in low- and medium-degree terms with significant improvements spread over a wide range of degree and order.

Bertiger, Willy I.↗

The Importance of Semi-Major Axis Knowledge in the Determination of Near-Circular Orbits

Modem orbit determination has mostly been accomplished using Cartesian coordinates. This usage has carried over in recent years to the use of GPS for satellite orbit determination. The unprecedented positioning accuracy of GPS has tended to focus attention more on the system's capability to locate the spacecraft's location at a particular epoch than on its accuracy in determination of the orbit, per se. As is well-known, the latter depends on a coordinated knowledge of position, velocity, and the correlation between their errors. Failure to determine a properly coordinated position/velocity state vector at a given epoch can lead to an epoch state that does not propagate well, and/or may not be usable for the execution of orbit adjustment maneuvers. For the quite common case of near-circular orbits, the degree to which position and velocity estimates are properly coordinated is largely captured by the error in semi-major axis (SMA) they jointly produce. Figure 1 depicts the relationships among radius error, speed error, and their correlation which exist for a typical low altitude Earth orbit. Two familiar consequences are the relationship Figure 1 shows are the following: (1) downrange position error grows at the per orbit rate of 3(pi) times the SMA error; (2) a velocity change imparted to the orbit will have an error of (pi) divided by the orbit period times the SMA error. A less familiar consequence occurs in the problem of initializing the covariance matrix for a sequential orbit determination filter. An initial covariance consistent with orbital dynamics should be used if the covariance is to propagate well. Properly accounting for the SMA error of the initial state in the construction of the initial covariance accomplishes half of this objective, by specifying the partition of the covariance corresponding to down-track position and radial velocity errors. The remainder of the in-plane covariance partition may be specified in terms of the flight path angle error of the initial state. Figure 2 illustrates the effect of properly and not properly initializing a covariance. This figure was produced by propagating the covariance shown on the plot, without process noise, in a circular low Earth orbit whose period is 5828.5 seconds. The upper subplot, in which the proper relationships among position, velocity, and their correlation has been used, shows overall error growth, in terms of the standard deviations of the inertial position coordinates, of about half of the lower subplot, whose initial covariance was based on other considerations.

Carpenter, J. Russell↗

Relative Navigation In Elliptical Orbits Using An Iterative Nonlinear Filter

The two step filter is applied to process intersatellite radar measurements to determine the motion of one satellite relative to another in close elliptical orbits. This filter breaks a nonlinear estimation problem into two state vectors. The 'first step' state is chosen so as to have a linear measurement equation. This is nonlinearly related to the 'second step' state which describes the dynamics. Two different forms are used. In one, the first step state is the second step state vector augmented by the measurement equation. In the other, the first step and second step state vectors are of equal dimension. The two step filter is compared against an iterated extended Kalman filter and a Kalman filter using a change of variables. Analytical differences between the two step estimator and these conventional filters are highlighted. Special concerns for initializing the first step state covariance matrix and handling the possibility of numerically rank deficient covariance matrices are addressed. Numerical simulations are performed which show that the Two Step estimator produces a lower estimation bias under two circumstances; large apriori initial error; and small dimension observation vectors which require a longer arc of measurements to generate observability of the state.

Garrison, James L.↗

Deep Interacting Multiple Model Filtering

In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.

Ghananeel Rotithor↗

DESI-DR1 $3 \times 2$-pt analysis: consistent cosmology across weak lensing surveys

We present a joint cosmological analysis of projected galaxy clustering observations from the Dark Energy Spectroscopic Instrument Data Release 1 (DESI-DR1), and overlapping weak gravitational lensing observations from three datasets: the Kilo-Degree Survey (KiDS-1000), the Dark Energy Survey (DES-Y3), and the Hyper-Suprime-Cam Survey (HSC-Y3). This combination of large-scale structure probes allows us to measure a set of $3 \times 2$-pt correlation functions, breaking the degeneracies between parameters in cosmological fits to individual observables. We obtain mutually-consistent constraints on the parameter $S_8 = σ_8 \sqrt{Ω_{\rm m}/0.3} = 0.786^{+0.022}_{-0.019}$ from the combination of DESI-DR1 and DES-Y3, $S_8 = 0.760^{+0.020}_{-0.018}$ from KiDS-1000, and $S_8 = 0.771^{+0.026}_{-0.027}$ from HSC-Y3. These parameter determinations are consistent with fits to the Planck Cosmic Microwave Background dataset, albeit with $1.5-2σ$ lower values in the $S_8-Ω_{\rm m}$ plane. We perform our analysis with a unified pipeline tailored to the requirements of each cosmic shear survey, which self-consistently determines cosmological and astrophysical parameters. We generate an analytical covariance matrix for the correlation data including all cross-covariances between probes, and we design a new blinding procedure to safeguard our analysis against confirmation bias, whilst leaving goodness-of-fit statistics unchanged. Our study is part of a suite of papers that present joint cosmological analyses of DESI-DR1 and weak gravitational lensing datasets.

