Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table

Development of an evaluation technique for strapdown guidance systems Interim report, 1 Feb. 1968 - 1 Feb. 1969

Evaluation techniques for astrionics systems using aided strapdown inertial guidanc

Fusion of Imaging and Inertial Sensors for Navigation

The motivation of this research is to address the limitations of satellite-based navigation by fusing imaging and inertial systems. The research begins by rigorously describing the imaging and navigation problem and developing practical models of the sensors, then presenting a transformation technique to detect features within an image. Given a set of features, a statistical feature projection technique is developed which utilizes inertial measurements to predict vectors in the feature space between images. This coupling of the imaging and inertial sensors at a deep level is then used to aid the statistical feature matching function. The feature matches and inertial measurements are then used to estimate the navigation trajectory using an extended Kalman filter. After accomplishing a proper calibration, the image-aided inertial navigation algorithm is then tested using a combination of simulation and ground tests using both tactical and consumer- grade inertial sensors. While limitations of the Kalman filter are identified, the experimental results demonstrate a navigation performance improvement of at least two orders of magnitude over the respective inertial-only solutions

Sensitivity Analyses of Optimized Attitude Estimators Using Sensor Fusion Solutions for Low-Cost MEMS Configurations

Since the 1990’s, there has been increased focus on creating navigation systems for small unmanned systems, particularly small unmanned aerial systems (SUAS). Due to size, weight, and cost restrictions, compared to larger more costly manned systems, navigation systems for SUAS have evolved to be quite different from the proven systems of the past. Today, there are many solutions for the problem of navigation for SUAS. The problem has now become choosing the most fitting navigation solution for a given application/mission. This is particularly true for evaluating solutions that are fundamentally different. This research analyses the performance and sensitivity of four sensor fusion solutions for attitude estimation under multiple simulated flight conditions. There are three different hardware configurations between the four estimators. For this reason, each estimator is tuned to be experimentally optimal, as to provide a fair comparison between different estimators. With each estimator tuned to its highest performance, the estimators are compared based on their sensitivity to tuning error, sensor bias, and estimator initialization error. Finally the estimators\u27 accuracy performances are directly compared. This thesis also provides methods to tune different configuration estimators to their individual best performances. These methods show that choosing tuning parameters based on sensor noise covariance, as is typically done in research, does not produce optimal performance for all estimator formulations. After comparing multiple sensitivity and performance properties of the estimators, observations are provided regarding the efficacy of the analyses, including the applicability of the metrics used to determine performance. Some metrics where shown to be misleading for particular estimators or analyses. Ultimately, guidance is given for choosing performance metrics capable of comparing different solutions

Tightly Integrating Optical and Inertial Sensors for Navigation Using the UKF

The motivation of this research is to address the benefits of tightly integrating optical and inertial sensors where GNSS signals are not available. The research begins with describing the navigation problem. Then, error and measurement models are presented. Given a set of features, a feature detection and projection algorithm is developed which utilizes inertial measurements to predict vectors in the feature space between images. The unscented Kalman filter is applied to the navigation system using the inertial measurements and feature matches to estimate the navigation trajectory. Finally, the image-aided navigation algorithm is tested using a simulation and an experiment. As a result, the optical measurements combined with the inertial sensors result in improved performance for non-GNSS based navigation

Propagation of uncertainty through coning, sculling, and scrolling corrections for inertial navigation

This thesis investigates the propagation of estimation errors through generalized coning, sculling, and scrolling algorithms used in modern day inertial navigation systems, in order to accurately quantify the uncertainty in the estimation of position, velocity, and attitude. The corrections for coning, sculling, and scrolling algorithms have an often unaccounted for effect on documented and empirically derived error statistics for measurements used to predict the uncertainty in a vehicle\u27s position, velocity, and attitude estimates. Through the development of an error analysis for these generalized algorithms, mappings of the measurement and estimation errors through the correction termare generated. Using the developed mappings, an efficient and consistent propagation of state uncertainty with the multiplicative extended Kalman filter is achieved. Asimulation environment is developed to investigate the performance of the algorithms within a descent-to-landing scenario. Monte Carlo analysis is used to analyze the effects of the developed error propagation and the accompanying algorithms to compare them with commonly used discrete dead-reckoning approaches --Abstract, page iii

