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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.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Digital Beamforming Based RFI Mitigation for Synthetic Aperture Radar

An increasing challenge for P-band Synthetic Aperture Radar (SAR) is Radio Frequency Interference (RFI). RFI results in image distortions and degrades the derived science products. This makes it critical to apply RFI removal techniques to restore the image quality. New advanced techniques can be achieved with Digital Beamforming (DBF) radars such as EcoSAR. In this paper, we present a Range-Dependent Time Minimum Variance Distortionless Response (RDTMVDR) Beamformer and apply it to EcoSAR flight data during post-processing. The antenna pattern (AP) is adaptively changed for each range line which increases the RFI suppression compared to a fixed AP for each pulse. The interferometric image quality is assessed before and after RFI suppression.

Bollian, Tobias↗
NASA NTRS2018

Statistical Study of the Properties of Magnetosheath Lion Roars

Lion roars are narrowband whistler wave emissions that have been observed in several environments, such as planetary magnetosheaths, the Earth's magnetosphere, the solar wind, downstream of interplanetary shocks, and the cusp region. We present measurements of more than 30,000 such emissions observed by the Magnetospheric Multiscale spacecraft with high‐cadence (8,192 samples/s) search coil magnetometer data. A semiautomatic algorithm was used to identify the emissions, and an adaptive interval algorithm in conjunction with minimum variance analysis was used to determine their wave vector. The properties of the waves are determined in both the spacecraft and plasma rest frame. The mean wave normal angle, with respect to the background magnetic field (B(sub 0)), plasma bulk flow velocity (V(sub b)), and the coplanarity plane (V(sub b) × B(sub 0)) are 23°, 56°, and 0°, respectively. The average peak frequencies were ∼31% of the electron gyrofrequency (ω(sub ce)) observed in the spacecraft frame and ∼18% of ω(sub ce) in the plasma rest frame. In the spacecraft frame, ∼99% of the emissions had a frequency <ω(sub ce), while 98% had a peak frequency <0.72 ω(sub ce) in the plasma rest frame. None of the waves had frequencies lower than the lower hybrid frequency, ω. From the probability density function of the electron plasma β(sub e), the ratio between the electron thermal and magnetic pressure, ∼99.6% of the waves were observed with β(sub e)<4 with a large narrow peak at 0.07 and two smaller, but wider, peaks at 1.26 and 2.28, while the average value was ∼1.25.

Magnetosheath emissions↗
NASA NTRS2019

Adaptive Antenna Pattern Notching of Interference in Synthetic Aperture Radar Data Using Digital Beamforming

Radio Frequency Interference (RFI) is a growing problem in Synthetic Aperture Radar (SAR)systems as scientific motivations push the radars to lower frequencies and as more wireless services share the frequency spectrum. New, advanced SAR instruments, such as NASA's EcoSAR, DBSAR and DLR's Tandem-L mission, employ a multichannel architecture capable of Digital Beamforming (DBF). Radars with DBF are capable of notching the antenna pattern in specific directions, which can be utilized to suppress RFI on board or in post-processing. A well-researched beamformer for this purpose is the Minimum Variance Distortionless Response (MVDR) Beamformer. However, the number of interferers that can be removed through notching is limited by the number of receive channels. It is therefore essential to adaptively change the antenna pattern notching throughout the image in time and frequency for the best results with a given number of receive channels. In this paper, we present several methods to achieve this notching by making use of the spatial SAR signal distribution in range time, range frequency, azimuth time and azimuth Doppler that is inherent to the SAR imaging geometry. Because this distribution is time-variable and yet predictable, it can be used to improve the angle of arrival estimation of the RFI and the adaptive notching. The presented methods can be applied to a Digital Beamforming (DBF) SAR signal with multiple channels in elevation and/or in azimuth. Simulations show that the proposed methods increase the ability to notch out-of-swath interference from multiple directions and lessen the impact on in-swath interference. The improvement of the interferometric coherence for a single-pass interferogram acquired by NASA's EcoSAR system (capable of DBF in elevation) is demonstrated. The removal of periodic RFI artifacts is achieved.

