Adaptive Radar Thresholding for Cluttered Environments

The current innovation utilizes heritage flight proven L-band Digital Beamforming Synthetic Aperture Radar (DBSAR) in conjunction with a new P-Band Digital beamforming Polarimetric and Interferometric EcoSAR (ESTO IIP) architecture. The system employs digital beamforming (DBF) and reconfigurable hardware to provide advanced radar capabilities not possible with conventional radar instruments. The SAR is operated without the use of a slewing antenna allowing the single radar system to provide polarimetric imaging, interferometry, and altimetry or scatterometry data types. The SAR is also capable of Sweep-SAR, simultaneous SAR/GNSS-R , and simultaneous active/passive techniques. This system has an increased coverage area and can rapidly image large areas of the surface using the simultaneous left/right imaging. The resulting images maintain their full resolution and allows for faster full coverage mapping

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.

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.

The Virtual Target Generator (VTG) is a system designed to create radio frequency (RF) signals that mimic the presence of nearby aircraft, for testing satellite-based surveillance systems. The VTG works by using a GPS receiver or other position data source to calculate a virtual target position, which may be calculated by applying predefined offsets and trajectories relative to the host. These virtual positions are encoded into standard message formats, such as ADS-B, and modulated onto an RF carrier signal. The VTG includes a calibrated signal amplifier that adjusts the signal power so only the target test aircraft receives the signal, ensuring no other aircraft are affected. The test aircraft's surveillance avionics detect and interpret these virtual targets as if they were actual aircraft, enabling full participation in the testing of traffic collision avoidance, surveillance, or display systems. The VTG supports complex test scenarios including dynamic movement, multiple simultaneous targets, and customizable aircraft parameters such as speed, heading, and altitude. The system is fully compliant with FAA ADS-B specifications and can be installed on the test aircraft itself or on a nearby companion aircraft.

Lightning-AI is a machine learning system that addresses the critical gap in lightning safety by providing predictive warnings before the first strike occurs. The technology uses a combined CNN/LSTM architecture to identify atmospheric signals that lead to lightning initiation and converts them into short-term probabilistic forecasts. The model ingests four sequential WSR-88D radar scans spaced roughly 5 to 6 minutes apart, incorporating polarimetric variables including horizontal reflectivity, differential reflectivity, and correlation coefficient that reveal mixed-phase microphysics and graupel growth driving cloud electrification. The radar data is transformed into a uniform two-kilometer grid creating a consistent spatial framework. Before processing, data is normalized and filtered to remove non-meteorological clutter. The model uses lightning initiation points from the Geostationary Lightning Mapper as ground truth observations to learn physical signatures of developing storms that appear minutes before the first flash. The CNN identifies spatial electrification patterns while the LSTM interprets their temporal evolution across sequential scans. Together, they detect subtle microphysical cues of impending lightning initiation, even before precipitation reaches the surface. This capability transforms lightning safety from reactive to proactive, offering more accurate threat identification. Validation results demonstrate the system can forecast lightning 15 to 30 minutes in advance, achieving an 84% probability of detection with a 22% false alarm rate. The algorithm operates in near-real-time using existing radar infrastructure and can integrate into commercial weather applications, emergency management systems, and automated alert platforms. Currently at TRL 5, Lightning-AI is available for patent licensing.