The Hilbert-Huang Transform Real-Time Data Processing System
information technology and software
The Hilbert-Huang Transform Real-Time Data Processing System (GSC-TOPS-63)
Analyzing nonlinear and nonstationary signals
Overview
One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). The Fourier view of nonlinear mechanics that had existed for a long time and the associated FFT carry strong a-priori assumptions about the source data, such as linearity and being stationary. Natural phenomena measurements are essentially nonlinear and nonstationary.
A recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT) proposes a novel approach to the solution for the nonlinear class of spectrum analysis problems.
The Technology
The present innovation is an engineering tool known as the HHT Data Processing System (HHTDPS). The HHTDPS allows applying the Transform, or 'T,' to a data vector in a fashion similar to the heritage FFT. It is a generic, low cost, high performance personal computer (PC) based system that implements the HHT computational algorithms in a user friendly, file driven environment. Unlike other signal processing techniques such as the Fast Fourier Transform (FFT1 and FFT2) that assume signal linearity and stationarity, the Hilbert-Huang Transform (HHT) utilizes relationships between arbitrary signals and local extrema to find the signal instantaneous spectral representation.
Using the Empirical Mode Decomposition (EMD) followed by the Hilbert Transform of the empirical decomposition data, the HHT allows spectrum analysis of nonlinear and nonstationary data by using an engineering a-posteriori data processing, based on the EMD algorithm. This results in a non-constrained decomposition of a source real value data vector into a finite set of Intrinsic Mode Functions (IMF) that can be further analyzed for spectrum interpretation by the classical Hilbert Transform.
The HHTDPS has a large variety of applications and has been used in several NASA science missions.
NASA cosmology science missions, such as Joint Dark Energy Mission (JDEM/WFIRST), carry instruments with multiple focal planes populated with many large sensor detector arrays with sensor readout electronics circuitry that must perform at extremely low noise levels.
A new methodology and implementation platform using the HHTDPS for readout noise reduction in large IR/CMOS hybrid sensors was developed at NASA Goddard Space Flight Center (GSFC). Scientists at NASA GSFC have also used the algorithm to produce the first known Hilbert-Transform based wide-field broadband data cube constructed from actual interferometric data.
Furthermore, HHT has been used to improve signal reception capability in radio frequency (RF) communications.
This NASA technology is currently available to the medical community to help in the diagnosis and prediction of syndromes that affect the brain, such as stroke, dementia, and traumatic brain injury.
The HHTDPS is available for non-exclusive and partial field of use licenses.
Benefits
- Unlike other signal processing techniques such as the Fast Fourier Transform (FFT1 and FFT2), HHT does not assume signal linearity and stationarity
- HHT utilizes relationships between arbitrary signals and local extrema to find the signal instantaneous spectral representation
Applications
- The HHT Data Processing System is broadly applicable to analyzing nonlinear and nonstationary signals while improving the accuracy of linear- and stationary-signal analysis
- Structural damage detection
- Analyzing dynamic and earthquake motion recordings in studies of seismology and engineering
- Pitch determination in speech recognition
- Geometrical Signal Processing
- Biological Signal Processing
- Geophysical Signal Processing
- Analyzing nonstationary financial time series
Technology Details
information technology and software
GSC-TOPS-63
GSC-16328-1
GSC-15584-1
GSC-16181-1
Kizhner, S., Flatley, T., Huang, N., Blank, K., & Conwell, E. (2004). On the Hilbert-Huang transform data processing system development. 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).
Similar Results
Data Transfer for Multiple Sensor Networks
High-temperature sensors have been used in silicon carbide electronic oscillator circuits. The frequency of the oscillator changes as a function of changes in the sensor's parameters, such as pressure. This change is analogous to changes in the pitch of a person's voice. The output of this oscillator, and many others may be superimposed onto a single medium. This medium may be the power lines supplying current to the sensors, a third wire dedicated to data transmission, the airwaves through radio transmission, or an optical or other medium. However, with nothing to distinguish the identities of each source, this system is useless. Using frequency dividers and linear feedback shift registers, comprised of flip flops and combinatorial logic gates connected to each oscillator, unique bit stream codes may be generated. These unique codes are used to amplitude modulate the output of the sensor (both amplitude shift keying and on-off keying are applicable). By using a dividend of the oscillator frequency to generate the code, a constant a priori number of oscillator cycles will define each bit. At the receiver, a detected frequency will have associated with it a stored code pattern. Thus, a detected frequency will have a unique modulation pattern or "voice," disassociating it from noise and from other transmitting sensors. These codes may be pseudorandom binary sequences (PRBS), ASCII characters, gold codes, etc. The detected code length and frequency are measured, offering intelligent data transfer.
This is an early-stage technology requiring additional development. Glenn welcomes co-development opportunities.
