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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Anomaly Identification of Synchronized Voltage Waveform for Situational Awareness of Low Inertia Systems

Inverter-based resources (IBRs) such as photovoltaics (PVs), wind turbines, and battery energy storage systems (BESSs) are widely deployed in low-carbon power systems. However, these resources typically do not provide the inertia needed for grid stability, resulting in a low-inertia power system. IBRs and lack of inertia have been known to cause anomalies such as waveform distortions and wideband oscillations in power systems due to the limited inertia level, leading to increased generation trips and load shedding. Here, to achieve effective anomaly identification, this paper proposes a synchro-waveform-based algorithm utilizing real-time synchronized voltage waveform measurements from waveform measurement units (WMUs). In the proposed method, different physical characteristics, as well as statistical features, are extracted from synchronized voltage waveform measurements to filter anomalies. Then, the anomaly identification approach based on the random forest is developed and deployed into the FNET/GridEye system considering trade-offs among accuracy, computational burden, and deployment cost. Moreover, four WMUs are specially designed and deployed on Kauai Island to receive instantaneous synchronized voltage waveform measurements. To verify the performance of the proposed algorithm, different experiments are carried out with collected field test data. The result demonstrates that the performance of the proposed synchro-waveform-based anomaly categorization algorithm can accurately identify anomalies 95.35% of the time, which has comparable performance among benchmarking algorithms.

Situational awareness↗
DOE OSTI2025

AK112: Full Waveform Inversion Tomography of Alaska Improves Waveform Fits While Imaging Crustal, Mantle, and Slab Structure

We report a full waveform inversion tomography model of Alaska and the surrounding regions, inferring radially anisotropic shear and isotropic compressional wavespeeds by fitting complete waveforms from 120 regional earthquakes. Our multiscale approach inverted time–frequency phase misfits (maximum period of 100 s), starting with a minimum period of 40 s and ending at 20 s in 7 stages and 112 total iterations. The model (AK112) was evaluated by computing the misfits for 36 independent validation events. We find that misfit reductions were large and equal (∼55%) for both the inversion and validation data sets, providing confidence in the model. AK112 also provides much better waveform fits compared to other reported models for the region, including an isotropic version of itself, highlighting the importance of anisotropy. The model resolves known crustal, upper mantle, and slab structure to depths of 100 km with new detail: sedimentary basins in the Alaskan Shelf, Cook Inlet, and Colville basins, among others; discontinuous lithospheric structure across major terrane boundaries; and subducting slab geometry and back‐arc volcanic sources. In addition to tectonic interpretations, the model enables full waveform simulations for long‐period earthquake ground motions and source characterization (e.g., moment tensor and finite‐fault inversion).

Rodgers, Arthur [Lawrence Livermore National Labor↗
DOE OSTI2024

WUS324: Multiscale Full Waveform Inversion Approaching Convergence Improves Waveform Fits While Imaging Seismic Structure of the Western United States

Abstract We report a new model of radially anisotropic crustal and upper mantle structure of the western United States (WUS324) obtained from full waveform inversion of earthquake data. We ran three multiscale inversion stages beyond model WUS256 (Rodgers et al., 2022, https://doi.org/10.1029/2022jb024549 ) allowing them to approach convergence to fit a larger data set to a shorter minimum period of 16 s. WUS324 is based on 324 total iterations from its starting model, significantly more (16 times) than previous studies. Waveform misfit reductions are 66%–70% for both the inversion data and an independent validation data set providing confidence in the predictive power of the model. WUS324 provides much better fits and reveals shear wavespeed, v S , structure of this large region with more detail than previous waveform tomography models. We show representative images demonstrating the resolution of diverse seismic structure across this highly heterogeneous region including oceanic lithosphere, subducting slabs and continental magmatism.

