GPS-Denied Navigation Using Synthetic Aperture Radar

In most modern navigation systems, GPS is used to determine the precise location of the vehicle; however, GPS signals can easily be blocked, jammed, or spoofed. These signals can be blocked by canyons or tall buildings. Additionally, adversaries can transmit signals that either make GPS signals difficult to interpret or that imitate real GPS signals and cause a navigation system to think it is somewhere other than its true location. GPS-denied (GPS-D) navigation is the process of navigating in the absence of GPS. Many methods of performing GPS-D navigation have been proposed and explored. One such method is to use synthetic aperture radar (SAR) to provide information lost in the absence of GPS. SAR is a technique that uses radar to form images. To create high-quality SAR images, precise location information must be used. This thesis explores using the quality of SAR images to improve position accuracy. First, a method of measuring the quality of a SAR image is determined and tested. Next, a GPS-D algorithm is developed that uses this measure of SAR image quality. The algorithm is then tested on multiple sets of SAR data. The results show that the algorithm performs variably depending on the data set and the parameters of the algorithm

Self-Describing Fiducials for GPS-Denied Navigation of Unmanned Aerial Vehicles

Accurate estimation of an Unmanned Aerial Vehicle’s (UAV’s) location is critical for the operation of the UAV when it is controlled completely by its onboard processor. This can be particularly challenging in environments in which GPS is not available (GPS-denied). Many of the options previously explored for estimation of a UAV’s location without the use of GPS require more sophisticated processors than can feasibly be mounted on a UAV because of weight, size, and power restrictions. Many options are also aimed at indoor operation without the range capabilities to scale to outdoor operations. This research explores an alternative method of GPS-denied navigation which utilizes line-of-sight measurements to self-describing fiducials to aid in position determination. Each self-describing fiducial is an easily identifiable object fixed at a specific location. Each fiducial relays data containing its specific location to the observing UAV. The UAV can measure its relative position to the fiducial using camera images. This measurement can be combined with measurements from an Inertial Measurement Unit (IMU) to obtain a more accurate estimate of the UAV’s location. In this research, a simulation is used to validate and assess the performance of algorithms used to estimate the UAV’s position using these measurements. This research analyzes the effectiveness of the estimation algorithm when used with various IMUs and fiducial spacings. The effect of how quickly camera images of fiducials can be captured and processed is also analyzed. Preparations for demonstrating this system with hardware are then presented and discussed, including options for fiducial type and a way to measure the true position of the UAV. The results from the simulated scenarios and the hardware demonstration preparation are analyzed, and future work is discussed

Sensitivity Study for UAV GPS-Denied Navigation in Uncertain Landmark Fields

This document provides two 2D simulation sensitivity analyses regarding a drone’s flight characteristic (state) errors within a GPS-denied region. The research focuses on a development and investigation of utilizing a camera to simultaneously determine a drone’s state while locating landmarks, where there is uncertainty in the landmarks’ exact positions prior to the mission (SLAM). This SLAM method is performed in regions with limited access to GPS. Furthermore, there is development and investigation of controlling the drone in conjunction with SLAM using potential error-reducing control parameters. Objectives are to quantitatively understand the UAV’s sensitivity of position errors to sensor grade and landmark characteristics as well as sensitivity of position errors to tuned control parameters

GPS-Denied Navigation Using Synthetic Aperture Radar Images and Neural Networks

Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) system. This is accomplished by comparing an online-generated SAR image with a reference image obtained a priori. The distortions relative to the reference image are learned and exploited with a convolutional neural network to recover the initial navigational errors, which can be used to recover the true flight trajectory throughout the synthetic aperture. The proposed neural network approach is able to learn to predict the initial errors on both simulated and real SAR image data

A low-cost vision-based unmanned aerial system for extremely low-light GPS-denied navigation and thermal imaging

