Lean Neural Networks for Autonomous Radar Waveform Design

In recent years, neural networks have exploded in popularity, revolutionizing the domains of computer vision, natural language processing, and autonomous systems. This is due to neural networks ability to approximate complex non-linear functions. Despite their effectiveness, they generally require large labeled data sets and considerable processing power for both training and prediction. Some of these bottlenecks have been mitigated by recent increased availability of high-quality data sets, improvements in neural network development software, and greater hardware support. Due to algorithmic bloat, neural network inference times and imprecision make them undesirable for some problems where fast classical algorithm solutions already exist, other classes of algorithms, such as convex optimization, with non-trivial execution times could be reduced using neural solutions. These algorithms could be replaced with light-weight neural networks, benefiting from their high degree of parallelization and high accuracy when properly trained. Previous work has explored how low size, weight, and power (low SWaP) neural networks and neuromorphic computing can be used to improve autonomous radar waveform design techniques that currently rely on convex optimization. Autonomous radar waveform design helps meet the need for interference mitigation caused by an ever-growing number of consumer and commercial technologies which pollute the radio frequency (RF) spectrum. Spectral notching, a radar waveform design technique, augments transmitted radar waveforms to avoid frequencies with excessive interference while maintaining the integrity of the waveform. In this paper, we extend that work, demonstrating that lean neural networks and specialized hardware can improve inference time for waveform design without sacrificing accuracy. Our lean neural solution incorporates problem-specific information into the layer structures and loss functions to decrease network size and improve accuracy. We provide model outcomes implemented on radio frequency system on a chip (RFSoC) hardware that support our simulation results. Our neural network solution decreases inference time on traditional CPU hardware by 1057× and on GPU hardware accelerators by 883× while maintaining 99% cosine similarity

Dunes on Titan observed by Cassini Radar

Thousands of longitudinal dunes have recently been discovered by the Titan Radar Mapper on the surface of Titan. These are found mainly within ±30° of the equator in optically-, near-infrared-, and radar-dark regions, indicating a strong proportion of organics, and cover well over 5% of Titan's surface. Their longitudinal duneform, interactions with topography, and correlation with other aeolian forms indicate a single, dominant wind direction aligned with the dune axis plus lesser, off-axis or seasonally alternating winds. Global compilations of dune orientations reveal the mean wind direction is dominantly eastwards, with regional and local variations where winds are diverted around topographically high features, such as mountain blocks or broad landforms. Global winds may carry sediments from high latitude regions to equatorial regions, where relatively drier conditions prevail, and the particles are reworked into dunes, perhaps on timescales of thousands to tens of thousands of years. On Titan, adequate sediment supply, sufficient wind, and the absence of sediment carriage and trapping by fluids are the dominant factors in the presence of dunes

RADAR imaging of giant longitudinal dunes: Namib desert (Earth) and the Belet sand sea (Titan)

itan's dark regions. Long dunes, like Zen rock garden. Seas of sand, not oil

The Sand Seas of Titan: Cassini RADAR Observations of Longitudinal Dunes

International audienceThe most recent Cassini RADAR images of Titan show widespread regions (up to 1500 kilometers by 200 kilometers) of near-parallel radar-dark linear features that appear to be seas of longitudinal dunes similar to those seen in the Namib desert on Earth. The Ku-band (2.17-centimeter wavelength) images show ~100-meter ridges consistent with duneforms and reveal flow interactions with underlying hills. The distribution and orientation of the dunes support a model of fluctuating surface winds of ~0.5 meter per second resulting from the combination of an eastward flow with a variable tidal wind. The existence of dunes also requires geological processes that create sand-sized (100- to 300-micrometer) particulates and a lack of persistent equatorial surface liquids to act as sand traps

RADAR Imaging of Giant Longitudinal Dunes: Namib Desert (Earth) and the Belet Sand Sea (Titan)

International audienc

RADAR Imaging of Giant Longitudinal Dunes: Namib Desert (Earth) and the Belet Sand Sea (Titan)

International audienc

RADAR Imaging of Giant Longitudinal Dunes: Namib Desert (Earth) and the Belet Sand Sea (Titan)

International audienc

Cryovolcanic features on Titan's surface as revealed by the Cassini Titan Radar Mapper

International audienceThe Cassini Titan Radar Mapper obtained Synthetic Aperture Radar images of Titan's surface during four fly-bys during the mission's first year. These images show that Titan's surface is very complex geologically, showing evidence of major planetary geologic processes, including cryovolcanism. This paper discusses the variety of cryovolcanic features identified from SAR images, their possible origin, and their geologic context. The features which we identify as cryovolcanic in origin include a large (180 km diameter) volcanic construct (dome or shield), several extensive flows, and three calderas which appear to be the source of flows. The composition of the cryomagma on Titan is still unknown, but constraints on rheological properties can be estimated using flow thickness. Rheological properties of one flow were estimated and appear inconsistent with ammonia-water slurries, and possibly more consistent with ammonia-water-methanol slurries. The extent of cryovolcanism on Titan is still not known, as only a small fraction of the surface has been imaged at sufficient resolution. Energetic considerations suggest that cryovolcanism may have been a dominant process in the resurfacing of Titan