58 GEOSCIENCES↗
NASA NTRS2013

A Comparison of Foliage Profiles in the Sierra National Forest Obtained with a Full-Waveform Under-Canopy EVI Lidar System with the Foliage Profiles Obtained with an Airborne Full-Waveform LVIS Lidar System

Foliage profiles retrieved froma scanning, terrestrial, near-infrared (1064 nm), full-waveformlidar, the Echidna Validation Instrument (EVI), agree well with those obtained from an airborne, near-infrared, full-waveform, large footprint lidar, the Lidar Vegetation Imaging Sensor (LVIS). We conducted trials at 5 plots within a conifer stand at Sierra National Forest in August, 2008. Foliage profiles retrieved from these two lidar systems are closely correlated (e.g., r = 0.987 at 100 mhorizontal distances) at large spatial coverage while they differ significantly at small spatial coverage, indicating the apparent scanning perspective effect on foliage profile retrievals. Alsowe noted the obvious effects of local topography on foliage profile retrievals, particularly on the topmost height retrievals. With a fine spatial resolution and a small beam size, terrestrial lidar systems complement the strengths of the airborne lidars by making a detailed characterization of the crowns from a small field site, and thereby serving as a validation tool and providing localized tuning information for future airborne and spaceborne lidar missions.

Full-waveform↗
DOE OSTI2026

Technical Report on Adjoint Waveform Tomography of East Asia for Improved Waveform Prediction

We present a preliminary version of the East Asia Tomography (EAT) model, an adjoint waveform tomography model of East and Southeast Asia. We used SPiRaL (Simmons et al., 2021) as our starting model and source parameters for 250 earthquakes from the Global Centroid Moment Tensor catalogue (Ekström et al., 2012). Over 198 iterations, we have iterated the EAT model down to a minimum period of 35 seconds. We plan on continuing our iteration technique down to 30 seconds period before updating our misfit function to use a normalized cross correlation-based misfit functions (e.g., Tao et al., 2018) to better constrain Earth structure. We hope to iterate the current extent of the model to 25 seconds minimum period before iterating to shorter periods for a smaller subregion of the full model.

58 GEOSCIENCES↗
DOE OSTI2026

Full Waveform Inversion Tomography for Improved Seismic Waveform Simulations and Lithospheric Structure of Northwest China and Surrounding Regions

We report a new model of three-dimensional (3D) anisotropic seismic wavespeed structure of the lithospheric of northwestern China, western Mongolia and east }|ern Kazakhstan from full waveform inversion tomography using openly available broadband data. To investigate the efficacy of a conservative multiscale inversion methodology, we used a simple isotropic starting model.

58 GEOSCIENCES↗
NASA NTRS2012

Goldstone Solar System Radar Waveform Generator

Due to distances and relative motions among the transmitter, target object, and receiver, the time-base between any transmitted and received signal will undergo distortion. Pre-distortion of the transmitted signal to compensate for this time-base distortion allows reception of an undistorted signal. In most radar applications, an arbitrary waveform generator (AWG) would be used to store the pre-calculated waveform and then play back this waveform during transmission. The Goldstone Solar System Radar (GSSR), however, has transmission durations that exceed the available memory storage of such a device. A waveform generator capable of real-time pre-distortion of a radar waveform to a given time-base distortion function is needed. To pre-distort the transmitted signal, both the baseband radar waveform and the RF carrier must be modified. In the GSSR, this occurs at the up-conversion mixing stage to an intermediate frequency (IF). A programmable oscillator (PO) is used to generate the IF along with a time-varying phase component that matches the time-base distortion of the RF carrier. This serves as the IF input to the waveform generator where it is mixed with a baseband radar waveform whose time-base has been distorted to match the given time-base distortion function producing the modulated IF output. An error control feedback loop is used to precisely control the time-base distortion of the baseband waveform, allowing its real-time generation. The waveform generator produces IF modulated radar waveforms whose time-base has been pre-distorted to match a given arbitrary function. The following waveforms are supported: continuous wave (CW), frequency hopped (FH), binary phase code (BPC), and linear frequency modulation (LFM). The waveform generator takes as input an IF with a time varying phase component that matches the time-base distortion of the carrier. The waveform generator supports interconnection with deep-space network (DSN) timing and frequency standards, and is controlled through a 1 Gb/s Ethernet UDP/IP interface. This real-time generation of a timebase distorted radar waveform for continuous transmission in a planetary radar is a unique capability.

