Publications
Covariance estimation with scanning arrays: FY23 RF Systems Technical Investment Program
December 21, 2023
Project Report
Published in:
MIT Lincoln Laboratory Report TIP-194
Summary
Analog arrays with steerable beams can be capable of angle estimation and sometimes even adaptive beamforming based on power measurements taken at the outputs of multiple beam dwells. In the interesting case of a reflectarray, where beams are formed using a large collection of programmable, passive phase shifters, it is possible to use multiple dwells to estimate signal correlations among the phase shifters. These correlations form an estimated covariance matrix at the phase centers of the shifters. Adaptive beamforming and geolocation can be based on this covariance matrix. Various methods for estimating full-rank and approximately rank-deficient covariance matrices using power measurements from multiple dwells are introduced and evaluated. In some cases, the performance of an estimator can be shown to be optimal in the sense of achieving Cramer-Rao bounds for the estimated covariance parameters.
Summary
Analog arrays with steerable beams can be capable of angle estimation and sometimes even adaptive beamforming based on power measurements taken at the outputs of multiple beam dwells. In the interesting case of a reflectarray, where beams are formed using a large collection of programmable, passive phase shifters, it is...
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Robust network protocols for large swarms of small UAVs
March 5, 2022
Conference Paper
Published in:
2022 IEEE Aeropsace Conf., AERO 2022, 18 p.
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Summary
In this work, we detail a synchronized channel hopping network for autonomous swarms of small unmanned aerial vehicles (UAVs) conducting intelligence, surveillance, and reconnaissance (ISR) missions in the presence of interference and jamming. The core component of our design is Queue Length Informed Maximal Matching (QLIMM), a distributed transmission scheduling protocol that exchanges queue state information between nodes to assign subdivisions of the swarm to orthogonal hopping patterns in response to the network’s throughput demands. QLIMM efficiently allocates channel resources across large networks without relying on any centralized control or pre-planned traffic patterns, which is in the spirit of a swarming capability. However, given that the control messaging must scale up with the swarm’s size and the challenging interference environments we consider, fragility could be a concern. To observe under what conditions control fails, we test our protocol against both simulated partial-band noise jamming and background interference. For the latter, we use data collected from a small unmanned aircraft system to characterize the interference seen by a UAV in the 2.4 and 5 GHz bands in both urban and rural settings. These measurements show that the interference can be 15 dB higher at a 50-meter flight altitude when compared to observations on the ground. Using this data, we conduct extensive network simulations of QLIMM in Riverbed Modeler to show that, under moderate jamming and interference, it outperforms traditional channel access methods as well as other scheduling protocols that do not pass queue state information.
Summary
In this work, we detail a synchronized channel hopping network for autonomous swarms of small unmanned aerial vehicles (UAVs) conducting intelligence, surveillance, and reconnaissance (ISR) missions in the presence of interference and jamming. The core component of our design is Queue Length Informed Maximal Matching (QLIMM), a distributed transmission scheduling...
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Detecting pathogen exposure during the non-symptomatic incubation period using physiological data: proof of concept in non-human primates
September 3, 2021
Journal Article
Published in:
Front Physiol., Vol. 12, September 2021, Article 691074.
Summary
Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices. Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device. Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001. Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.
Summary
Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First...
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Priority scheduling for multi-function apertures with hard- and soft-time constraints
March 6, 2021
Conference Paper
Published in:
2021 IEEE Aerospace Conf., 6-13 March 2021.
Summary
A multi-function aperture (MFA) is an antenna array that supports multiple RF signals for a diverse set of activities. An MFA may support multiple activities simultaneously if they are compatible, and platforms may utilize multiple MFAs to meet field-of-regard and frequency range requirements. Efficient MFA utilization requires a Resource Manager (RM) that routes signals to the correct MFA based on field-of-view and other requirements, and schedules MFA access to resolve conflicts based on request priority. An efficient RM scheduler time-interleaves requests from different activities as needed. Requested access events may be hard-time—that is, the event must be scheduled at a specified time or not at all, or soft-time, indicating it may be scheduled anytime in a specified window. Hard-time events include communications channels with assigned time slots, and soft-time events include asynchronous communications channels. This paper describes and evaluates an optimal algorithm to jointly schedule sequences of hard-time requests, maximizing the number of scheduled events while meeting priority requirements. An extension of this algorithm provides near-optimal schedules for sequences of soft-time or mixed hard- and soft-time events. Algorithms are evaluated by simulation, using two conflict models. The first is based on fixed signal paths that conflict if two paths share a common resource. The second model assumes the RM dynamically assigns resources. As implemented, these algorithms are too slow for real-time operation, and further work is required. They do provide insight into the MFA management problem, a useful metric for evaluating resource sharing and scheduling approaches, and may suggest efficient sub-optimal algorithms.
Summary
A multi-function aperture (MFA) is an antenna array that supports multiple RF signals for a diverse set of activities. An MFA may support multiple activities simultaneously if they are compatible, and platforms may utilize multiple MFAs to meet field-of-regard and frequency range requirements. Efficient MFA utilization requires a Resource Manager...
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Using oculomotor features to predict changes in optic nerve sheath diameter and ImPACT scores from contact-sport athletes
March 4, 2021
Journal Article
Published in:
Front. Neurol., 4 March 2021, Art. No. 584684.
