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NASA NTRS1973

Recursive ideal observer detection of known M-ary signals in multiplicative and additive Gaussian noise.

This paper presents the derivation of the recursive algorithms necessary for real-time digital detection of M-ary known signals that are subject to independent multiplicative and additive Gaussian noises. The motivating application is minimum probability of error detection of digital data-link messages aboard civil aircraft in the earth reflection multipath environment. For each known signal, the detector contains one Kalman filter and one probability computer. The filters estimate the multipath disturbance. The estimates and the received signal drive the probability computers. Outputs of all the computers are compared in amplitude to give the signal decision. The practicality and usefulness of the detector are extensively discussed.

Painter, J. H.↗

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 NTRS1990

Infrared fiber coupled acousto-optic tunable filter spectrometer

A spectrometer design is introduced which combines an acoustooptic tunable filter (AOTF) and IR-transmitting flouride-glass fibers. The AOTF crystal is fabricated from TeO2 and permits random access to any wavelength in less than 50 microseconds, and the resulting spectrometer is tested for the remote analysis of gases and hydrocarbons. The AOTF spectrometer, when operated with a high-speed frequency synthesizer and optimized algorithms, permits accurate high-speed spectroscopy in the mid-IR spectral region.

Levin, K. H.↗

NASA NTRS1992

Development of the Advance Warning Airborne System(AWAS)

The thermal characteristics of microbursts are utilized by the AWAS IR and OAT features to provide predictive warning of hazardous microbursts ahead of the aircraft during landing or take off. The AWAS was evaluated satisfactorily in 1990 on a Cessna Citation that was intentionally flown into a number of wind shear events. The events were detected, and both the IR and OAT thermal features were shown to be effective. In 1991, AWAS units were flown on three American Airline MD-80's and three Northwest Airlines DC-9's to study and to decrease the nuisance alert response of the system. The AWAS was also flown on the NASA B737 during the summer of 1991. The results of these flights were inconclusive and disappointing. The results were not as promising as before because NASA conducted research flights which were outside of the normal operating envelope for which the AWAS is designed to operate. In an attempt to compensate for these differences in airspeed and mounting location, the automatic features of the system were sometimes overridden by NASA personnel during the flight. Each of these critical factors is discussed in detail. The effect of rain on the OAT signals is presented as a function of the air speed. Use of a 4 pole 1/20 Hertz filter is demonstrated by both the IR and thermal data. Participation in the NASA 1992 program was discussed. FAA direction in the continuing Certification program requires the addition of a reactive feature to the AWAS predictive system. This combined system will not require flight guidance on newer aircraft. The features of AWAS-IV, with the NASA algorithm included, were presented. Expected completion of the FAA Certification plan was also described.

Adamson, H. Patrick↗

NASA NTRS1995

Parallel Climate Data Assimilation PSAS Package Achieves 18 GFLOPs on 512-Node Intel Paragon

Several algorithms were added to the Physical-space Statistical Analysis System (PSAS) from Goddard, which assimilates observational weather data by correcting for different levels of uncertainty about the data and different locations for mobile observation platforms. The new algorithms and use of the 512-node Intel Paragon allowed a hundred-fold decrease in processing time.

weather prediction climate modeling data assimilat↗

NASA NTRS1991

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

Kumar, Rajendra↗

DOE OSTI2020

Fatigue-Crack Detection and Monitoring through the Scattered-Wave Two-Dimensional Cross-Correlation Imaging Method Using Piezoelectric Transducers

Piezoelectric transducers are convenient enablers for generating and receiving Lamb waves for damage detection. Fatigue cracks are one of the most common causes for the failure of metallic structures. Increasing emphasis on the integrity of critical structures creates an urgent need to monitor structures and to detect cracks at an early stage to prevent catastrophic failures. This paper presents a two-dimensional (2D) cross-correlation imaging technique that can not only detect a fatigue crack but can also precisely image the fatigue cracks in metallic structures. The imaging method was based on the cross-correlation algorithm that uses incident waves and the crack-scattered waves of all directions to generate the crack image. Fatigue testing for crack generation was then conducted in both an aluminum plate and a stainless-steel plate. Piezoelectric wafer transducer was used to actuate the interrogating Lamb wave. To obtain the scattered waves as well as the incident waves, a scanning laser Doppler vibrometer was adopted for acquiring time-space multidimensional wavefield, followed with frequency-wavenumber processing. The proof-of-concept study was conducted in an aluminum plate with a hairline fatigue crack. A frequency-wavenumber filtering method was used to obtain the incident wave and the scattered wave wavefields for the cross-correlation imaging. After this, the imaging method was applied to evaluate cracks on a stainless-steel plate generated during fatigue loading tests. The presented imaging method showed successful inspection and quantification results of the crack and its growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1991

