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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 271 records · Page 15
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NASA NTRS2021

Adaptive-Sweep Algorithm for Spacecraft Carrier Acquisition and Tracking: System Analysis and Implementation

This paper presents an adaptive-sweep algorithm that can be used in conjunction with a Phased-Lock Loop to acquire and track the carrier of a Binary Phase-shift Keying signal with residual carrier. This algorithm is useful in proximity links where no Doppler pre-compensation is available at the transmitter, and allows the receiver to acquire the incoming carrier even if the experienced Doppler shift is several orders of magnitude larger than the PLL bandwidth. Other applications for this algorithm include Multiple Uplink per Antenna at the Deep Space Network, and generation of observables (Doppler shift, Doppler rate, range rate) for in-situ navigation purposes. This paper enhances a previously presented adaptive-sweep algorithm and its performance analysis in two fronts. First, the algorithm now uses the direction function to automatically estimate the required frequency jump to achieve PLL lock. This enables a fully adaptive-sweep scheme in which system implementer does not need to manually select thresholds. On the other hand, we study the system performance in the presence of Additive White Gaussian Noise and derive necessary conditions for the system to operate at low signal-to-noise conditions. This allows us to create design rules that specify all parameters of the algorithm given a set of system-level requirements. To test the performance of the proposed algorithm, we implement it in GNU Radio, an open-source Software-Defined Radio that interfaces with several commercial radio peripherals. We use this implementation to demonstrate carrier lock and quantify system performance under different noise conditions. We also test the algorithm using open-loop recordings of two downlinks between the Lunar Reconnaissance Orbiter and the Deep Space Network. We show that even after large frequency jumps (tens of kHz), the proposed system is able to rapidly reacquire the carrier and continue operation.

Divsalar, Dariush↗
NASA NTRS2021

LiDAR-Based Map Relative Localization Performance Analysis for Landing on Europa

This paper presents preliminary simulations andanalyses done to assess the feasibility of performing Map RelativeLocalization (MRL) with the Europa Lander LiDAR beingdeveloped for the Europa Lander Pre-Phase A concept. MapRelative Localization is the process of determining the horizontalposition of a lander with respect to an onboard, a-priori map,by comparing the map to sensor observations of the terrain duringdeorbit, descent, and landing (DDL). Although kilometerscaleposition knowledge is commonly available during DDL,landing in hazard-rich environments requires position errors of100 m or less. Prior knowledge in the case of Europa Landerwill be visual and topographic maps collected by the upcomingEuropa Clipper mission. The Mars 2020 Lander Vision System(LVS) uses images from a camera to localize with respect tovisual maps. This technology, as well as a 3D imaging LiDAR indevelopment for hazard detection, is currently baselined for theEuropa Lander Pre-Phase A concept. This paper investigatesthe potential use of the hazard detection LiDAR to performMRL with respect to a 3D digital elevation model (DEM)provided by the Europa Clipper mission, as an alternative orbackup solution to passive optical MRL. Compared to passiveoptical MRL, one advantage of LiDAR-based localization isthat it is insensitive to lighting conditions, potentially relaxingrequirements on synchronizing map acquisition and landingtime of day. To analyze LiDAR based MRL performance,six representative terrains are synthetically up-sampled fromGalileo-derived maps of Europa to a resolution of 0.5 m/pxand covering an area of 4 km by 4 km. These maps are usedas ground-truth to generate simulated noisy a-priori onboardtopographic maps expected from Europa Clipper as well assimulated LiDAR DEMs generated at an altitude of 5 km duringEuropa Lander DDL. The simulated LiDAR DEM is matchedagainst the simulated map via 2D normalized cross-correlation,exploiting the accurately known spacecraft attitude to avoidthe need for more computationally intensive algorithms such asIterative Closest Point (ICP). Two sources of measurement errorare identified for analysis: 1) additive Gaussian noise in therange measurements from the Europa Lander LiDAR and theEuropa Clipper derived maps and 2) errors in the LiDAR DEMinduced by errors in the Europa Lander state estimate which isused to de-warp the LiDAR scan data into a DEM format. Weassess the effect of each of these types of errors independently onmatching performance as well as the overall performance whenall types of error are introduced. Additionally, we present theresult of a sensitivity study to terrain frequency content.

Trawny, Nikolas↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
NASA NTRS1969

Automatic Gaussian random-noise limiter

Circuit limits the positive and negative peaks of a random-noise signal. It accurately establishes a known limiting level relative to any given RMS value of the random-noise signal input.

Woodbury, R. C.↗
NASA NTRS1972

Gaussian and click noise in an FM/FM multiplex receiver.

