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Jason N. Gross
West Virginia University, Morgantown, WV, USA

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Journal article
Published: 08 June 2021 in IEEE Aerospace and Electronic Systems Magazine
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A waypoint planning algorithm for an unmanned aerial vehicle (UAV) is presented that is teamed with an unmanned ground vehicle (UGV) for the task of search and rescue in a subterranean environment. The UAV and UGV are teamed such that the localization of the UAV is conducted on the UGV via the multisensor fusion of a fisheye camera, 3-D light detection and ranging, ranging radio, and a laser altimeter. Likewise, the trajectory planning of the UAV is conducted on the UGV, which is assumed to have a 3-D map of the environment (e.g., from simultaneous localization and mapping). The goal of the planning algorithm is to satisfy the mission's exploration criteria while reducing the localization error of the UAV by evaluating the belief space for potential exploration routes. The presented algorithm is evaluated in a relevant simulation environment where the planning algorithm is shown to be effective at reducing the localization errors of the UAV.

ACS Style

Matteo De Petrillo; Jared Beard; Yu Gu; Jason N. Gross. Search Planning of a UAV/UGV Team With Localization Uncertainty in a Subterranean Environment. IEEE Aerospace and Electronic Systems Magazine 2021, 36, 6 -16.

AMA Style

Matteo De Petrillo, Jared Beard, Yu Gu, Jason N. Gross. Search Planning of a UAV/UGV Team With Localization Uncertainty in a Subterranean Environment. IEEE Aerospace and Electronic Systems Magazine. 2021; 36 (6):6-16.

Chicago/Turabian Style

Matteo De Petrillo; Jared Beard; Yu Gu; Jason N. Gross. 2021. "Search Planning of a UAV/UGV Team With Localization Uncertainty in a Subterranean Environment." IEEE Aerospace and Electronic Systems Magazine 36, no. 6: 6-16.

Journal article
Published: 25 March 2021 in IEEE Robotics and Automation Letters
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The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization performance with ZUPT. To do this, we propose a 3D dead-reckoning approach that predicts wheel slippage while the rover is in motion and forecasts the appropriate time to stop without changing any rover hardware or major rover operations. We validate with field tests that our approach is viable on different terrain types and achieves a 3D localization accuracy of ~97% over 650 m drives on rough terrain.

ACS Style

Cagri Kilic; Nicholas Ohi; Yu Gu; Jason Gross. Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization. IEEE Robotics and Automation Letters 2021, 6, 4782 -4789.

AMA Style

Cagri Kilic, Nicholas Ohi, Yu Gu, Jason Gross. Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization. IEEE Robotics and Automation Letters. 2021; 6 (3):4782-4789.

Chicago/Turabian Style

Cagri Kilic; Nicholas Ohi; Yu Gu; Jason Gross. 2021. "Slip-Based Autonomous ZUPT Through Gaussian Process to Improve Planetary Rover Localization." IEEE Robotics and Automation Letters 6, no. 3: 4782-4789.

Journal article
Published: 01 July 2020 in Remote Sensing
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Making use of dual-frequency (DF) global navigation satellite system (GNSS) observations and good dynamic models, the precise orbit determination (POD) for the satellites on low earth orbits has been intensively investigated in the last decades and has achieved an accuracy of centimeters. With the rapidly increasing number of the CubeSat missions in recent years, the POD of CubeSats were also attempted with combined dynamic models and GNSS DF observations. While comprehensive dynamic models are allowed to be used in the postprocessing mode, strong constraints on the data completeness, continuity, and restricted resources due to the power and size limits of CubeSats still hamper the high-accuracy POD. An analysis of these constraints and their impact on the achievable orbital accuracy thus needs to be considered in the planning phase. In this study, with the focus put on the use of DF GNSS data in postprocessing CubeSat POD, a detailed sensitivity analysis of the orbital accuracy was performed w.r.t. the data continuity, completeness, observation sampling interval, latency requirements, availability of the attitude information, and arc length. It is found that the overlapping of several constraints often causes a relatively large degradation in the orbital accuracy, especially when one of the constraints is related to a low duty-cycle of, e.g., below 40% of time. Assuming that the GNSS data is properly tracked except for the assumed constraints, and using the International GNSS Service (IGS) final products or products from the IGS real-time service, the 3D orbital accuracy for arcs of 6 h to 24 h should generally be within or around 1 dm, provided that the limitation on data is not too severe, i.e., with a duty-cycle not lower than 40% and an observation sampling interval not larger than 60 s.