A Low-Cost Vision-Based Unmanned Aerial System for Extremely Low-Light GPS-Denied Navigation and Thermal Imaging}, abstract = {This paper presents the design and implementation details of a complete unmanned aerial system (UAS) based on commercial-off-the-shelf (COTS) components, focusing on safety, security, search and rescue scenarios in GPS-denied environments. In particular, the aerial platform is capable of semi-autonomously navigating through extremely low-light, GPS-denied indoor environments based on onboard sensors only, including a downward-facing optical flow camera. Besides, an additional low-cost payload camera system is developed to stream both infrared video and visible light video to a ground station in real-time, for the purpose of detecting sign of life and hidden humans. The total cost of the complete system is estimated to be $1150, and the effectiveness of the system has been tested and validated in practical scenarios

Design, Development and Implementation of Intelligent Algorithms to Increase Autonomy of Quadrotor Unmanned Missions

This thesis presents the development and implementation of intelligent algorithms to increase autonomy of unmanned missions for quadrotor type UAVs. A six-degree-of freedom dynamic model of a quadrotor is developed in Matlab/Simulink in order to support the design of control algorithms previous to real-time implementation. A dynamic inversion based control architecture is developed to minimize nonlinearities and improve robustness when the system is driven outside bounds of nominal design. The design and the implementation of the control laws are described. An immunity-based architecture is introduced for monitoring quadrotor health and its capabilities for detecting abnormal conditions are successfully demonstrated through flight testing. A vision-based navigation scheme is developed to enhance the quadrotor autonomy under GPS denied environments. An optical flow sensor and a laser range finder are used within an Extended Kalman Filter for position estimation and its estimation performance is analyzed by comparing against measurements from a GPS module. Flight testing results are presented where the performances are analyzed, showing a substantial increase of controllability and tracking when the developed algorithms are used under dynamically changing environments. Healthy flights, flights with failures, flight with GPS-denied navigation and post-failure recovery are presented

Rigid Body Motion Estimation based on the Lagrange-d'Alembert Principle

Stable estimation of rigid body pose and velocities from noisy measurements, without any knowledge of the dynamics model, is treated using the Lagrange-d'Alembert principle from variational mechanics. With body-fixed optical and inertial sensor measurements, a Lagrangian is obtained as the difference between a kinetic energy-like term that is quadratic in velocity estimation error and the sum of two artificial potential functions; one obtained from a generalization of Wahba's function for attitude estimation and another which is quadratic in the position estimate error. An additional dissipation term that is linear in the velocity estimation error is introduced, and the Lagrange-d'Alembert principle is applied to the Lagrangian with this dissipation. This estimation scheme is discretized using discrete variational mechanics. The presented pose estimator requires optical measurements of at least three inertially fixed landmarks or beacons in order to estimate instantaneous pose. The discrete estimation scheme can also estimate velocities from such optical measurements. In the presence of bounded measurement noise in the vector measurements, numerical simulations show that the estimated states converge to a bounded neighborhood of the actual states.Comment: My earlier submitted manuscript (arXiv:1508.07671), is an extended version of this work, containing detailed proofs and more elaborated numerical simulations, currently under review in Automatica. This paper will be cited in the extended journal version (arXiv:1508.07671) upon publicatio

Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion

Navigation in Global Positioning Systems (GPS)-denied environments requires robust estimators reliant on fusion of inertial sensors able to estimate rigid-body's orientation, position, and linear velocity. Ultra-wideband (UWB) and Inertial Measurement Unit (IMU) represent low-cost measurement technology that can be utilized for successful Inertial Navigation. This paper presents a nonlinear deterministic navigation observer in a continuous form that directly employs UWB and IMU measurements. The estimator is developed on the extended Special Euclidean Group $\mathbb{SE}_{2}\left(3\right)$ and ensures exponential convergence of the closed loop error signals starting from almost any initial condition. The discrete version of the proposed observer is tested using a publicly available real-world dataset of a drone flight. Keywords: Ultra-wideband, Inertial measurement unit, Sensor Fusion, Positioning system, GPS-denied navigation.Comment: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS