Defense Notices

We describe an autonomous driving system that uses reinforcement learning to train a car to drive without the need for collecting training input from human drivers.  We achieve this by using the Advantage Actor Critic reinforcement system that trains the car based on continuously adapting the model to minimize the penalty received by the car.  This penalty is determined if the car intersected the borders of the track on which it is driving.  We show the resilience of the proposed autonomously trained system to noisy sensor inputs and variations in the shape of the track.

The Center for Oldest Ice Exploration (COLDEX) is an NSF-funded multi-institution collaboration to explore Antarctica for the oldest possible continuous ice record. It comprises of exploration and modelling teams that are using instruments like radars, lidars, gravimeters, and magnetometers to select candidate locations to collect a continuous 1.5-million-year ice core. To assist in this search for old ice, the Center for Remote Sensing and Integrated Systems (CReSIS) at the University of Kansas developed a new airborne higher-power version of the 600-900 MHz Accumulation Radar with a much larger multichannel cross-track antenna array. The fuselage portion of the antenna array is a 64-element 0.9 m by 3.8 m array with 4 elements in along-track and 16 elements in cross-track. Each element is a dual-polarized microstrip antenna and each column of 4 elements is power combined into a single channel resulting in 16 cross-track channels. Power is transmitted across 4 cross-track channels on either side of the fuselage array alternatingly to produce a total peak power of 6.4 kW (before losses). Three additional antennas are integrated on each wing to lengthen the antenna aperture. A novel receiver concept is developed using limiters to compress the dynamic range to simultaneously capture the strong ice surface and weak ice bottom returns. This system was flown on a Basler aircraft at the South Pole during the 2022-2023 Austral Summer season and will be flown again during the upcoming 2023-2024 season for repeat interferometry. This work describes the current radar system design and proposes to develop improvements to the compact, high-power divider and large multichannel polarimetric array used by the radar. It then proposes to develop and implement a system engineering perspective on the calibration of this multi-pass imaging radar.

Even for a fixed time-bandwidth product there are infinite possible spectrally-shaped random FM (RFM) waveforms one could generate due to their being phase-continuous. Moreover, certain RFM classes rely on an imposed basis-like structure scaled by underlying parameters that can be optimized (e.g. gradient descent and greedy search have been demonstrated). Because these structures must include oversampling with respect to 3-dB bandwidth to account for sufficient spectral roll-off (necessary to be physically realizable in hardware), they are not true bases (i.e. not square). Therefore, any individual structure cannot represent all possible waveforms, with the waveforms generated by a given structure tending to possess similar attributes. Unless of course we consider over-coded polyphaser-coded FM (PCFM), which increases the number of elements in the parameter vector, while maintaining the relationship between waveform samples and the time-bandwidth product. Which presents the potential for a true bases, if there is a constraint either explicit or implicit that will constrain the spectrum. Here we examine waveforms possessing different attributes, as well as the potential for a true basis which may inform their selection for given radar applications.

During the car insurance claims process after an accident has occurred, a vehicle must be assessed by a claims adjuster manually. This process will take time and often results in inaccuracies between what a customer is paid and what the damages actually cost. Separately, companies like KBB and Carfax rely on previous claims records or untrustworthy user input to determine a car’s damage and valuation. Part of this process can be automated to determine where exterior vehicle damage exists on a vehicle. 

In this project, a deep-learning approach is taken using the MaskR-CNN model to train on a dataset for instance segmentation. The model can then outline and label instances on images where vehicles have dents, scratches, cracks, broken glass, broken lamps, and flat tires. The results have shown that broken glass, flat tires, and broken lamps are much easier to locate than the remaining categories, which tend to be smaller in size. These predictions have an end goal of being used as an input for damage cost prediction. 

During the car insurance claims process after an accident has occurred, a vehicle must be assessed by a claims adjuster manually. This process will take time and often results in inaccuracies between what a customer is paid and what the damages actually cost. Separately, companies like KBB and Carfax rely on previous claims records or untrustworthy user input to determine a car’s damage and valuation. Part of this process can be automated to determine where exterior vehicle damage exists on a vehicle. 

In this project, a deep-learning approach is taken using the MaskR-CNN model to train on a dataset for instance segmentation. The model can then outline and label instances on images where vehicles have dents, scratches, cracks, broken glass, broken lamps, and flat tires. The results have shown that broken glass, flat tires, and broken lamps are much easier to locate than the remaining categories, which tend to be smaller in size. These predictions have an end goal of being used as an input for damage cost prediction. 