ACS Style

Kan Wang; Amir Allahvirdi-Zadeh; Ahmed El-Mowafy; Jason Gross. A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources. Remote Sensing 2020, 12, 2107 .

AMA Style

Kan Wang, Amir Allahvirdi-Zadeh, Ahmed El-Mowafy, Jason Gross. A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources. Remote Sensing. 2020; 12 (13):2107.

Chicago/Turabian Style

Kan Wang; Amir Allahvirdi-Zadeh; Ahmed El-Mowafy; Jason Gross. 2020. "A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources." Remote Sensing 12, no. 13: 2107.

Journal article
Published: 09 March 2020 in IEEE Robotics and Automation Letters
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Recent advances in the fields of robotics and automation have spurred significant interest in robust state estimation. To enable robust state estimation, several methodologies have been proposed. One such technique, which has shown promising performance, is the concept of iteratively estimating a Gaussian Mixture Model (GMM), based upon the state estimation residuals, to characterize the measurement uncertainty model. Through this iterative process, the measurement uncertainty model is more accurately characterized, which enables robust state estimation through the appropriate de-weighting of erroneous observations. This approach, however, has traditionally required a batch estimation framework to enable the estimation of the measurement uncertainty model, which is not advantageous to robotic applications. In this paper, we propose an efficient, incremental extension to the measurement uncertainty model estimation paradigm. The incremental covariance estimation (ICE) approach, as detailed within this paper, is evaluated on several collected data sets, where it is shown to provide a significant increase in localization accuracy when compared to other state-of-the-art robust, incremental estimation algorithms.

ACS Style

Ryan M. Watson; Jason N. Gross; Clark N. Taylor; Robert C. Leishman. Robust Incremental State Estimation Through Covariance Adaptation. IEEE Robotics and Automation Letters 2020, 5, 3737 -3744.

AMA Style

Ryan M. Watson, Jason N. Gross, Clark N. Taylor, Robert C. Leishman. Robust Incremental State Estimation Through Covariance Adaptation. IEEE Robotics and Automation Letters. 2020; 5 (2):3737-3744.

Chicago/Turabian Style

Ryan M. Watson; Jason N. Gross; Clark N. Taylor; Robert C. Leishman. 2020. "Robust Incremental State Estimation Through Covariance Adaptation." IEEE Robotics and Automation Letters 5, no. 2: 3737-3744.

Dissertation
Published: 02 October 2019
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ACS Style

Jason Gross. Sensor Fusion Based Fault-Tolerant Attitude Estimation Solutions for Small Unmanned Aerial Vehicles. 2019, 1 .

AMA Style

Jason Gross. Sensor Fusion Based Fault-Tolerant Attitude Estimation Solutions for Small Unmanned Aerial Vehicles. . 2019; ():1.

Chicago/Turabian Style

Jason Gross. 2019. "Sensor Fusion Based Fault-Tolerant Attitude Estimation Solutions for Small Unmanned Aerial Vehicles." , no. : 1.

Journal article
Published: 12 September 2019 in IEEE Transactions on Aerospace and Electronic Systems
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Accurate platform localization is an integral component of most robotic systems. As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and non-cooperative environments. When dealing with novel and non-cooperative environments, little is known a priori about the measurement error uncertainty, thus, there is a requirement that the uncertainty models of the localization algorithm be adaptive. Within this paper, we propose the batch covariance estimation technique, which enables robust state estimation through the iterative adaptation of the measurement uncertainty model. The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model. The provided Gaussian mixture model can be utilized within any non-linear least squares optimization algorithm by approximately characterizing each observation with the sufficient statistics of the assigned cluster (i.e., each observation's uncertainty model is updated based upon the assignment provided by the nonparametric clustering algorithm). The proposed algorithm is verified on several GNSS collected data sets, where it is shown that the proposed technique exhibits some advantages when compared to other robust estimation techniques when confronted with degraded data quality.