RFI↗
NASA NTRSDate not supplied

Sequential Filtering in the Presence of Uniform Measurement Errors

This paper presents a sequential filtering strategy using observations corrupted with uniform measurement noise. While the Kalman filter remains the best linear estimator of the state, other filtering techniques provide minimum variance optimal estimates, a trait only enjoyed by the Kalman filter when the underlying noises are, in fact, Gaussian. This work develops a new approximate optimal estimator for uniform measurement noises. The resulting recursion requires just slightly more computational time to complete a measurement update than the Kalman filter, which generally cannot be claimed by other optimal strategies such as the particle or Gaussian mixture filters.

James S. McCabe↗
NASA NTRSDate not supplied

Sequential Filtering in the Presence of Uniform Measurement Errors

This paper presents a sequential filtering strategy using observations corrupted with uniform measurement noise. While the Kalman filter remains the best linear estimator of the state, other filtering techniques provide minimum variance optimal estimates, a trait only enjoyed by the Kalman filter when the underlying noises are, in fact, Gaussian. This work develops a new approximate optimal estimator for uniform measurement noises. The resulting recursion requires just slightly more computational time to complete a measurement update than the Kalman filter, which generally cannot be claimed by other optimal strategies such as the particle or Gaussian mixture filters.

James S McCabe↗
NASA NTRS2023

Tensor-to-scalar ratio forecasts for extended LiteBIRD frequency configurations

LiteBIRD is a planned JAXA-led cosmic microwave background (CMB) B -mode satellite experiment aiming for launch in the late 2020s, with a primary goal of detecting the imprint of primordial inflationary gravitational waves. Its current baseline focal-plane configuration includes 15 frequency bands between 40 and 402 GHz, fulfilling the mission requirements to detect the amplitude of gravitational waves with the total uncertainty on the tensor-to-scalar ratio, δr , down to δr < 0.001. A key aspect of this performance is accurate astrophysical component separation, and the ability to remove polarized thermal dust emission is particularly important. In this paper we note that the CMB frequency spectrum falls off nearly exponentially above 300 GHz relative to the thermal dust spectral energy distribution, and a relatively minor high frequency extension can therefore result in even lower uncertainties and better model reconstructions. Specifically, we compared the baseline design with five extended configurations, while varying the underlying dust modeling, in each of which the High-Frequency Telescope (HFT) frequency range was shifted logarithmically toward higher frequencies, with an upper cutoff ranging between 400 and 600 GHz. In each case, we measured the tensor-to-scalar ratio r uncertainty and bias using both parametric and minimum-variance component-separation algorithms. When the thermal dust sky model includes a spatially varying spectral index and temperature, we find that the statistical uncertainty on r after foreground cleaning may be reduced by as much as 30–50% by extending the upper limit of the frequency range from 400 to 600 GHz, with most of the improvement already gained at 500 GHz. We also note that a broader frequency range leads to higher residuals when fitting an incorrect dust model, but also it is easier to discriminate between models through higher χ 2 sensitivity. Even in the case in which the fitting procedure does not correspond to the underlying dust model in the sky, and when the highest frequency data cannot be modeled with sufficient fidelity and must be excluded from the analysis, the uncertainty on r increases by only about 5% for a 500 GHz configuration compared to the baseline.

U. Fuskeland↗
NASA NTRSDate not supplied

A Comparison of Control Allocation Methods in the Presence of Parametric Model Uncertainty

When allocating redundant effectors to virtual control commands, linear (generalized inverse) allocators have historically been used on aircraft and spacecraft. While simple to implement, generalized inverses are unable to realize a significant portion of the attainable moments. To address this drawback, the control allocation problem can also be formulated as a linear programming or quadratic programming problem and solved using convex optimization based solvers. These approaches have been shown to access a larger set of attainable moments, however, little work has been done to understand the performance of convex optimization-based control allocation in the presence of parametric model uncertainty. This paper seeks to compare the performance of several control allocation approaches, including two forms of generalized inverse allocators, the pseudo inverse and minimum-variance, and the linear programming and quadratic programming approach in the presence of parametric model uncertainty. The performance of these four allocators were tested on an aircraft model in the presence of realistic parametric model uncertainty and the convex optimization approaches were shown to outperform the generalized inverses.