Porredon, A. [Madrid, CIEMAT; Edinburgh U., Inst. ↗

Measurement of the 239 Pu$(n, f$) prompt fission neutron spectrum from 10 keV to 10 MeV induced by neutrons of energy 1–20 MeV

Although the prompt fission neutron spectrum (PFNS) is an essential component of neutron-driven systems that has been measured for decades, there are still multiple glaring unknowns regarding the PFNS of major actinides in the fission neutron incident energy range, specifically with regard to multichance fission and pre-equilibrium neutron emission processes. The only impactful experimental 239 Pu PFNS measurements included in recent nuclear data evaluations were measured over a limited outgoing neutron energy range at thermal and 1.5-MeV average incident neutron energy, while other potentially impactful measurements have been shown to contain errors that resulted in either large uncertainty increases or in complete exclusion from nuclear data evaluation. Here, we report a measurement of the 239 Pu PFNS over a wide range of incident neutron energy (1–20 MeV) and three orders of magnitude in outgoing neutron energy (0.01–10 MeV) resulting from the Chi-Nu experiment at the Los Alamos Neutron Science Center. These results are the combination of separate PFNS measurements in the same experimental area, one using a Li-glass and the other a liquid scintillator detector array. Covariances between all PFNS data points from each detector and within each incident energy range were generated between all other data in both detector arrays and within all other incident neutron energy bins, yielding a single covariance matrix for all 1300 PFNS data points reported here. These covariances are based on a thorough assessment of systematic bias and uncertainties associated with the measurement, PFNS extraction technique, combination of data from each detector type, and other aspects of the analysis. The existence of covariances between PFNS data points in different incident neutron energy ranges yielded covariances between average PFNS energy values at each incident energy to be reported here as well, which allowed for firm statements to be made regarding a shape of a purely experimental mean PFNS energy trend for the first time. Although minor PFNS shape differences exist between the results reported here and recent nuclear data evaluations, the ENDF/B-VIII.0 and JEFF-3.3 PFNS evaluations agree reasonably well with the present results from 1-to 10-MeV incident neutron energy, which spans the well-measured 1.5-MeV incident neutron energy PFNS from Lestone and Shores as well as the onset of second-chance fission. However, while the pre-equilibrium component of the PFNS above 12-MeV incident neutron energy roughly agrees in position and magnitude with ENDF/B-VIII.0 and JEFF-3.3, clear differences relating to the relative magnitude of third-chance fission PFNS features are present in the PFNS shape and in the mean PFNS energy trends.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Angular-Rate Estimation Using Delayed Quaternion Measurements

This paper presents algorithms for estimating the angular-rate vector of satellites using quaternion measurements. Two approaches are compared one that uses differentiated quaternion measurements to yield coarse rate measurements, which are then fed into two different estimators. In the other approach the raw quaternion measurements themselves are fed directly into the two estimators. The two estimators rely on the ability to decompose the non-linear part of the rotas rotational dynamics equation of a body into a product of an angular-rate dependent matrix and the angular-rate vector itself. This non unique decomposition, enables the treatment of the nonlinear spacecraft (SC) dynamics model as a linear one and, thus, the application of a PseudoLinear Kalman Filter (PSELIKA). It also enables the application of a special Kalman filter which is based on the use of the solution of the State Dependent Algebraic Riccati Equation (SDARE) in order to compute the gain matrix and thus eliminates the need to compute recursively the filter covariance matrix. The replacement of the rotational dynamics by a simple Markov model is also examined. In this paper special consideration is given to the problem of delayed quaternion measurements. Two solutions to this problem are suggested and tested. Real Rossi X-Ray Timing Explorer (RXTE) data is used to test these algorithms, and results are presented.