ACS Style

Ryan M. Watson; Jason N. Gross; Clark N. Taylor; Robert C. Leishman. Enabling Robust State Estimation Through Measurement Error Covariance Adaptation. IEEE Transactions on Aerospace and Electronic Systems 2019, 56, 2026 -2040.

AMA Style

Ryan M. Watson, Jason N. Gross, Clark N. Taylor, Robert C. Leishman. Enabling Robust State Estimation Through Measurement Error Covariance Adaptation. IEEE Transactions on Aerospace and Electronic Systems. 2019; 56 (3):2026-2040.

Chicago/Turabian Style

Ryan M. Watson; Jason N. Gross; Clark N. Taylor; Robert C. Leishman. 2019. "Enabling Robust State Estimation Through Measurement Error Covariance Adaptation." IEEE Transactions on Aerospace and Electronic Systems 56, no. 3: 2026-2040.

Conference paper
Published: 26 October 2018 in Proceedings of the 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018)
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ACS Style

Ryan M. Watson; Clark N. Taylor; Robert C. Leishman; Jason N. Gross. Batch Measurement Error Covariance Estimation for Robust Localization. Proceedings of the 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018) 2018, 2429 -2439.

AMA Style

Ryan M. Watson, Clark N. Taylor, Robert C. Leishman, Jason N. Gross. Batch Measurement Error Covariance Estimation for Robust Localization. Proceedings of the 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018). 2018; ():2429-2439.

Chicago/Turabian Style

Ryan M. Watson; Clark N. Taylor; Robert C. Leishman; Jason N. Gross. 2018. "Batch Measurement Error Covariance Estimation for Robust Localization." Proceedings of the 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018) , no. : 2429-2439.

Journal article
Published: 18 June 2018 in IEEE Transactions on Aerospace and Electronic Systems
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We propose an extension to the so-called PD detector. The PD detector jointly monitors received power and correlation profile distortion to detect the presence of GNSS carry-off-type spoofing, jamming, or multipath. We show that classification performance can be significantly improved by replacing the PD detector's symmetric-difference-based distortion measurement with one based on the post-fit residuals of the maximum-likelihood estimate of a single-signal correlation function model. We call the improved technique the PD-ML detector. In direct comparison with the PD detector, the PD-ML detector exhibits improved classification accuracy when tested against an extensive library of recorded field data. In particular, it is (1) significantly more accurate at distinguishing a spoofing attack from a jamming attack, (2) better at distinguishing multipathafflicted data from interference-free data, and (3) less likely to issue a false alarm by classifying multipath as spoofing. The PDML detector achieves this improved performance at the expense of additional computational complexity.

ACS Style

Jason N. Gross; Cagri Kilic; Todd E. Humphreys. Maximum-Likelihood Power-Distortion Monitoring for GNSS-Signal Authentication. IEEE Transactions on Aerospace and Electronic Systems 2018, 55, 469 -475.

AMA Style

Jason N. Gross, Cagri Kilic, Todd E. Humphreys. Maximum-Likelihood Power-Distortion Monitoring for GNSS-Signal Authentication. IEEE Transactions on Aerospace and Electronic Systems. 2018; 55 (1):469-475.

Chicago/Turabian Style

Jason N. Gross; Cagri Kilic; Todd E. Humphreys. 2018. "Maximum-Likelihood Power-Distortion Monitoring for GNSS-Signal Authentication." IEEE Transactions on Aerospace and Electronic Systems 55, no. 1: 469-475.

Preprint
Published: 11 April 2018
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Estimation techniques to precisely localize a kinematic platform with GNSS observables can be broadly partitioned into two categories: differential, or undifferenced. The differential techniques (e.g., real-time kinematic (RTK)) have several attractive properties, such as correlated error mitigation and fast convergence; however, to support a differential processing scheme, an infrastructure of reference stations within a proximity of the platform must be in place to construct observation corrections. This infrastructure requirement makes differential processing techniques infeasible in many locations. To mitigate the need for additional receivers within proximity of the platform, the precise point positioning (PPP) method utilizes accurate orbit and clock models to localize the platform. The autonomy of PPP from local reference stations make it an attractive processing scheme for several applications; however, a current disadvantage of PPP is the slow positioning convergence when compared to differential techniques. In this paper, we evaluate the convergence properties of PPP with an incremental graph optimization scheme (Incremental Smoothing and Mapping (iSAM2)), which allows for real-time filtering and smoothing. The characterization is first conducted through a Monte Carlo analysis within a simulation environment, which allows for the variations of parameters, such as atmospheric conditions, satellite geometry, and intensity of multipath. Then, an example collected data set is utilized to validate the trends presented in the simulation study.

