no code implementations • 30 Nov 2023 • William W. Howard, Samuel R. Shebert, Anthony F. Martone, R. Michael Buehrer
Cognitive Radar Networks, which were popularized by Simon Haykin in 2006, have been proposed to address limitations with legacy radar installations.
no code implementations • 25 Oct 2023 • William W. Howard, Samuel R. Shebert, Benjamin H. Kirk, R. Michael Buehrer
The goal of the network is to learn over many target tracks both the characteristics of the targets as well as the optimal action choices for each type of target.
no code implementations • 30 Aug 2023 • R. Michael Buehrer, William W. Howard, Steven Ellingson
Second, since in many cases these weights require gains other than one, we develop a technique for determining unit-modulus weights so as to allow for surfaces which merely modify the phase of the scattered field, while substantially reducing the gain at arbitrary angles.
no code implementations • 24 Jul 2023 • William W. Howard, Anthony F. Martone, R. Michael Buehrer
We discuss centralized and distributed approaches to solving this problem, taking into account the quality of node observations, the maneuverability of each target, and a limit on the rate at which any node may provide updates to the FC.
no code implementations • 30 Jun 2023 • Don-Roberts Emenonye, Anik Sarker, Alan T. Asbeck, Harpreet S. Dhillon, R. Michael Buehrer
More specifically, we investigate the possibility of using on-body RISs to estimate the location information over time of upper limbs that may have been impaired due to stroke.
no code implementations • 29 May 2023 • Jordan Budhu, Sean V. Hum, Steven Ellingson, R. Michael Buehrer
The design of a reflectarray which can be used to reconfigure a radio telescopes radiation pattern by driving a null to the angle of incoming interference is presented.
no code implementations • 21 Apr 2023 • Charles E. Thornton, R. Michael Buehrer
This paper attempts to characterize the kinds of physical scenarios in which an online learning-based cognitive radar is expected to reliably outperform a fixed rule-based waveform selection strategy, as well as the converse.
no code implementations • 27 Feb 2023 • Gaurav Duggal, R. Michael Buehrer, Nishith Tripathi, Jeffrey H. Reed
The LoS probability and coverage probabilities derived in this paper can be used to analyze the outdoor UAV-to-indoor propagation environment to determine optimal UAV positioning and the number of UAVs needed to achieve the desired performance of the emergency network.
no code implementations • 7 Feb 2023 • Samuel R. Shebert, Benjamin H. Kirk, R. Michael Buehrer
To address unknown signals, we propose an open set hybrid classifier, which combines deep learning and expert feature classifiers to leverage the reliability and explainability of expert feature classifiers and the lower computational complexity of deep learning classifiers.
no code implementations • 28 Jan 2023 • William W. Howard, R. Michael Buehrer
We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio varies by channel and over time for a target with fixed radar cross section, a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection.
no code implementations • 1 Dec 2022 • Charles E. Thornton, R. Michael Buehrer
When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy?
no code implementations • 1 Dec 2022 • Charles E. Thornton, William W. Howard, R. Michael Buehrer
This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection for target identification in frequency modulated continuous wave (FMCW) automotive radar systems.
no code implementations • 21 Nov 2022 • William W. Howard, Charles E. Thornton, R. Michael Buehrer
When each radar node in the network only is able to obtain noisy state measurements for a subset of the targets, the fusion center may not receive updates on every target during each update period.
no code implementations • 1 Sep 2022 • Jianyuan Yu, William W. Howard, Yue Xu, R. Michael Buehrer
Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation.
no code implementations • 20 Jul 2022 • William Howard, R. Michael Buehrer
Completely decentralized Multi-Player Bandit models have demonstrated high localization accuracy at the cost of long convergence times in cognitive radar networks.
no code implementations • 7 Jul 2022 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone
This results in sample-efficient learning across a class of finite state target channels by exploiting inherent similarity across tracking scenes, attributed to common physical elements such as target type or clutter statistics.
no code implementations • 5 Jul 2022 • Charles E. Thornton, R. Michael Buehrer
As a result, convergence to the optimal jamming strategy can be slow, especially when the victim and jammer's symbols are not perfectly synchronized.
no code implementations • 3 Jun 2022 • Don-Roberts Emenonye, Harpreet S. Dhillon, R. Michael Buehrer
Specifically, we start from the assumption that the position and orientation of a RIS can be viewed as prior information for RIS-aided localization in wireless systems and derive Bayesian bounds for the localization of a user equipment (UE).
no code implementations • 4 Mar 2022 • William Howard, Anthony Martone, R. Michael Buehrer
We approach this problem using online Machine Learning (ML) techniques.
no code implementations • 26 Feb 2022 • R. Michael Buehrer, Steve W. Ellingson
Further, it is shown that weights can be obtained that both cancel sidelobes while providing a constant main lobe gain.
