Search Results for author: Robert C. Leishman

Found 6 papers, 1 papers with code

Path Planning with Uncertainty for Aircraft Under Threat of Detection from Ground-Based Radar

no code implementations8 Jul 2022 Austin Costley, Greg Droge, Randall Christensen, Robert C. Leishman, James Swedeen

The error budgets identify the contribution of each source of uncertainty (e. g., sensor measurement noise, radar position uncertainty) to the overall variability in the probability of detection.

Position

Sensitivity of Single-Pulse Radar Detection to Radar State Uncertainty

no code implementations21 Mar 2022 Austin Costley, Randall Christensen, Robert C. Leishman, Greg Droge

This paper presents a method to incorporate the uncertainty of the radar state in a single-pulse radar detection model.

Position

Sensitivity of Single-Pulse Radar Detection to Aircraft Pose Uncertainties

no code implementations18 Jan 2022 Austin Costley, Randall Christensen, Greg Droge, Robert C. Leishman

This paper provides a method for combining aircraft pose uncertainty with single-pulse radar detection models to aid mission planning efforts.

Analytical Aircraft State and IMU Signal Generator from Smoothed Reference Trajectory

no code implementations25 May 2021 Austin Costley, Randall Christensen, Robert C. Leishman, Greg Droge

This work presents a method for generating position, attitude, and velocity states for an aircraft following a smoothed reference trajectory.

Position

Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems

no code implementations1 Jul 2020 Kyung Kim, Robert C. Leishman, Scott L. Nykl

Monocular visual navigation methods have seen significant advances in the last decade, recently producing several real-time solutions for autonomously navigating small unmanned aircraft systems without relying on GPS.

Monocular Visual Odometry Visual Navigation

URSA: A Neural Network for Unordered Point Clouds Using Constellations

1 code implementation14 Aug 2018 Mark B. Skouson, Brett J. Borghetti, Robert C. Leishman

This paper describes a neural network layer, named Ursa, that uses a constellation of points to learn classification information from point cloud data.

Classification General Classification +1

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