ACS Style

Ryan M. Watson; Jason N. Gross. Evaluation of Kinematic Precise Point Positioning Convergence with an Incremental Graph Optimizer. 2018, 1 .

AMA Style

Ryan M. Watson, Jason N. Gross. Evaluation of Kinematic Precise Point Positioning Convergence with an Incremental Graph Optimizer. . 2018; ():1.

Chicago/Turabian Style

Ryan M. Watson; Jason N. Gross. 2018. "Evaluation of Kinematic Precise Point Positioning Convergence with an Incremental Graph Optimizer." , no. : 1.

Proceedings article
Published: 01 April 2018 in 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)
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Estimation techniques to precisely localize a kinematic platform with GNSS observables can be broadly partitioned into two categories: differential, or undifferenced. The differential techniques (e.g., real-time kinematic (RTK)) have several attractive properties, such as correlated error mitigation and fast convergence; however, to support a differential processing scheme, an infrastructure of reference stations within a proximity of the platform must be in place to construct observation corrections. This infrastructure requirement makes differential processing techniques infeasible in many locations. To mitigate the need for additional receivers within proximity of the platform, the precise point positioning (PPP) method utilizes accurate orbit and clock models to localize the platform. The autonomy of PPP from local reference stations make it an attractive processing scheme for several applications; however, a current disadvantage of PPP is the slow positioning convergence when compared to differential techniques. In this paper, we evaluate the convergence properties of PPP with an incremental graph optimization scheme (Incremental Smoothing and Mapping (iSAM2)), which allows for real-time filtering and smoothing. The characterization is first conducted through a Monte Carlo analysis within a simulation environment, which allows for the variations of parameters, such as atmospheric conditions, satellite geometry, and intensity of multipath. Then, an example collected data set is utilized to validate the trends presented in the simulation study.

ACS Style

Ryan M. Watson; Jason N. Gross. Evaluation of kinematic precise point positioning convergence with an incremental graph optimizer. 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) 2018, 589 -596.

AMA Style

Ryan M. Watson, Jason N. Gross. Evaluation of kinematic precise point positioning convergence with an incremental graph optimizer. 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS). 2018; ():589-596.

Chicago/Turabian Style

Ryan M. Watson; Jason N. Gross. 2018. "Evaluation of kinematic precise point positioning convergence with an incremental graph optimizer." 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) , no. : 589-596.

Journal article
Published: 27 March 2018 in IEEE Robotics & Automation Magazine
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A fundamental aspect of biological intelligence, from microbes to megafauna, is the ability to forage for the provisions required to sustain life. This sometimes underrated ability to seek out, identify, and use objects of interest in an environment with limited prior knowledge is an important capability that is also needed by robots. Many robotics applications can be modeled as foraging problems, such as search and rescue, wildlife tracking, crop pollination and harvesting, mining and in-situ-resource utilization, and scientific data/sample collection.

ACS Style

Yu Gu; Jared Strader; Nicholas Ohi; Scott Harper; Kyle Lassak; Chizhao Yang; Lisa Kogan; Boyi Hu; Matthew Gramlich; Rahul Kavi; Jason Gross. Robot Foraging: Autonomous Sample Return in a Large Outdoor Environment. IEEE Robotics & Automation Magazine 2018, 25, 93 -101.

AMA Style

Yu Gu, Jared Strader, Nicholas Ohi, Scott Harper, Kyle Lassak, Chizhao Yang, Lisa Kogan, Boyi Hu, Matthew Gramlich, Rahul Kavi, Jason Gross. Robot Foraging: Autonomous Sample Return in a Large Outdoor Environment. IEEE Robotics & Automation Magazine. 2018; 25 (3):93-101.