Quirk, Kevin J.↗
NASA NTRS2012

An MSK Radar Waveform

The minimum-shift-keying (MSK) radar waveform is formed by periodically extending a waveform that separately modulates the in-phase and quadrature- phase components of the carrier with offset pulse-shaped pseudo noise (PN) sequences. To generate this waveform, a pair of periodic PN sequences is each passed through a pulse-shaping filter with a half sinusoid impulse response. These shaped PN waveforms are then offset by half a chip time and are separately modulated on the in-phase and quadrature phase components of an RF carrier. This new radar waveform allows an increase in radar resolution without the need for additional spectrum. In addition, it provides self-interference suppression and configurable peak sidelobes. Compared strictly on the basis of the expressions for delay resolution, main-lobe bandwidth, effective Doppler bandwidth, and peak ambiguity sidelobe, it appears that bi-phase coded (BPC) outperforms the new MSK waveform. However, a radar waveform must meet certain constraints imposed by the transmission and reception of the modulation, as well as criteria dictated by the observation. In particular, the phase discontinuity of the BPC waveform presents a significant impediment to the achievement of finer resolutions in radar measurements a limitation that is overcome by using the continuous phase MSK waveform. The phase continuity, and the lower fractional out-of-band power of MSK, increases the allowable bandwidth compared with BPC, resulting in a factor of two increase in the range resolution of the radar. The MSK waveform also has been demonstrated to have an ambiguity sidelobe structure very similar to BPC, where the sidelobe levels can be decreased by increasing the length of the m-sequence used in its generation. This ability to set the peak sidelobe level is advantageous as it allows the system to be configured to a variety of targets, including those with a larger dynamic range. Other conventionally used waveforms that possess an even greater spectral efficiency than the MSK waveform, such as linear frequency modulation (LFM) and Costas frequency hopping, have a fixed peak sidelobe level that is therefore not configurable, and can be exceeded by high contrast targets. Furthermore, in the case of a multistatic experiment observing a target in motion, self-interference from the transmitter to the receiver is mitigated by the MSK waveform. Waveforms that have delay Doppler coupling, such as LFM, provide no such protection.

Quirk, Kevin J.↗
DOE OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS2021

Challenges to Aboveground Biomass Prediction from Waveform Lidar

Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD—that forest stands with different AGBD may produce highly similar waveforms—and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non-uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7–411 Mg ha−1, average of 167 Mg ha−1). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.

GEDI↗
NASA NTRS2008

High-Voltage, Asymmetric-Waveform Generator

The shapes of waveforms generated by commercially available analytical separation devices, such as some types of mass spectrometers and differential mobility spectrometers are, in general, inadequate and result in resolution degradation in output spectra. A waveform generator was designed that would be able to circumvent these shortcomings. It is capable of generating an asymmetric waveform, having a peak amplitude as large as 2 kV and frequency of several megahertz, which can be applied to a capacitive load. In the original intended application, the capacitive load would consist of the drift plates in a differential-mobility spectrometer. The main advantage to be gained by developing the proposed generator is that the shape of the waveform is made nearly optimum for various analytical devices requiring asymmetric-waveform such as differential-mobility spectrometers. In addition, this waveform generator could easily be adjusted to modify the waveform in accordance with changed operational requirements for differential-mobility spectrometers. The capacitive nature of the load is an important consideration in the design of the proposed waveform generator. For example, the design provision for shaping the output waveform is based partly on the principle that (1) the potential (V) on a capacitor is given by V=q/C, where C is the capacitance and q is the charge stored in the capacitor; and, hence (2) the rate of increase or decrease of the potential is similarly proportional to the charging or discharging current. The proposed waveform generator would comprise four functional blocks: a sine-wave generator, a buffer, a voltage shifter, and a high-voltage switch (see Figure 1). The sine-wave generator would include a pair of operational amplifiers in a feedback configuration, the parameters of which would be chosen to obtain a sinusoidal timing signal of the desired frequency. The buffer would introduce a slight delay (approximately equal to 20 ns) but would otherwise leave the fundamental timing signal unchanged. The buffered timing signal would be fed as input to the level shifter. The output of the level shifter would serve as a timing and control signal for the high-voltage switch, causing the switch to alternately be (1) opened, allowing the capacitive load to be charged from a high-voltage DC power supply; then (2) closed to discharge the capacitive load to ground. Hence, the output waveform would closely approximate a series of exponential charging and discharging curves (see Figure 2).

