Search Results for author: R. Michael Buehrer

Found 42 papers, 0 papers with code

Mode Selection in Cognitive Radar Networks

no code implementations30 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.

Mode Selection and Target Classification in Cognitive Radar Networks

no code implementations25 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.

Open and Closed-Loop Weight Selection for Pattern Control of Paraboloidal Reflector Antennas with Reconfigurable Rim Scattering

no code implementations30 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.

Timely Target Tracking: Distributed Updating in Cognitive Radar Networks

no code implementations24 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.

Decision Making

RIS-Aided Kinematic Analysis for Remote Rehabilitation

no code implementations30 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.

Design of Rim-Located Reconfigurable Reflectarrays for Interference Mitigation in Reflector Antennas

no code implementations29 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.

On the Value of Online Learning for Radar Waveform Selection

no code implementations21 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.

Decision Making

Line-of-Sight Probability for Outdoor-to-Indoor UAV-Assisted Emergency Networks

no code implementations27 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.

Open Set Wireless Signal Classification: Augmenting Deep Learning with Expert Feature Classifiers

no code implementations7 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.

Hybrid Cognition for Target Tracking in Cognitive Radar Networks

no code implementations28 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.

When is Cognitive Radar Beneficial?

no code implementations1 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?

Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar

no code implementations1 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.

reinforcement-learning Reinforcement Learning (RL) +1

Timely Target Tracking in Cognitive Radar Networks

no code implementations21 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.

Decentralized Bandits with Feedback for Cognitive Radar Networks

no code implementations20 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.

Online Bayesian Meta-Learning for Cognitive Tracking Radar

no code implementations7 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.

Meta-Learning

Linear Jamming Bandits: Sample-Efficient Learning for Non-Coherent Digital Jamming

no code implementations5 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.

Fundamentals of RIS-Aided Localization in the Far-Field

no code implementations3 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).

Weight Selection for Pattern Control of Paraboloidal Reflector Antennas with Reconfigurable Rim Scattering

no code implementations26 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.

Universal Learning Waveform Selection Strategies for Adaptive Target Tracking

no code implementations10 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.

reinforcement-learning Reinforcement Learning (RL)

Multi-Band Wi-Fi Sensing with Matched Feature Granularity

no code implementations28 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.

Indoor Localization

Differential Modulation in Massive MIMO With Low-Resolution ADCs

no code implementations9 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.

Quantization

Differential Deep Detection in Massive MIMO With One-Bit ADC

no code implementations27 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.

Quantization

Open-set Classification of Common Waveforms Using A Deep Feed-forward Network and Binary Isolation Forest Models

no code implementations1 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.

open-set classification

Multi-Target Localization Using Polarization Sensitive Arrays

no code implementations18 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.

Classification of Common Waveforms Including a Watchdog for Unknown Signals

no code implementations16 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.

Classification

Open Set Wireless Standard Classification Using Convolutional Neural Networks

no code implementations3 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.

Classification

Waveform Selection for Radar Tracking in Target Channels With Memory via Universal Learning

no code implementations2 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.

Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection

no code implementations9 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.

Thompson Sampling

When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning

no code implementations13 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.

BIG-bench Machine Learning Decision Making

The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications

no code implementations1 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.

BIG-bench Machine Learning

Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling

no code implementations24 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.

Thompson Sampling

Predicting Bit Error Rate from Meta Information using Random Forests

no code implementations10 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.)

Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

no code implementations23 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.

Q-Learning reinforcement-learning +1

Direction of Arrival Estimation for a Vector Sensor Using Deep Neural Networks

no code implementations12 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.

Direction of Arrival Estimation

Interference Classification Using Deep Neural Networks

no code implementations3 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.

Classification General Classification

Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar

no code implementations6 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.

Q-Learning reinforcement-learning +1

Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications

no code implementations1 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.

BIG-bench Machine Learning

Probabilistic Receiver Architecture Combining BP, MF, and EP for Multi-Signal Detection

no code implementations17 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.

graph construction

Jamming Bandits

no code implementations13 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.

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