Chicago/Turabian Style

Yu Gu; Jared Strader; Nicholas Ohi; Scott Harper; Kyle Lassak; Chizhao Yang; Lisa Kogan; Boyi Hu; Matthew Gramlich; Rahul Kavi; Jason Gross. 2018. "Robot Foraging: Autonomous Sample Return in a Large Outdoor Environment." IEEE Robotics & Automation Magazine 25, no. 3: 93-101.

Research article
Published: 16 January 2018 in International Journal of Aerospace Engineering
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We present various performance trades for multiantenna global navigation satellite system (GNSS) multisensor attitude estimation systems. In particular, attitude estimation performance sensitivity to various error sources and system configurations is assessed. This study is motivated by the need for system designers, scientists, and engineers of airborne astronomical and remote sensing platforms to better determine which system configuration is most suitable for their specific application. In order to assess performance trade-offs, the attitude estimation performance of various approaches is tested using a simulation that is based on a stratospheric balloon platform. For GNSS errors, attention is focused on multipath, receiver measurement noise, and carrier-phase breaks. For the remaining attitude sensors, different performance grades of sensors are assessed. Through a Monte Carlo simulation, it is shown that, under typical conditions, sub-0.1-degree attitude accuracy is available when using multiple antenna GNSS data only, but that this accuracy can degrade to degree level in some environments warranting the inclusion of additional attitude sensors to maintain the desired level of accuracy. Further, we show that integrating inertial sensors is more valuable whenever accurate pitch and roll estimates are critical.

ACS Style

Nathan Tehrani; Jason N. Gross. Performance Trades for Multiantenna GNSS Multisensor Attitude Determination Systems. International Journal of Aerospace Engineering 2018, 2018, 1 -12.

AMA Style

Nathan Tehrani, Jason N. Gross. Performance Trades for Multiantenna GNSS Multisensor Attitude Determination Systems. International Journal of Aerospace Engineering. 2018; 2018 ():1-12.

Chicago/Turabian Style

Nathan Tehrani; Jason N. Gross. 2018. "Performance Trades for Multiantenna GNSS Multisensor Attitude Determination Systems." International Journal of Aerospace Engineering 2018, no. : 1-12.

Journal article
Published: 01 December 2017 in Aerospace Science and Technology
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ACS Style

Victor O. Sivaneri; Jason N. Gross. UGV-to-UAV cooperative ranging for robust navigation in GNSS-challenged environments. Aerospace Science and Technology 2017, 71, 245 -255.

AMA Style

Victor O. Sivaneri, Jason N. Gross. UGV-to-UAV cooperative ranging for robust navigation in GNSS-challenged environments. Aerospace Science and Technology. 2017; 71 ():245-255.

Chicago/Turabian Style

Victor O. Sivaneri; Jason N. Gross. 2017. "UGV-to-UAV cooperative ranging for robust navigation in GNSS-challenged environments." Aerospace Science and Technology 71, no. : 245-255.

Conference paper
Published: 03 November 2017 in Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017)
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ACS Style

Ryan M. Watson; Jason N. Gross. Robust Navigation In GNSS Degraded Environment Using Graph Optimization. Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017) 2017, 2906 -2918.

AMA Style

Ryan M. Watson, Jason N. Gross. Robust Navigation In GNSS Degraded Environment Using Graph Optimization. Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017). 2017; ():2906-2918.

Chicago/Turabian Style

Ryan M. Watson; Jason N. Gross. 2017. "Robust Navigation In GNSS Degraded Environment Using Graph Optimization." Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017) , no. : 2906-2918.

Journal article
Published: 25 October 2017 in IEEE Transactions on Aerospace and Electronic Systems
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We propose a simple low-cost technique that enables civil Global Positioning System (GPS) receivers and other civil global navigation satellite system (GNSS) receivers to reliably detect carry-off spoofing and jamming. The technique, which we call the Power-Distortion detector, classifies received signals as interference-free, multipath-afflicted, spoofed, or jammed according to observations of received power and correlation function distortion. It does not depend on external hardware or a network connection and can be readily implemented on many receivers via a firmware update. Crucially, the detector can with high probability distinguish low-power spoofing from ordinary multipath. In testing against over 25 high-quality empirical data sets yielding over 900,000 separate detection tests, the detector correctly alarms on all malicious spoofing or jamming attacks while maintaining a < 0.5% single-channel false alarm rate

ACS Style

Kyle D. Wesson; Jason N. Gross; Todd E. Humphreys; Brian L. Evans. GNSS Signal Authentication Via Power and Distortion Monitoring. IEEE Transactions on Aerospace and Electronic Systems 2017, 54, 739 -754.

