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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.

The technology utilizes photopolymer droplets (invisible to the digital radiograph) with embedded radiopaque fragments to create randomized fingerprints on battery samples. The droplets are deposited using a jig (see figure on right) that precisely positions samples. Then, at different points during battery R&D testing or use, digital radiography imaging with micron-level resolution can be performed. The high-resolution imaging required to detect dendrite formation requires images to be collected in multiple “tiles” as shown below. The randomized fingerprints uniquely identify relative positioning of these tiles, allowing rapid assembly of composite high-resolution images from multiple tiles. This same composite creation process can be used for images taken at a series of points in time during testing, and background subtraction can be applied to efficiently compare how the battery is changing over successive charge/discharge cycles to identify dendrite formation. This inspection technique is proven effective for thin-film pouch cell prototypes at NASA, and it works well at the lowest available x-ray energy level (limiting impact on the samples). The Fingerprinting for Rapid Battery Inspection technology is available for patent licensing.

This concept presents a new statistical likelihood function and Bayesian analysis update for non-standard measurement types that rely on associations between observed and cataloged features. These measurement types inherently contain non-standard errors that standard techniques, such as the Kalman filter, make no effort to model, and this mismodeling can lead to filter instability and degraded performance. Vision-based navigation methods utilizing the Kalman filter involve a preprocessing step to identify features within an image by referencing a known catalog. However, errors in this pre-processing can cause navigation failures. AFP offers a new approach, processing points generated by features themselves without requiring identification. Points such as range or bearing are directly processed by AFP. Operating on finite set statistics principles, AFP treats data as sets rather than individual features. This enables simultaneous tracking of multiple targets without feature labeling. Unlike the sequential processing of the Kalman filter, AFP processes updates in parallel, independently scoring each output based on rigorous mathematical functions. This parallel processing ensures robust navigation updates in dynamic environments, and without requiring an identification algorithm upstream of the filter. Computational simulations conducted at Johnson Space Center demonstrate that AFP's performance matches or exceeds that of the ideal Kalman filter, even under non-ideal conditions. Anonymous Feature Processing for Enhanced Navigation is at a technology readiness level (TRL) 4 (component and/or breadboard validation in laboratory environment) and is now available for patent licensing. Please note that NASA does not manufacture products itself for commercial sale.

The DLC platform is composed of three key components: a NASA-designed field programmable gate array (FPGA) board, a NASA-designed multiprocessor on-a-chip (MPSoC) board, and a proprietary datapath that links the boards to available inputs and outputs to enable high-bandwidth data collection and processing. The inertial measurement unit (IMU), camera, Navigation Doppler Lidar (NDL), and Hazard Detection Lidar (HDL) navigation sensors (depicted in the diagram below) are connected to the DLC’s FPGA board. The datapath on this board consists of high-speed serial interfaces for each sensor, which accept the sensor data as input and converts the output to an AXI stream format. The sensor streams are multiplexed into an AXI stream which is then formatted for input to a XAUI high speed serial interface. This interface sends the data to the MPSoC Board, where it is converted back from the XAUI format to a combined AXI stream, and demultiplexed back into individual sensor AXI streams. These AXI streams are then inputted into respective DMA interfaces that provide an interface to the DDRAM on the MPSoC board. This architecture enables real-time high-bandwidth data collection and processing by preserving the MPSoC’s full ability. This sensor datapath architecture may have other potential applications in aerospace and defense, transportation (e.g., autonomous driving), medical, research, and automation/control markets where it could serve as a key component in a high-performance computing platform and/or critical embedded system for integrating, processing, and analyzing large volumes of data in real-time.

Proprotors on tiltrotor aircraft have complex aeroelastic properties, experiencing torsion, bending, and chord movement vibrational modes, in addition to whirl flutter dynamic instabilities. These dynamics can be stabilized by high-frequency swashplate adjustments to alter the incidence angle between the swashplate and the rotor shaft (cyclic control) and blade pitch (collective control). To make these high-speed adjustments while minimizing control inputs, generalized predictive control (GPC) algorithms predict future outputs based on previous system behavior. However, these algorithms are limited by the fact that tiltrotor systems can substantially change in orientation and airspeed during a normal flight regime, breaking system continuity for predictive modeling. NASA’s Advanced GPC (AGPC) is a self-adaptive algorithm that overcomes these limitations by identifying system changes and adapting its predictive behavior as flight conditions change. If system vibration conditions deteriorate below a set threshold for a set time interval, the AGPC will incrementally update its model parameters to improve damping response. AGPC has shown significant performance enhancements over conventional GPC algorithms in comparative simulations based on an analytical model of NASA’s TiltRotor Aeroelastic Stability Testbed (TRAST). Research for Hardware-In-the-Loop testing and flight vehicle deployment is ongoing, and hover data show improved vibration reduction and stability performance using AGPC over other methods. The example presented here is an application to tiltrotor aircraft for envelope expansion and vibration reduction. However, AGPC can be employed on many dynamic systems.