Defense Notices

The Doppler tolerance of a waveform refers to its behavior when subjected to a fast-time Doppler shift imposed by scattering that involves nonnegligible radial velocity. While previous efforts have established decision-based criteria that lead to a binary judgment of Doppler tolerant or intolerant, it is also useful to establish a measure of the degree of Doppler tolerance. The purpose in doing so is to introduce a Doppler "quasi-tolerant" trade-space that can ultimately inform automated/cognitive waveform design in increasingly complex and dynamic radio frequency (RF) environments. This idea of Doppler quasi-tolerance leads to the development of random FM (RFM) waveforms that retain a degree of Doppler tolerance while still providing the diversity of a nonrepeating waveform structure. The ensuing ambiguity functions split the delay/Doppler ridge into a variety of different patterns. Since these patterns are known at transmission, a strategy for appropriate coherent slow time combining is demonstrated in simulation. Separately, the application of slow-time coding (STC) to the Doppler-tolerant linear FM (LFM) waveform has been examined for disambiguation of multiple range ambiguities. However, using STC with non-adaptive Doppler processing often results in high Doppler "cross-ambiguity" side lobes that can hinder range disambiguation despite the degree of separability imparted by STC. To enhance this separability, a gradient-based optimization of STC sequences is developed, and a "multi-range" (MR) modification to the reiterative super-resolution (RISR) approach that accounts for the distinct range interval structures from STC is examined. The efficacy of these approaches is demonstrated using open-air measurements. Pulse agility is an alternative range disambiguation technique that relies on pulse-to-pulse waveform separability. Although pulse-agile waveforms are often uncorrelated and therefore amenable to range disambiguation, they may exhibit poor Doppler tolerance. To preserve Doppler tolerance and achieve separability, a class of hybrid waveforms is developed whereby a phase code is embedded on an LFM base waveform. A gradient-based optimization is developed for this waveform structure to achieve enhanced suppression of range-folded scattering in desired delay/Doppler regions. The Doppler tolerance and separability of the optimized waveforms are examined in simulation, and open-air measurements are used to demonstrate the range disambiguation capability.

Deploying deep neural networks for perception on autonomous vehicles forces a compromise between how accurately the system perceives and how quickly it responds. This compromise is especially binding on embedded compute platforms, where limited processing power means a high-accuracy detector may fail to finish within the control loop's timing budget, leaving the vehicle to act on outdated information. Anytime perception offers a way to manage this by adjusting inference cost at runtime, but its benefits have so far been shown mainly in simulation, with little evidence from physical deployment.

This thesis provides that evidence. We take MURAL — a multi-resolution anytime LiDAR detector previously integrated into the Autoware stack and evaluated in the AWSIM simulator — and deploy it on a physical mid-size rover, running the full sensing-to-actuation pipeline on a single NVIDIA Jetson AGX Orin. Reaching a working deployment required substantial adaptation of a stack originally built for full-scale vehicles in simulation, from retargeting the vehicle model to rover scale to bringing the entire pipeline on-board a single embedded device.

By carrying the complete stack onto real hardware, this work makes it possible to evaluate anytime perception under the conditions it was designed for: a full autonomous-driving pipeline running on an edge device in the physical world. We assess, through end-to-end physical experiments, whether dynamically scaling detection resolution delivers a real performance benefit on embedded hardware — providing, to our knowledge, the first true evaluation of anytime perception for edge-deployed autonomous driving.

Modern security relies heavily on public key cryptography, and private keys and secrets in general must be protected from attackers. Against a highly-capable adversary it is ideal to store secrets outside of main memory, which is easy on general purpose systems with the now widely-available Trusted Platform Module (TPM) 2.0. However, the lack of integration between the TPM and the OS makes protecting secrets with automated availability needs difficult. We develop a strategy to authenticate OS entities and protect TPM-stored secrets without restricting access to the TPM, using standard features available on Linux---SELinux, Integrity Measurement Architecture (IMA), Extended Verification Module (EVM), and Linux Unified Key Setup (LUKS).