Beegle, Luther W.↗
NASA NTRS1995

Lightning-channel morphology by return-stroke radiation field waveforms

Simultaneous video and wideband electric field recordings of 32 cloud-to-ground lightning flashes in Florida were analyzed to show the formation of new channels to ground can be detected by examination of the return-stroke radiation fields alone. The return-stroke E and dE/dt waveforms were subjectively classified according to their fine structure. Then the video images were examined field by field to identify each waveform with a visible channel to ground. Fifty-five correlated waveforms and channel images were obtained. Of these, all 34 first-stroke waveforms (multiple jagged E peaks, noisy dE/dt), 8 of which were not radiated by the chronologically first stroke in the flash, came from new channels to ground (not previously seen on video). All 18 subsequent-stroke waveforms (smoothly rounded E and quiet dE/dt after initial peak) were radiated by old channels (illuminated by a previous stroke). Two double-ground waveforms (two distinct first-return-stroke pulses separated by tens of microseconds or less) coincided with video fields showing two new channels. One `anomalous-stroke' waveform (beginning like a first stroke and ending like a subsequent) was produced by a new channel segment to ground branching off an old channel. This waveform classification depends on the presence or absence of high-frequency fine structure. Fourier analysis shows that first-stroke waveforms contain about 18 dB more spectral power in the frequency interval from 500 kHz to at least 7 MHz than subsequent-stroke waveforms for at least 13 microseconds after the main peak.

Willett, J. C.↗
NASA NTRS2008

Platform for Postprocessing Waveform-Based NDE

Taking advantage of the similarities that exist among all waveform-based non-destructive evaluation (NDE) methods, a common software platform has been developed containing multiple- signal and image-processing techniques for waveforms and images. The NASA NDE Signal and Image Processing software has been developed using the latest versions of LabVIEW, and its associated Advanced Signal Processing and Vision Toolkits. The software is useable on a PC with Windows XP and Windows Vista. The software has been designed with a commercial grade interface in which two main windows, Waveform Window and Image Window, are displayed if the user chooses a waveform file to display. Within these two main windows, most actions are chosen through logically conceived run-time menus. The Waveform Window has plots for both the raw time-domain waves and their frequency- domain transformations (fast Fourier transform and power spectral density). The Image Window shows the C-scan image formed from information of the time-domain waveform (such as peak amplitude) or its frequency-domain transformation at each scan location. The user also has the ability to open an image, or series of images, or a simple set of X-Y paired data set in text format. Each of the Waveform and Image Windows contains menus from which to perform many user actions. An option exists to use raw waves obtained directly from scan, or waves after deconvolution if system wave response is provided. Two types of deconvolution, time-based subtraction or inverse-filter, can be performed to arrive at a deconvolved wave set. Additionally, the menu on the Waveform Window allows preprocessing of waveforms prior to image formation, scaling and display of waveforms, formation of different types of images (including non-standard types such as velocity), gating of portions of waves prior to image formation, and several other miscellaneous and specialized operations. The menu available on the Image Window allows many further image processing and analysis operations, some of which are found in commercially-available image-processing software programs (such as Adobe Photoshop), and some that are not (removing outliers, Bscan information, region-of-interest analysis, line profiles, and precision feature measurements).