An FM/FM system is one in which the signal information frequency modulates a subcarrier, which, in turn, frequency modulates a main carrier for transmission. The detection process then involves two frequency demodulators in sequence. Using Rice's click model for both FM demodulators, we obtain the average click rate and an approximate output spectrum that include the effect of the impulses generated by the carrier demodulator on the subcarrier demodulation at and below threshold. We then focus on the threshold behavior of these systems and the proportioning of their modulation parameters so as to provide certain desired characteristics. We find that due to the filtering action of the second demodulator, the number of output clicks from carrier generated spikes can be orders of magnitude smaller, yet their contribution to the output noise can be much greater than that produced by the first demodulation.

Stochel, N. H.↗
NASA NTRS1974

MIA computer simulation test results report

Results of the first noise susceptibility computer simulation tests of the complete MIA receiver analytical model are presented. Computer simulation tests were conducted with both Gaussian and pulse noise inputs. The results of the Gaussian noise tests were compared to results predicted previously and were found to be in substantial agreement. The results of the pulse noise tests will be compared to the results of planned analogous tests in the Data Bus Evaluation Laboratory at a later time. The MIA computer model is considered to be fully operational at this time.

Unger, G. E.↗
NASA NTRS1967

Adaptive filtering Interim report

Adaptive filter with inaccuracies approximated by Gaussian white noise input and determination of covariance for most probable residual sequence

GAUSSIAN NOISE↗
NASA NTRS1987

Optimal application of Morrison's iterative noise removal for deconvolution. Appendices

Morrison's iterative method of noise removal, or Morrison's smoothing, is applied in a simulation to noise-added data sets of various noise levels to determine its optimum use. Morrison's smoothing is applied for noise removal alone, and for noise removal prior to deconvolution. For the latter, an accurate method is analyzed to provide confidence in the optimization. The method consists of convolving the data with an inverse filter calculated by taking the inverse discrete Fourier transform of the reciprocal of the transform of the response of the system. Various length filters are calculated for the narrow and wide Gaussian response functions used. Deconvolution of non-noisy data is performed, and the error in each deconvolution calculated. Plots are produced of error versus filter length; and from these plots the most accurate length filters determined. The statistical methodologies employed in the optimizations of Morrison's method are similar. A typical peak-type input is selected and convolved with the two response functions to produce the data sets to be analyzed. Both constant and ordinate-dependent Gaussian distributed noise is added to the data, where the noise levels of the data are characterized by their signal-to-noise ratios. The error measures employed in the optimizations are the L1 and L2 norms. Results of the optimizations for both Gaussians, both noise types, and both norms include figures of optimum iteration number and error improvement versus signal-to-noise ratio, and tables of results. The statistical variation of all quantities considered is also given.

Ioup, George E.↗
NASA NTRS1971

Self-synchronizing, bi-orthogonal coded PCM telemetry system

Communications and data handling system improves signal to noise ratio when transmission channel is perturbed by noise. Telemetry system consists of airborne source, Gaussian additive noise channel, and ground receiver unit. Advantages of system are given.

Miller, W.↗
NASA NTRS1972

Input noise approximation in tracker modeling

The validity of approximating random Gaussian distributed inputs used in human response modeling by sums of discrete sine waves is studied. An ideal rectangular power density spectrum is simulated using both filtered Gaussian white noise and sums-of-discrete sine waves with three different input cutoff frequencies in the same compensatory tracking task. Resulting normalized tracking error and quality operator observations are used to investigate apparent discrepancies in human operator characteristics. Results show that discrete and continuous input tracking data compare favorable when the power in the crossover region is taken into account.

Torrey, P. F.↗
NASA NTRS2017

A Comparison of Three Random Number Generators for Aircraft Dynamic Modeling Applications

Three random number generators, which produce Gaussian white noise sequences, were compared to assess their suitability in aircraft dynamic modeling applications. The first generator considered was the MATLAB (registered) implementation of the Mersenne-Twister algorithm. The second generator was a website called Random.org, which processes atmospheric noise measured using radios to create the random numbers. The third generator was based on synthesis of the Fourier series, where the random number sequences are constructed from prescribed amplitude and phase spectra. A total of 200 sequences, each having 601 random numbers, for each generator were collected and analyzed in terms of the mean, variance, normality, autocorrelation, and power spectral density. These sequences were then applied to two problems in aircraft dynamic modeling, namely estimating stability and control derivatives from simulated onboard sensor data, and simulating flight in atmospheric turbulence. In general, each random number generator had good performance and is well-suited for aircraft dynamic modeling applications. Specific strengths and weaknesses of each generator are discussed. For Monte Carlo simulation, the Fourier synthesis method is recommended because it most accurately and consistently approximated Gaussian white noise and can be implemented with reasonable computational effort.

Grauer, Jared A.↗