ION Webinars provide timely, engaging presentations on topics of interest to the PNT community.
GAO Report, “Defense Navigation Capabilities”
Co-hosted by the Resilient Navigation and Timing Foundation
Tuesday, June 15 at 11:00 a.m. EDT
Moderated by: Dana A. Goward, Resilient Navigation and Timing Foundation
Join the Institute of Navigation (ION) and the Resilient Navigation and Timing Foundation for a complimentary webinar featuring the recently published GAO Report “Defense Navigation Capabilities.”
On the 10 of May the General Accountability Office (GAO) released its report “Defense Navigation Capabilities: DOD is Developing Positioning, Navigation, and Timing Technologies to Complement GPS.” Among the report’s recommendations were that the Department of Defense consider using resilient technologies as the cornerstone of its PNT efforts going forward, instead of its current plan to build its plans around GPS.
The webinar will feature representatives from the Senate Armed Services Committee staff discussing the impetus for GAO’s efforts, and two of the principal authors of the report who will discuss their findings.
Factor Graph Optimization for GNSS/INS Integration: A Comparison with the Extended Kalman Filter
Monday, June 28 at 11:00 a.m. EDT
Presented by: Dr. Li-Ta Hsu, The Hong Kong Polytechnic University
Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS-INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real-time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO-based estimator into an “EKF-like estimator.” In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation; and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption.
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|May 11, 2021||Multi‐slices navigation approach for unknown 3D environments using micro aerial vehicles||Dr. Haytham Mohamed|
|April 20, 2021||GNSS interference mitigation: A measurement and position domain assessment||Dr. Daniele Borio and Dr. Ciro Gioia|
|March 23, 2021||Navigation of the Mars 2020 Perseverance Rover from Earth to Jezero Crater||Dr. Gerhard Kruizinga|
February 18, 2021
|Effect of GPS III weighted voting on P(Y) receiver processing performance||Dr. Karl Kovach|
December 10, 2020
|Automatic detection of ionospheric scintillation-like GNSS satellite oscillator anomaly using a machine-learning algorithm||Yunxiang (Leo) Liu and Dr. Y. Jade Morton|
|November 12, 2020||Residual-based multi-filter methodology for all-source fault detection, exclusion, and performance monitoring||Dr. Juan Jurado|
|October 22, 2020||Dr. Kevin Brink|
|August 20, 2020||Dr. Mathieu Joerger|
|July 9, 2020||Dr. Han Gao|
|June 25, 2020||Jonathan Turner, Alan Grant and Dana Goward|
|May 28, 2020||Impact of Sample Correlation on SISRE Overbound for ARAIM||Dr. Santiago Perea|
|April 16, 2020||Flight Results of GPS-Based Attitude Determination for the Canadian CASSIOPE Satellite||Dr. André Hauschild|
|March 5, 2020||Gaussian-Pareto Overbounding of DGNSS Pseudoranges from CORS||Dr. Jordan D. Larson|
|December 5, 2019||LTE Receiver Design and Multipath Analysis for Navigation in Urban Environments||Kimia Shamaei and Zak Kassas|