no code implementations • 10 Feb 2022 • Charles E. Thornton, R. Michael Buehrer, Harpreet S. Dhillon, Anthony F. Martone
More recently, reinforcement learning has been proposed for waveform selection, in which the problem is framed as a Markov decision process (MDP), allowing for long-term planning.
no code implementations • 28 Dec 2021 • Jianyuan Yu, Pu, Wang, Toshiaki Koike-Akino, Ye Wang, Philip V. Orlik, R. Michael Buehrer
The granularity matching is realized by pairing two feature maps from the CSI and beam SNR at different granularity levels and linearly combining all paired feature maps into a fused feature map with learnable weights.
no code implementations • 9 Nov 2021 • Don-Roberts Emenonye, Carl Dietrich, R. Michael Buehrer
We derive an expression for the maximum likelihood (ML) detector of a differentially encoded phase information symbol received by a base station operating in the low-resolution ADC regime.
no code implementations • 27 Oct 2021 • Don-Roberts Emenonye, Carl Dietrich, R. Michael Buehrer
We note that while the one-bit detector can decode the differentially encoded phase information symbols, it fails to decode the differentially encoded amplitude information.
no code implementations • 1 Oct 2021 • C. Tanner Fredieu, Anthony Martone, R. Michael Buehrer
Results for the IF models showed an overall accuracy of 98% when detecting known and unknown signals with signal impairments present.
no code implementations • 18 Aug 2021 • William W. Howard, R. Michael Buehrer
The first method is iterative and uses only a series of observed bearings to multiple targets to establish clusters.
no code implementations • 16 Aug 2021 • C. Tanner Fredieu, Justin Bui, Anthony Martone, Robert J. Marks II, Charles Baylis, R. Michael Buehrer
Synchronization of the signals is not needed as we assume there is an unknown and uncompensated time and frequency offset.
no code implementations • 3 Aug 2021 • Samuel R. Shebert, Anthony F. Martone, R. Michael Buehrer
The closed set classifier achieves an average accuracy of 94. 5% for known signals with SNR's greater than 0 dB, but by design, has a 0% accuracy detecting signals from unknown classes.
no code implementations • 2 Aug 2021 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone
The proposed approach is tested in a simulation study, and is shown to provide tracking performance improvements over two state-of-the-art waveform selection schemes.
no code implementations • 9 Mar 2021 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone
It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient.
no code implementations • 13 Oct 2020 • Megan Moore, William H. Clark IV, R. Michael Buehrer, William C. Headley
Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals.
no code implementations • 1 Oct 2020 • Lauren J. Wong, William H. Clark IV, Bryse Flowers, R. Michael Buehrer, Alan J. Michaels, William C. Headley
While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications.
no code implementations • 24 Aug 2020 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone
Additionally, we show that the TS learning scheme results in a favorable SINR distribution compared to other online learning algorithms.
no code implementations • 10 Jul 2020 • Jianyuan Yu, Yue Xu, Hussein Metwaly Saad, R. Michael Buehrer
With the increasing power of machine learning-based reasoning, the use of meta-information (e. g., digital signal modulation parameters, channel conditions, etc.)
no code implementations • 23 Jun 2020 • Charles E. Thornton, Mark A. Kozy, R. Michael Buehrer, Anthony F. Martone, Kelly D. Sherbondy
In this paper, dynamic non-cooperative coexistence between a cognitive pulsed radar and a nearby communications system is addressed by applying nonlinear value function approximation via deep reinforcement learning (Deep RL) to develop a policy for optimal radar performance.
no code implementations • 12 Apr 2020 • Jianyuan Yu, William W. Howard, Daniel Tait, R. Michael Buehrer
A vector sensor, a type of sensor array with six collocated antennas to measure all electromagnetic field components of incident waves, has been shown to be advantageous in estimating the angle of arrival and polarization of the incident sources.
no code implementations • 4 Feb 2020 • Jianyuan Yu, R. Michael Buehrer
Modern techniques in the Internet of Things or autonomous driving require more accuracy positioning ever.
no code implementations • 3 Feb 2020 • Jianyuan Yu, Mohammad Alhassoun, R. Michael Buehrer
The recent success in implementing supervised learning to classify modulation types suggests that other problems akin to modulation classification would eventually benefit from that implementation.
no code implementations • 6 Jan 2020 • Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone, Kelly D. Sherbondy
In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting.
no code implementations • 1 Mar 2019 • Bryse Flowers, R. Michael Buehrer, William C. Headley
Recent advancements in radio frequency machine learning (RFML) have demonstrated the use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks.
no code implementations • 17 Apr 2016 • Daniel J. Jakubisin, R. Michael Buehrer, Claudio R. C. M. da Silva
We then develop a low-complexity variant to the proposed construction in which Gaussian BP is applied to the equalization factors.
no code implementations • 13 Nov 2014 • SaiDhiraj Amuru, Cem Tekin, Mihaela van der Schaar, R. Michael Buehrer
We first present novel online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs and prove that our learning algorithm converges to the optimal (in terms of the error rate inflicted at the victim and the energy used) jamming strategy.