Absolute Positioning Using the Earth\u27s Magnetic Anomaly Field

Achieving worldwide alternatives to GPS is a challenging engineering problem. Current GPS alternatives often suffer from limitations such as where and when the systems can operate. Navigation using the Earth\u27s magnetic anomaly field, which is globally available at all times, shows promise to overcome many of these limitations. We present a navigation filter which uses the Earth\u27s magnetic anomaly field as a navigation signal to aid an inertial navigation system (INS) in an aircraft. The filter utilizes highly-accurate optically pumped cesium (OPC) magnetometers to make scalar measurements of the Earth\u27s magnetic field and compare them to a map using a marginalized particle filter approach. We demonstrate navigation accuracy of 13 meters DRMS with a high quality magnetic anomaly map at low altitudes with real flight data. We conduct a simulation over the continental United States to predict accuracies with respect to variables like location and altitude. Finally, we address the problem of map availability by presenting a method for a self-building magnetic anomaly model

MagSLAM: Aerial Simultaneous Localization and Mapping using Earth\u27s Magnetic Anomaly Field

Instances of spoofing and jamming of global navigation satellite systems (GNSSs) have emphasized the need for alternative navigation methods. Aerial navigation by magnetic map matching has been demonstrated as a viable GNSS‐alternative navigation technique. Flight test demonstrations have achieved accuracies of tens of meters over hour‐long flights, but these flights required accurate magnetic maps which are not always available. Magnetic map availability and resolution vary widely around the globe. Removing the dependency on prior survey maps extends the benefits of aerial magnetic navigation methods to small unmanned aerial systems (sUAS) at lower altitudes where magnetic maps are especially undersampled or unavailable. In this paper, a simultaneous localization and mapping (SLAM) algorithm known as FastSLAM was modified to use scalar magnetic measurements to constrain a drifting inertial navigation system (INS). The algorithm was then demonstrated on real magnetic navigation flight test data. Similar in performance to the map‐based approach, MagSLAM achieved tens of meters accuracy in a 100‐minute flight without the use of a prior magnetic map. Aerial SLAM using Earth\u27s magnetic anomaly field provides a GNSS‐alternative navigation method that is globally persistent, impervious to jamming or spoofing, stealthy, and locally accurate to tens of meters without the need for a magnetic map

Integration of Cold Atom Interferometry INS with Other Sensors

Inertial navigation systems (INS) using cold-atom interferometry are currently under development, and sensors in these systems are expected to be several orders of magnitude more ccurate than current navigation grade sensors. This signi cant increase in accuracy motivates the need to explore how these high accuracy inertial navigation systems can be integrated with other sensors. This research focuses on methods of integrating cold atom interferometry INS with conventional navigation grade INS, as well as with GPS. Results from a full 6 degree of freedom simulation show that integrating CAI INS with navigation grade INS is a successful way to address the dynamic performance limitations of a CAI INS. Results from a CAI INS-GPS simulation show that a CAI INS-GPS can keep near GPS level accuracy with outages as long as 1000 seconds, compared to 100 seconds with a navigation grade INS

An Analysis of the Benefits and Difficulties of Aerial Magnetic Vector Navigation

Recent successful flight tests have demonstrated scalar magnetic anomaly navigation to be a viable GPS-alternative navigation system. These flight tests matched magnetic field measurements to maps of the Earth\u27s crustal magnetic field in order to navigate. Scalar magnetic navigation uses only the magnetic field intensity, not direction, in order to navigate. While it appears obvious to extend aerial magnetic navigation to use the full vector field, in practice there are substantial obstacles to doing so. This article explores the key challenges of magnetic vector navigation including current sensor limitations, lack of high frequency magnetic vector maps of the Earth\u27s crust, and proper integration of the magnetic data with an inertial navigation system. In overcoming these challenges several key benefits of magnetic vector navigation over scalar magnetic navigation become apparent, including modestly improved navigation accuracy and greatly improved platform attitude

Terrain‐referenced Navigation Using a Steerable‐laser Measurement Sensor

Excerpt: The benefits of GNSS have created dependencies on navigation in modern day aviation systems. Many of these systems operate with no backup navigation source. This makes the capabilities supported by precise navigation vulnerable. This paper investigates a contemporary approach to terrain-referenced naviga- tion (TRN), used to preserve an aircraft’s navigation solution during periods of GNSS denial. © 2021 Institute of Navigation

Cooperative Navigation Using Pairwise Communication with Ranging and Magnetic Anomaly Measurements

The problem of cooperative localization for a small group of Unmanned Aerial Vehicles (UAVs) in a GNSS denied environment is addressed in this paper. The presented approach contains two sequential steps: first, an algorithm called cooperative ranging localization, formulated as an Extended Kalman Filter (EKF), estimates each UAV\u27s relative pose inside the group using inter-vehicle ranging measurements; second, an algorithm named cooperative magnetic localization, formulated as a particle filter, estimates each UAV\u27s global pose through matching the group\u27s magnetic anomaly measurements to a given magnetic anomaly map. In this study, each UAV is assumed to only perform a ranging measurement and data exchange with one other UAV at any point in time. A simulator is developed to evaluate the algorithms with magnetic anomaly maps acquired from airborne geophysical survey. The simulation results show that the average estimated position error of a group of 16 UAVs is approximately 20 meters after flying about 180 kilometers in 1 hour. Sensitivity analysis shows that the algorithms can tolerate large variations of velocity, yaw rate, and magnetic anomaly measurement noises. Additionally, the UAV group shows improved position estimation robustness with both high and low resolution maps as more UAVs are added into the group