Video guidance sensor

A Martin-Marietta study comparing the application of laser, video, or RF sensors was conducted in 1982. The study concluded that video was the most attractive sensor (the video also could be used for operator monitoring). The Retro-Reflector field Tracker from the Solar Array Flight Experiment was chosen as a 'first generation' sensor and integrated with guidance algorithms for evaluation on the air-bearing vehicle. Results indicated that this sensor was not applicable for the noise environment posed by the multi-layer insulation used on most spacecraft. A 'second generation' sensor was developed to be used with a modified RMS target. This sensor utilized two sets of laser diodes to acquire three optically filtered targets. The targets were illuminated first with a 780 nanometer diode, followed by illumination with a 830 nm diode. The second digitized picture was subtracted from the first to get a low-noise image. The centroids of the retroreflectors were used then to uniquely derive target attitude and range. The sensor presently operates to 80 feet and within +/- 40 degrees in pitch and yaw. Sensor operability is a concern if the sun is within a +/- 40 degree cone angle to the target. The present sensor performance characteristics are less than 1 percent range error and less than 1 degree angle error. Future sensor development is anticipated to extend the operating range to 150 feet and reduce the cone angle of sensor inoperability to within +/- 10 degrees of direct sunlight. Performance improvements also are anticipated. TRW currently is developing a system that utilizes dual cameras with simultaneous diode illumination. Although further development is being pursued, the basic system has proven sound, and the sensor is essentially ready for application.

Howard, Richard↗

NASA NTRS2004

Development and Evaluation of Fault-Tolerant Flight Control Systems

The research is concerned with developing a new approach to enhancing fault tolerance of flight control systems. The original motivation for fault-tolerant control comes from the need for safe operation of control elements (e.g. actuators) in the event of hardware failures in high reliability systems. One such example is modem space vehicle subjected to actuator/sensor impairments. A major task in flight control is to revise the control policy to balance impairment detectability and to achieve sufficient robustness. This involves careful selection of types and parameters of the controllers and the impairment detecting filters used. It also involves a decision, upon the identification of some failures, on whether and how a control reconfiguration should take place in order to maintain a certain system performance level. In this project new flight dynamic model under uncertain flight conditions is considered, in which the effects of both ramp and jump faults are reflected. Stabilization algorithms based on neural network and adaptive method are derived. The control algorithms are shown to be effective in dealing with uncertain dynamics due to external disturbances and unpredictable faults. The overall strategy is easy to set up and the computation involved is much less as compared with other strategies. Computer simulation software is developed. A serious of simulation studies have been conducted with varying flight conditions.

Song, Yong D.↗

NASA NTRS2004

High Speed Operation and Testing of a Fault Tolerant Magnetic Bearing

Research activities undertaken to upgrade the fault-tolerant facility, continue testing high-speed fault-tolerant operation, and assist in the commission of the high temperature (1000 degrees F) thrust magnetic bearing as described. The fault-tolerant magnetic bearing test facility was upgraded to operate to 40,000 RPM. The necessary upgrades included new state-of-the art position sensors with high frequency modulation and new power edge filtering of amplifier outputs. A comparison study of the new sensors and the previous system was done as well as a noise assessment of the sensor-to-controller signals. Also a comparison study of power edge filtering for amplifier-to-actuator signals was done; this information is valuable for all position sensing and motor actuation applications. After these facility upgrades were completed, the rig is believed to have capabilities for 40,000 RPM operation, though this has yet to be demonstrated. Other upgrades included verification and upgrading of safety shielding, and upgrading control algorithms. The rig will now also be used to demonstrate motoring capabilities and control algorithms are in the process of being created. Recently an extreme temperature thrust magnetic bearing was designed from the ground up. The thrust bearing was designed to fit within the existing high temperature facility. The retrofit began near the end of the summer, 04, and continues currently. Contract staff authored a NASA-TM entitled "An Overview of Magnetic Bearing Technology for Gas Turbine Engines", containing a compilation of bearing data as it pertains to operation in the regime of the gas turbine engine and a presentation of how magnetic bearings can become a viable candidate for use in future engine technology.

DeWitt, Kenneth↗

NASA NTRS2006

NASA Tech Briefs, April 2006

The topics covered include: 1) Replaceable Sensor System for Bioreactor Monitoring; 2) Unitary Shaft-Angle and Shaft-Speed Sensor Assemblies; 3) Arrays of Nano Tunnel Junctions as Infrared Image Sensors; 4) Catalytic-Metal/PdO(sub x)/SiC Schottky-Diode Gas Sensors; 5) Compact, Precise Inertial Rotation Sensors for Spacecraft; 6) Universal Controller for Spacecraft Mechanisms; 7) The Flostation - an Immersive Cyberspace System; 8) Algorithm for Aligning an Array of Receiving Radio Antennas; 9) Single-Chip T/R Module for 1.2 GHz; 10) Quantum Entanglement Molecular Absorption Spectrum Simulator; 11) FuzzObserver; 12) Internet Distribution of Spacecraft Telemetry Data; 13) Semi-Automated Identification of Rocks in Images; 14) Pattern-Recognition Algorithm for Locking Laser Frequency; 15) Designing Cure Cycles for Matrix/Fiber Composite Parts; 16) Controlling Herds of Cooperative Robots; 17) Modification of a Limbed Robot to Favor Climbing; 18) Vacuum-Assisted, Constant-Force Exercise Device; 19) Production of Tuber-Inducing Factor; 20) Quantum-Dot Laser for Wavelengths of 1.8 to 2.3 micron; 21) Tunable Filter Made From Three Coupled WGM Resonators; and 22) Dynamic Pupil Masking for Phasing Telescope Mirror Segments.

Source record↗

NASA NTRS2010

Platform for Post-Processing Waveform-Based NDE

Signal- and image-processing methods are commonly needed to extract information from the waves, improve resolution of, and highlight defects in an image. Since some similarity exists for all waveform-based nondestructive evaluation (NDE) methods, it would seem that a common software platform containing multiple signal- and image-processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. NDE Wave & Image Processor Version 2.0 software provides a single, integrated signal- and image-processing and analysis environment for total NDE data processing and analysis. It brings some of the most useful algorithms developed for NDE over the past 20 years into a commercial-grade product. The software can import signal/spectroscopic data, image data, and image series data. This software offers the user hundreds of basic and advanced signal- and image-processing capabilities including esoteric 1D and 2D wavelet-based de-noising, de-trending, and filtering. Batch processing is included for signal- and image-processing capability so that an optimized sequence of processing operations can be applied to entire folders of signals, spectra, and images. Additionally, an extensive interactive model-based curve-fitting facility has been included to allow fitting of spectroscopy data such as from Raman spectroscopy. An extensive joint-time frequency module is included for analysis of non-stationary or transient data such as that from acoustic emission, vibration, or earthquake data.

Roth, Don J.↗

NASA NTRS2017

Pointing System Simulation Toolbox with Application to a Balloon Mission Simulator

The development of attitude estimation and pointing-control algorithms is necessary in order to achieve high-fidelity modeling for a Balloon Mission Simulator (BMS). A pointing system simulation toolbox was developed to enable this. The toolbox consists of a star-tracker (ST) and Inertial Measurement Unit (IMU) signal generator, a UDP (User Datagram Protocol) communication le (bridge), and an indirect-multiplicative extended Kalman filter (imEKF). This document describes the Python toolbox developed and the results of its implementation in the imEKF.

Maringolo Baldraco, Rosana M.↗

NASA NTRS2021

Features and Characteristics of the New NASA MicroPuLse NETwork (MPLNET) Automatic Rain Detection Algorithm

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics.

S. Lolli↗

NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗

NASA NTRS1991

A fast algorithm for control and estimation using a polynomial state-space structure

One of the major problems associated with the control of flexible structures is the estimation of system states. Since the parameters of the structures are not constant under varying loads and conditions, conventional fixed parameter state estimators can not be used to effectively estimate the states of the system. One alternative is to use a state estimator which adapts to the condition of the system. One such estimator is the Kalman filter. This filter is a time varying recursive digital filter which is based upon a model of the system being measured. This filter adapts the model according to the output of the system. Previously, the Kalman filter has only been used in an off-line capacity due to the computational time required for implementation. With recent advances in computer technology, it is becoming a viable tool for use in the on-line environment. A distributed Kalman filter implementation is described for fast estimation of the state of a flexible arm. A key issue, is the sensor structure and initial work on a distributed sensor that could be used with the Kalman filter is presented.

Shults, James R.↗

NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗

NASA NTRS2012

Level-1C Product from AIRS: Principal Component Filtering

The Atmospheric Infrared Sounder (AIRS), launched on the EOS Aqua spacecraft on May 4, 2002, is a grating spectrometer with 2378 channels in the range 3.7 to 15.4 microns. In a grating spectrometer each individual radiance measurement is largely independent of all others. Most measurements are extremely accurate and have very low noise levels. However, some channels exhibit high noise levels or other anomalous behavior, complicating applications needing radiances throughout a band, such as cross-calibration with other instruments and regression retrieval algorithms. The AIRS Level-1C product is similar to Level-1B but with instrument artifacts removed. This paper focuses on the "cleaning" portion of Level-1C, which identifies bad radiance values within spectra and produces substitute radiances using redundant information from other channels. The substitution is done in two passes, first with a simple combination of values from neighboring channels, then with principal components. After results of the substitution are shown, differences between principal component reconstructed values and observed radiances are used to investigate detailed noise characteristics and spatial misalignment in other channels.

Noise Equivalent delta Temperature (NEdT)↗