AMA Style

Kyle D. Wesson, Jason N. Gross, Todd E. Humphreys, Brian L. Evans. GNSS Signal Authentication Via Power and Distortion Monitoring. IEEE Transactions on Aerospace and Electronic Systems. 2017; 54 (2):739-754.

Chicago/Turabian Style

Kyle D. Wesson; Jason N. Gross; Todd E. Humphreys; Brian L. Evans. 2017. "GNSS Signal Authentication Via Power and Distortion Monitoring." IEEE Transactions on Aerospace and Electronic Systems 54, no. 2: 739-754.

Journal article
Published: 16 October 2017 in IEEE Aerospace and Electronic Systems Magazine
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Airborne geodetic techniques are superior to their terrestrial counterparts with respect to both economy and efficiency [1]. In addition, airborne geodesy allows mapping of remote areas that would otherwise be inaccessible. A cornerstone for most airborne geodetic measurements is the accurate determination of the aircraft position and orientation. Therefore, airborne geodesy was not widely used until the advent of global navigation satellite systems (GNSSs). Now, with precise GNSS positioning techniques, airborne geodesy is booming within several domains, including solid Earth monitoring (e.g., crustal deformation) [2]–[4], fluid Earth monitoring (e.g., ice sheet or sea-level monitoring) [5]–[7], and geoid determination [8], [9]. Despite the success of these airborne geodetic methods, the increased availability and reliability of accurate aircraft positioning remains an important enabling technology in support of future scientific endeavors.

ACS Style

Ryan M. Watson; Jason N. Gross; Yoaz Bar-Sever; William I. Bertiger; Bruce J. Haines. Flight data assessment of tightly coupled PPP/INS using real-time products. IEEE Aerospace and Electronic Systems Magazine 2017, 32, 10 -21.

AMA Style

Ryan M. Watson, Jason N. Gross, Yoaz Bar-Sever, William I. Bertiger, Bruce J. Haines. Flight data assessment of tightly coupled PPP/INS using real-time products. IEEE Aerospace and Electronic Systems Magazine. 2017; 32 (8):10-21.

Chicago/Turabian Style

Ryan M. Watson; Jason N. Gross; Yoaz Bar-Sever; William I. Bertiger; Bruce J. Haines. 2017. "Flight data assessment of tightly coupled PPP/INS using real-time products." IEEE Aerospace and Electronic Systems Magazine 32, no. 8: 10-21.

Article
Published: 11 July 2017 in Journal of Field Robotics
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This paper presents the design of Cataglyphis, a research rover that won the NASA Sample Return Robot Centennial Challenge in 2015. During the challenge, Cataglyphis was the only robot that was able to autonomously find, retrieve, and return multiple types of samples in a large natural environment without using Earth-specific sensors such as GPS and magnetic compasses. It navigates through a fusion of measurements collected from inertial sensors, wheel encoders, a nodding Lidar, a set of ranging radios, a camera on a panning platform, and a sun sensor. In addition to visual detection of a homing beacon, computer vision algorithms provide the sample detection, identification, and localization capabilities, with low false positive and false negative rates demonstrated during the competition. The mission planning and control software enables robot behaviors, determines sequences of actions, and helps the robot to recover from various failure conditions. A compliant, under-actuated manipulator conforms to the natural terrain before picking up samples of various size, weight, and shape.

ACS Style

Yu Gu; Nicholas Ohi; Kyle Lassak; Jared Strader; Lisa Kogan; Alexander Hypes; Scott Harper; Boyi Hu; Matthew Gramlich; Rahul Kavi; Ryan Watson; Marvin Cheng; Jason Gross. Cataglyphis: An autonomous sample return rover. Journal of Field Robotics 2017, 35, 248 -274.