Azor, R.↗

Angular-Rate Estimation Using Star Tracker Measurements

This paper presents algorithms for estimating the angular-rate vector of satellites using quaternion measurements. Two approaches are compared, one that uses differentiated quatemion measurements to yield coarse rate measurements which are then fed into two different estimators. In the other approach the raw quatemion measurements themselves are fed directly into the two estimators. The two estimators rely on the ability to decompose the non-linear rate dependent part of the rotational dynamics equation of a rigid body into a product of an angular-rate dependent matrix and the angular-rate vector itself This decomposition, which is not unique, enables the treatment of the nonlinear spacecraft dynamics model as a linear one and, consequently, the application of a Pseudo-Linear Kalman Filter (PSELIKA). It also enables the application of a special Kalman filter which is based on the use of the solution of the State Dependent Algebraic Riccati Equation (SDARE) in order to compute the Kalman gain matrix and thus eliminates the need to propagate and update the filter covariance matrix. The replacement of the elaborate rotational dynamics by a simple first order Markov model is also examined. In this paper a special consideration is given to the problem of delayed quatemion measurements. Two solutions to this problem are suggested and tested. Real Rossi X-Ray Timing Explorer (RXTE) data is used to test these algorithms, and results of these tests are presented.

Azor, R.↗

Angular-Rate Estimation using Star Tracker Measurements

This paper presents algorithms for estimating the angular-rate vector of satellites using quaternion measurements. Two approaches are compared, one that uses differentiated quaternion measurements to yield coarse rate measurements which are then fed into two different estimators. In the other approach the raw quaternion measurements themselves are fed directly into the two estimators. The two estimators rely on the ability to decompose the non-linear rate dependent part of the rotational dynamics equation of a rigid body into a product of an angular-rate dependent matrix and the angular-rate vector itself. This decomposition, which is not unique, enables the treatment of the nonlinear spacecraft dynamics model as a linear one and, consequently, the application of a Pseudo-Linear Kalman Filter (PSELIKA). It also enables the application of a special Kalman filter which is based on the use of the solution of the State Dependent Algebraic Riccati Equation (SDARE) in order to compute the Kalman gain matrix and thus eliminates the need to propagate and update the filter covariance matrix. The replacement of the elaborate rotational dynamics by a simple first order Markov model is also examined. In this paper a special consideration is given to the problem of delayed quaternion measurements. Two solutions to this problem are suggested and tested. Real Rossi X-Ray Timing Explorer (RXTE) data is used to test these algorithms, and results of these tests are presented.

Azor, R.↗

Design of optimal probing signals for vector parameter estimation.

In the design of optimal inputs or probing signals for parameter estimation, it is more natural to consider functions of the Fisher information matrix as the criterion of optimality instead of some function of the error covariance matrix. The input which maximizes the Fisher information measure for efficient estimation of a scalar parameter also provides the minimum error variance. The information is thus a logical choice for the optimality criterion in scalar problems. No such obvious choice is apparent for vector parameter estimation. A number of performance measures are examined and compared in the present study, and a useful criterion is selected. The design of an optimal probing signal using this criterion is shown to be equivalent to an optimal control problem in which certain equality constraints must be satisfied. This problem may be solved by conventional techniques of deterministic or stochastic optimal control.

Nahi, N. E.↗

True covariance simulation of the EUVE update filter

A covariance analysis of the performance and sensitivity of the attitude determination Extended Kalman Filter (EKF) used by the On Board Computer (OBC) of the Extreme Ultra Violet Explorer (EUVE) spacecraft is presented. The linearized dynamics and measurement equations of the error states are derived which constitute the truth model describing the real behavior of the systems involved. The design model used by the OBC EKF is then obtained by reducing the order of the truth model. The covariance matrix of the EKF which uses the reduced order model is not the correct covariance of the EKF estimation error. A true covariance analysis has to be carried out in order to evaluate the correct accuracy of the OBC generated estimates. The results of such analysis are presented which indicate both the performance and the sensitivity of the OBC EKF.

Bar-Itzhack, Itzhack Y.↗