During the car insurance claims process after an accident has occurred, a vehicle must be assessed by a claims adjuster manually. This process will take time and often results in inaccuracies between what a customer is paid and what the damages actually cost. Separately, companies like KBB and Carfax rely on previous claims records or untrustworthy user input to determine a car’s damage and valuation. Part of this process can be automated to determine where exterior vehicle damage exists on a vehicle. 

In this project, a deep-learning approach is taken using the MaskR-CNN model to train on a dataset for instance segmentation. The model can then outline and label instances on images where vehicles have dents, scratches, cracks, broken glass, broken lamps, and flat tires. The results have shown that broken glass, flat tires, and broken lamps are much easier to locate than the remaining categories, which tend to be smaller in size. These predictions have an end goal of being used as an input for damage cost prediction. 

Soft errors are frequent occurrences within extensive computing platforms, primarily attributed to the growing size and intricacy of high-performance computing (HPC) systems. To safeguard scientific applications against such errors, diverse resilience approaches have been introduced, encompassing techniques like checkpointing, Algorithm-Based Fault Tolerance (ABFT), and replication, each operating at distinct tiers of defense. Notably, system-level replication often necessitates the duplication or triplication of the entire computational process, yielding substantial resilience-associated costs. This project introduces a method for dynamic selective safeguarding of sparse iterative solvers, with a focus on the Preconditioned Conjugate Gradient (PCG) solver, aiming to mitigate system level resilience overhead. For this method, we leverage machine learning (ML) to predict the impact of soft errors that strike different elements of a key computation (i.e., sparse matrix-vector multiplication) at different iterations of the solver. Based on the result of the prediction, we design a dynamic strategy to selectively protect those elements that would result in a large performance degradation if struck by soft errors. Experimental assessment validates the efficacy of our dynamic protection strategy in curbing resilience overhead in contrast to prevailing algorithms.

The last century has seen incredible growth in the field of quantum computing. Quantum computation offers the opportunity to find efficient solutions to certain computational problems which are intractable on classical computers. One class of problems that seems to benefit from quantum computing is the Hidden Subgroup Problem (HSP). The HSP includes, as special cases, the problems of integer factoring, discrete logarithm, shortest vector, and subset sum - making the HSP incredibly important in various fields of research.                               

The presented research examines the HSP for Dihedral groups with order 2^n and proves a quantum polynomial-time reduction to the so-called Codomain Fiber Intersection Problem (CFIP). The usual approach to the HSP relies on harmonic analysis in the domain of the problem and the best-known algorithm using this approach is sub-exponential, but still super-polynomial. The algorithm we will present deviates from the usual approach by focusing on the structure encoded in the codomain and uses this structure to direct a “walk” down the subgroup lattice terminating at the hidden subgroup.                               

Though the algorithm presented here is specifically designed for the DHSP, it has potential applications to many other types of the HSP. It is hypothesized that any group with a sufficiently structured subgroup lattice could benefit from the analysis developed here. As this approach diverges from the standard approach to the HSP it could be a promising step in finding an efficient solution to this problem.

Information theory provides methods for quantifying the information content of observed signals and has found application in the radar sensing space for many years. Here, we examine a type of information derived from Fisher information known as Marginal Fisher Information (MFI) and investigate its use to design pulse-agile waveforms. By maximizing this form of information, the expected error covariance about an estimation parameter space may be minimized. First, a novel method for designing MFI optimal waveforms given an arbitrary waveform model is proposed and analyzed. Next, a transformed domain approach is proposed in which the estimation problem is redefined such that information is maximized about a linear transform of the original estimation parameters. Finally, informationally optimal waveform design is paired with informationally optimal estimation (receive processing) and are combined into a cognitive radar concept. Initial experimental results are shown and a proposal for continued research is presented.

In uniform pulse-Doppler radar, there is a well known trade-off between unambiguous Doppler and unambiguous range. Pulse repetition interval (PRI) staggering, a technique that involves modulating the interpulse times, addresses this trade-space allowing for expansion of the unambiguous Doppler domain with little range swath incursion. Random PRI staggering provides additional diversity, but comes at the cost of increased Doppler sidelobes. Thus, careful PRI sequence design is required to avoid spurious sidelobe peaks that could result in false alarms.

In this thesis, two random PRI stagger models are defined and compared, and sidelobe peak mitigation is discussed. First, the co-array concept (borrowed from the intuitively related field of sparse array design in the spatial domain) is utilized to examine the effect of redundancy on sidelobe peaks for random PRI sequences. Then, a sidelobe peak suppression technique is introduced that involves a gradient-based optimization of the random PRI sequences, producing pseudo-random sequences that are shown to significantly reduce spurious Doppler sidelobes in both simulation and experimentally.