Roth, Don↗
NASA NTRS1981

Radar altimeter waveform modeled parameter recovery

Satellite-borne radar altimeters include waveform sampling gates providing point samples of the transmitted radar pulse after its scattering from the ocean's surface. Averages of the waveform sampler data can be fitted by varying parameters in a model mean return waveform. The theoretical waveform model used is described as well as a general iterative nonlinear least squares procedures used to obtain estimates of parameters characterizing the modeled waveform for SEASAT-1 data. The six waveform parameters recovered by the fitting procedure are: (1) amplitude; (2) time origin, or track point; (3) ocean surface rms roughness; (4) noise baseline; (5) ocean surface skewness; and (6) altitude or off-nadir angle. Additional practical processing considerations are addressed and FORTRAN source listing for subroutines used in the waveform fitting are included. While the description is for the Seasat-1 altimeter waveform data analysis, the work can easily be generalized and extended to other radar altimeter systems.

Source record↗
NASA NTRS2007

JTRS/SCA and Custom/SDR Waveform Comparison

This paper compares two waveform implementations generating the same RF signal using the same SDR development system. Both waveforms implement a satellite modem using QPSK modulation at 1M BPS data rates with one half rate convolutional encoding. Both waveforms are partitioned the same across the general purpose processor (GPP) and the field programmable gate array (FPGA). Both waveforms implement the same equivalent set of radio functions on the GPP and FPGA. The GPP implements the majority of the radio functions and the FPGA implements the final digital RF modulator stage. One waveform is implemented directly on the SDR development system and the second waveform is implemented using the JTRS/SCA model. This paper contrasts the amount of resources to implement both waveforms and demonstrates the importance of waveform partitioning across the SDR development system.

Oldham, Daniel R.↗
NASA NTRS2008

STRS Compliant FPGA Waveform Development

The Space Telecommunications Radio System (STRS) Architecture Standard describes a standard for NASA space software defined radios (SDRs). It provides a common framework that can be used to develop and operate a space SDR in a reconfigurable and reprogrammable manner. One goal of the STRS Architecture is to promote waveform reuse among multiple software defined radios. Many space domain waveforms are designed to run in the special signal processing (SSP) hardware. However, the STRS Architecture is currently incomplete in defining a standard for designing waveforms in the SSP hardware. Therefore, the STRS Architecture needs to be extended to encompass waveform development in the SSP hardware. The extension of STRS to the SSP hardware will promote easier waveform reconfiguration and reuse. A transmit waveform for space applications was developed to determine ways to extend the STRS Architecture to a field programmable gate array (FPGA). These extensions include a standard hardware abstraction layer for FPGAs and a standard interface between waveform functions running inside a FPGA. A FPGA-based transmit waveform implementation of the proposed standard interfaces on a laboratory breadboard SDR will be discussed.

Nappier, Jennifer↗
NASA NTRS1991

Waveform-Generating Program

GENERATE (Digital Generation of Complex Waveforms) computer program is flexible and reliable software tool for advanced generation of waveforms. User employs standard arithmetic operator (+, -, x, /, and parentheses) to simulate any combination of eight commonly used basic waveforms at desired amplitude and frequency. Creates data file based on user's selections from menu. Provides necessary equations describing eight basic waveforms and enables user to combine them arithmetically. Basic waveforms offered as building blocks; sine, ramp, exponential, logarithmic, step, clipped sine, square, and sawtooth. Also contains routine for plotting output waveform data on terminal with Tektronics (or equivalent) emulator, providing visual confirmation of desirability of waveform. Developed by use of VMS FORTRAN on DEC VAX 3600 computer.

Hopson, Charles B.↗