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Summary
There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential for oculomotor features to complement existing diagnostic tools, such as measurements of Optic Nerve Sheath Diameter (ONSD) and Immediate Post-concussion Assessment and Cognitive Testing (ImPACT). Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Given the high risk of repetitive head impacts associated with both soccer and football, our hypotheses were that (1) ONSD and ImPACT scores would worsen through the season and (2) oculomotor features would effectively capture both neurophysiological changes reflected by ONSD and neuro-functional status assessed via ImPACT. Oculomotor features were used as input to Linear Mixed-Effects Regression models to predict ONSD and ImPACT scores as outcomes. Prediction accuracy was evaluated to identify explicit relationships between eye movements, ONSD, and ImPACT scores. Significant Pearson correlations were observed between predicted and actual outcomes for ONSD (Raw = 0.70; Normalized = 0.45) and for ImPACT (Raw = 0.86; Normalized = 0.71), demonstrating the capability of oculomotor features to capture neurological changes detected by both ONSD and ImPACT. The most predictive features were found to relate to motor control and visual-motor processing. In future work, oculomotor models, linking neural structures to oculomotor function, can be built to gain extended mechanistic insights into neurophysiological changes observed through seasons of participation in contact sports.
Summary
There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential...
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Beamforming with distributed arrays: FY19 RF Systems Line-Supported Program
January 7, 2020
Project Report
Published in:
MIT Lincoln Laboratory Report LSP-270
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Summary
Spatial beamforming using distributed arrays of RF sensors is treated. Unlike the observations from traditional RF antenna arrays, the distributed array's data can be subjected to widely varying time and frequency shifts among sensors and signals. These shifts require compensation upon reception in order to perform spatial filtering. To perform beamforming with a distributed array, the complex-valued observations from the sensors are shifted in time and frequency, weighted, and summed to form a beamformer output that is designed to mitigate interference and enhance signal energy. The appropriate time-frequency shifts required for good beamforming are studied here using several different methodologies.
Summary
Spatial beamforming using distributed arrays of RF sensors is treated. Unlike the observations from traditional RF antenna arrays, the distributed array's data can be subjected to widely varying time and frequency shifts among sensors and signals. These shifts require compensation upon reception in order to perform spatial filtering. To perform...
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Collaborative and passive channel gain estimation in fading environments
June 12, 2019
Journal Article
Published in:
IEEE Trans. Cognitive Commun. and Netw., Vol. 5, No. 4, December 2019, pp. 863-72.
Summary
Dynamic spectrum access techniques are typically aided by knowledge of the wireless channel gains among participating radios, as this knowledge allows the potential interference impact of any radio's transmissions on its neighbors to be quantified. We present a technique for collaborative inference of the channel gains which relies solely on the radios monitoring their aggregate transmitted and received energies as they transmit their data packets. We demonstrate that through low data-rate exchange of these energy metrics among bursty networks, the gains can be jointly estimated within a dB and with low latency on the order of seconds. In particular, we derive the best linear unbiased estimator (BLUE) for the gains. While this estimator relies on knowledge of fading parameters not known in practice, we propose a practical variant which achieves performance comparable to the BLUE in the realistic fading setting used in our simulations.
Summary
Dynamic spectrum access techniques are typically aided by knowledge of the wireless channel gains among participating radios, as this knowledge allows the potential interference impact of any radio's transmissions on its neighbors to be quantified. We present a technique for collaborative inference of the channel gains which relies solely on...
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Detecting pathogen exposure during the non-symptomatic incubation period using physiological data
November 13, 2017
Journal Article
Published in:
bioRxiv preprint doi: https://doi.org/10.1101/218818, 13 November 2017.
Summary
Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with highresolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.
Summary
Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states...
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High performance, 3D-printable dielectric nanocomposites for millimeter wave devices
December 5, 2016
Journal Article
Published in:
ACS Appl. Mater. Interfaces, Vol. 8, 2016, 34019-26.
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Summary
The creation of millimeter wave, 3D-printable dielectric nanocomposite is demonstrated. Alumina nanoparticles were combined with styrenic block copolymers and solvent to create shear thinning, viscoelastic inks that are printable at room temperature. Particle loadings of up to 41 vol % were achieved. Upon being dried, the highest-performing of these materials has a permittivity of 4.61 and a loss tangent of 0.00298 in the Ka band (26.5-40 GHz), a combination not previously demonstrated for 3D printing. These nanocomposite materials were used to print a simple resonator device with predictable pass-band features.
Summary
The creation of millimeter wave, 3D-printable dielectric nanocomposite is demonstrated. Alumina nanoparticles were combined with styrenic block copolymers and solvent to create shear thinning, viscoelastic inks that are printable at room temperature. Particle loadings of up to 41 vol % were achieved. Upon being dried, the highest-performing of these materials...
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Polymer dielectrics for 3D-printed RF devices in the Ka band
May 6, 2016
Journal Article
Published in:
Adv. Mater. Technol., Vol. 1, No. 2, May 2016, 1600027.
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Summary
Direct-write printing allows the fabrication of centimeter-wave radio devices. Most polymer dielectric polymer materials become lossy at frequencies above 10 GHz. Presented here is a printable dielectric material with low loss in the K a band (26.5–40 GHz). This process allows the fabrication of resonator filter devices and a radio antenna.
Summary
Direct-write printing allows the fabrication of centimeter-wave radio devices. Most polymer dielectric polymer materials become lossy at frequencies above 10 GHz. Presented here is a printable dielectric material with low loss in the K a band (26.5–40 GHz). This process allows the fabrication of resonator filter devices and a radio...
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