Adaptive Algorithm and Software for Recognition of Ground-based, Airborne, Underground, and Underwater Low Frequency Events
Acoustical studies of atmospheric events like convective storms, shear-induced turbulence, acoustic gravity waves, microbursts, hurricanes, and clear air turbulence over the last forty-five years have established that these events are strong emitters of infrasound (sound at frequencies below 20 Hz). Over the years, NASA Langley has designed and developed a portable infrasonic detection system which can be used to make useful infrasound measurements at a location where it was not possible previously. The system comprises an electret condenser microphone, and a small, compact windscreen. The system has been modified to be used in the air, underground, as well as underwater (to determine man-made and precursor to tsunami). The system also features a data acquisition system that permits real-time detection, bearing, and signature of a low frequency source. However, to determine bearing of the received signals, the microphones are to be arranged as an equilateral triangle with a certain microphone spacing. The spacing depends upon location of the microphone array. For a ground-based array, the microphone spacing of 100 feet (30.48m) is desired to determine time delay for signals arriving at each microphone location. The microphone spacing depends upon speed of sound through the array medium. For underwater array, the spacing between microphones would be around 1500 feet. The data acquisition system provides data output in the infrasonic bandwidth which is then analyzed using an adaptive algorithm (least-mean-squares time-delay-estimation) using modern computational power to locate source by plotting source location hyperbolas on-line.
A smaller array size reduces the time resolution resulting in strong signal coherence. The innovation approach is to exploit modern signal processing methods, i.e. adaptive filtering, where computer is trained on-line to recognize features of the event to be detected. Modern computational capability permits the adaptive algorithm (least-mean-squares time-delay estimation or LMSTDE) which is vastly more powerful algorithm. This system has better resolution able to determine direction with arrived signals within five-degree accuracy.
More Reliable Doppler Lidar for Autonomous Navigation
The NDL uses homodyne detection to obtain changes in signal frequency caused by a target of interest. Frequency associated with each segment of the modulated waveform collected by the instrument is positive or negative, depending on the relative range and direction of motion between the NDL and the target. Homodyne detection offers a direct measurement of signal frequency changes however only the absolute values of the frequencies are measured, therefore additional information is necessary to determine positive or negative sign of the detected frequencies. The three segmented waveform, as opposed to conventional two-segmented ones, allows for resolving the frequency sign ambiguity. In a practical system, there are times when one or more of the three frequencies are not available during a measurement. For these cases, knowledge of the relative positions of the frequency sideband components is used to predict direction of the Doppler shift and sign, and thus make correct range and velocity measurements. This algorithm provides estimates to the sign of the intermediate frequencies. The instrument operates continuously in real time, producing independent range and velocity measurements by each line of sight used to take the measurement. In case of loss of one of the three frequencies, past measurements of range and velocity are used by the algorithm to provide estimates of the expected new range and velocity measurement. These estimates are obtained by applying an estimation filter to past measurements. These estimates are used during signal loss to reduce uncertainty in the sign of the frequencies measured once signals are re-established, and never to replace value of a measurement.
Extreme Low Frequency Hydrophone
The extreme low frequency hydrophone boasts unprecedented capability for precise detection as proven in testing, where it sensed wave range between surface waves and tidal surges with periods between .3 and 30 seconds, or 3 to .033 Hz.
The technology uses a back-electret microphone, inherently reducing noise, in a stainless steel body. The stainless steel diaphragm conducts infrasound well and the materials robust nature and internal configuration facilitates sub-freezing and deeply submerged sensing of sound down to .0001 Hz.
With an appropriately spaced array of three hydrophones it is possible to determine the direction of origin of a submerged infrasonic source, the addition of one more in another location will also enable determination of the precise location of origin.
The oil industry uses existing infrasound systems to locate undersea oil deposits and this technology could potentially improve the accuracy or reliability of current practices. It could also be used to give tsunami and earthquake warnings, monitor ships, and to generate electrical energy from infrasound. This technology has potential to unlock new industry uses not currently understood due to the unprecedented nature of its capabilities.
Low Frequency Portable Acoustic Measurement System
Langley has developed various technologies to enable the portable detection system, including:
- 3-inch electret condenser microphone - unprecedented sensitivity of -45 dB/Hz
- compact nonporous windscreen - suitable for replacing spatially demanding soaker hoses in current use
- infrasonic calibrator for field use - piston phone with a test signal of 110 dB at 14Hz.
- laboratory calibration apparatus - to very low frequencies
- vacuum isolation vessel - sufficiently anechoic to permit measurement of background noise in microphones at frequencies down to a few Hz
- mobile source for reference - a Helmholtz resonator that provides pure tone at 19 Hz
The NASA system uses a three-element array in the field to locate sources of infrasound and their direction. This information has been correlated with PIREPs available in real time via the Internet, with 10 examples of good correlation.