AMA Style

Yu Gu, Nicholas Ohi, Kyle Lassak, Jared Strader, Lisa Kogan, Alexander Hypes, Scott Harper, Boyi Hu, Matthew Gramlich, Rahul Kavi, Ryan Watson, Marvin Cheng, Jason Gross. Cataglyphis: An autonomous sample return rover. Journal of Field Robotics. 2017; 35 (2):248-274.

Chicago/Turabian Style

Yu Gu; Nicholas Ohi; Kyle Lassak; Jared Strader; Lisa Kogan; Alexander Hypes; Scott Harper; Boyi Hu; Matthew Gramlich; Rahul Kavi; Ryan Watson; Marvin Cheng; Jason Gross. 2017. "Cataglyphis: An autonomous sample return rover." Journal of Field Robotics 35, no. 2: 248-274.

Conference paper
Published: 02 June 2017 in AIAA Modeling and Simulation Technologies Conference
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ACS Style

Matthew B. Rhudy; Jason N. Gross; Yu Gu. Determination of Stochastic Wind Speed Model Parameters Using Allan Deviation Approach. AIAA Modeling and Simulation Technologies Conference 2017, 1 .

AMA Style

Matthew B. Rhudy, Jason N. Gross, Yu Gu. Determination of Stochastic Wind Speed Model Parameters Using Allan Deviation Approach. AIAA Modeling and Simulation Technologies Conference. 2017; ():1.

Chicago/Turabian Style

Matthew B. Rhudy; Jason N. Gross; Yu Gu. 2017. "Determination of Stochastic Wind Speed Model Parameters Using Allan Deviation Approach." AIAA Modeling and Simulation Technologies Conference , no. : 1.

Conference paper
Published: 07 March 2017 in Proceedings of the 2017 International Technical Meeting of The Institute of Navigation
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ACS Style

Jason N. Gross; Todd E. Humphreys. GNSS Spoofing, Jamming, and Multipath Interference Classification using a Maximum-Likelihood Multi-Tap Multipath Estimator. Proceedings of the 2017 International Technical Meeting of The Institute of Navigation 2017, 662 -670.

AMA Style

Jason N. Gross, Todd E. Humphreys. GNSS Spoofing, Jamming, and Multipath Interference Classification using a Maximum-Likelihood Multi-Tap Multipath Estimator. Proceedings of the 2017 International Technical Meeting of The Institute of Navigation. 2017; ():662-670.

Chicago/Turabian Style

Jason N. Gross; Todd E. Humphreys. 2017. "GNSS Spoofing, Jamming, and Multipath Interference Classification using a Maximum-Likelihood Multi-Tap Multipath Estimator." Proceedings of the 2017 International Technical Meeting of The Institute of Navigation , no. : 662-670.

Preprint
Published: 21 February 2017
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We presented the PD detector, a novel low-cost, receiver-autonomous, readily-implementable GNSS jamming and carry-off spoofing detector. The detector traps a would-be attacker between simultaneous monitoring of received power and complex correlation function distortion. It amounts to a multi-hypothesis Bayesian classifier applied to a problem with three unknown parameters whose prior distributions are informed by the physics of GNSS signal reception and signal processing, and whose prior probabilities can be adjusted to reflect the threat environment in which a receiver operates. In evaluation against 27 high-quality experimental recordings of attack and non-attack scenarios, the detector correctly alarmed on all malicious attacks while maintaining a single-channel false alarm rate below 0.5\%. For convenient implementation, the PD detector's decision rule for three different cost functions, together with all code required to generate application-tailored decision rules, is available at https://github.com/navSecurity/P-D-defense.

ACS Style

Kyle D. Wesson; Jason N. Gross; Todd E. Humphreys; Brian L. Evans. GNSS Signal Authentication via Power and Distortion Monitoring. 2017, 1 .

AMA Style

Kyle D. Wesson, Jason N. Gross, Todd E. Humphreys, Brian L. Evans. GNSS Signal Authentication via Power and Distortion Monitoring. . 2017; ():1.

Chicago/Turabian Style

Kyle D. Wesson; Jason N. Gross; Todd E. Humphreys; Brian L. Evans. 2017. "GNSS Signal Authentication via Power and Distortion Monitoring." , no. : 1.