Luke J Miller↗
DOE OSTI2026

SPT-3G D1: Compton-$y$ maps using data from the SPT-3G and Planck surveys

We present thermal Sunyaev-Zel'dovich (tSZ) Compton-$y$ parameter maps constructed from two years (2019-2020) of observations with the South Pole Telescope (SPT) third-generation camera, SPT-3G, combined with data from the Planck satellite. Using a linear combination (LC) pipeline, we obtain a suite of reconstructions that explore different trade-offs between statistical sensitivity and suppression of astrophysical contaminants, including minimum-variance, CMB-deprojected, and CIB-deprojected $y$-maps. We validate these maps through different statistical techniques such as auto- and cross-power spectra with large-scale structure tracers as well as stacking on cluster locations. These tests are used to understand the balance between noise and astrophysical foreground residuals (such as the CIB) in combination with the recovery of the tSZ signal for different maps. For example, results from stacking at the location of clusters confirm the robustness of the recovered tSZ signal over the $\sim 1500\: {\rm deg}^2$ SPT-3G survey field used in this analysis. The high-resolution and low-noise maps produced here provide an important cosmological tool for future studies, including measurements of the Compton-$y$ map power spectrum, cross-correlations with other tracers of the large-scale structure, detailed modeling of cluster pressure profiles, and study of the thermodynamic state of the baryons in the Universe.

Maniyar, A. S. [Harvard-Smithsonian Ctr. Astrophys↗
DOE OSTI2026

SPT-3G D1: Foreground-Robust Lensing Templates for Primordial Gravitational Wave Searches

Gravitational lensing of the cosmic microwave background (CMB) generates B-mode polarization that acts as a source of contamination to searches for B modes generated by primordial gravitational waves (PGWs). The strongest constraint on PGW B modes is already significantly limited by lensing B modes, as shown in the most recent BICEP result. In this work, we present CMB lensing B-mode templates constructed using SPT-3G and Planck data, which characterize the lensing B modes and can be used to improve PGW B-mode searches. We use SPT-3G data from the 2019 and 2020 observing seasons for the E modes and the CMB-reconstructed lensing potential, and a cosmic infrared background (CIB) map from Planck as an external lensing tracer. To test for extragalactic foreground biases in the lensing template, we consider CMB lensing reconstruction variants with different levels of foreground immunity: the standard and profile-hardened global minimum variance (GMV) quadratic estimators, and a polarization-only quadratic estimator. We validate the template construction using Gaussian simulations and Agora simulations with realistic non-Gaussian foregrounds. From simulations, we find that foreground-induced biases are strongly suppressed for the template constructed with the profile-hardened GMV + CIB tracer, with residual bias below 10% of the statistical uncertainty on the template power spectrum. Data difference tests on this template similarly show no evidence for significant foreground contamination. This foreground-immune lensing template achieves delensed residual BB power of $A_{\rm lens}^{\rm res} \simeq 0.48$ averaged over $20 \leq \ell \leq 200$, the highest delensing efficiency lensing template to date. These results demonstrate and validate a method to construct foreground-robust lensing templates which will be used in upcoming delensed PGW B-mode analyses of BICEP data.

Nakato, Y. [Stanford U.]↗
NASA NTRS1963

Executive routine

Utilization of history tape and tape library procedures of alternate executive routine for minivar computer program

MAGNETIC TAPE↗
NASA NTRS1974

Optimal design of an unsupervised adaptive classifier with unknown priors

An adaptive detection scheme for M hypotheses was analyzed. It was assumed that the probability density function under each hypothesis was known, and that the prior probabilities of the M hypotheses were unknown and sequentially estimated. Each observation vector was classified using the current estimate of the prior probabilities. Using a set of nonlinear transformations, and applying stochastic approximation theory, an optimally converging adaptive detection and estimation scheme was designed. The optimality of the scheme lies in the fact that convergence to the true prior probabilities is ensured, and that the asymptotic error variance is minimum, for the class of nonlinear transformations considered. An expression for the asymptotic mean square error variance of the scheme was also obtained.

Kazakos, D.↗