Multihop Rendezvous Algorithm for Frequency Hopping Cognitive Radio Networks

Cognitive radios allow the possibility of increasing utilization of the wireless spectrum, but because of their dynamic access nature require new techniques for establishing and joining networks, these are known as rendezvous. Existing rendezvous algorithms assume that rendezvous can be completed in a single round or hop of time. However, cognitive radio networks utilizing frequency hopping that is too fast for synchronization packets to be exchanged in a single hop require a rendezvous algorithm that supports multiple hop rendezvous. We propose the Multiple Hop (MH) rendezvous algorithm based on a pre-shared sequence of random numbers, bounded timing differences, and similar channel lists to successfully match a percentage of hops. It is tested in simulation against other well known rendezvous algorithms and implemented in GNU Radio for the HackRF One. We found from the results of our simulation testing that at 100 hops per second the MH algorithm is faster than other tested algorithms at 50 or more channels with timing ±50 milliseconds, at 250 or more channels with timing ±500 milliseconds, and at 2000 channels with timing ±5000 milliseconds. In an asymmetric environment with 100 hops per second, a 500 millisecond timing difference, and 1000 channels the MH algorithm was faster than other tested algorithms as long as the channel overlap was 35% or higher for a 50% required packet success to complete rendezvous. We recommend the Multihop algorithm for use cases with a fast frequency hop rate and a slow data transmission rate requiring multiple hops to rendezvous or use cases where the channel count equals or exceeds 250 channels, as long as timing data is available and all of the radios to be connected to the network can be pre-loaded with a shared seed

Image Processing Based Horizon Sensor for Estimating the Orientation of Sounding Rockets, Launch Vehicles and Spacecraft

The paper describes how the attitude of a sounding rocket, launch vehicle or satellite with respect to the Earth can be estimated from camera images of the Earth horizon. Details about detecting the horizon in the camera image, fitting hyperbolae or ellipses to the detected horizon curve and deriving the Earth nadir vector and the corresponding error covariance from the fitted conic section are given. The presented method works at low heights, where the projected horizon mostly appears to be hyperbolic, as well as at large heights, where the projected horizon mostly appears to be elliptic and it is irrelevant if the Earth is fully or only partially in the field-of-view of the camera. In fact, the method can be universally used to estimate the direction vectors and attitude with respect to any spherical celestial body such as the Sun or Moon. Using the example of a sounding rocket mission with two cameras aboard, it is illustrated how the estimates of the Earth nadir and the Sun direction vectors are fused with the measurements of a strapdown inertial measurement unit and a GPS receiver to obtain an accurate and continuous estimate of the three-dimensional orientation of the sounding rocket with respect to the Earth

Satellite-Based Fusion of Image/Inertial Sensors for Precise Geolocation

The ability to produce high-resolution images of the Earth’s surface from space has flourished in recent years with the continuous development and improvement of satellite-based imaging sensors. Earth-imaging satellites often rely on complex onboard navigation systems, with dependence on Global Positioning System (GPS) tracking and/or continuous post-capture georegistration, to accurately geolocate ground targets of interest to either commercial and military customers. Consequently, these satellite systems are often massive, expensive, and susceptible to poor or unavailable target tracking capabilities in GPS-denied environments. Previous research has demonstrated that a tightly-coupled image-aided inertial navigation system (INS), using existing onboard imaging sensors, can provide significant target tracking improvement over that of conventional navigation and tracking systems. Satellite-based image-aided navigation is explored as a means of autonomously tracking stationary ground targets by implementing feature detection and recognition algorithms to accurately predict a ground target’s pixel location within subsequent satellite images. The development of a robust satellite-based image-aided INS model offers a convenient, low-cost, low-weight and highly accurate solution to the geolocation precision problem, without the need of human interaction or GPS dependency, while simultaneously providing redundant and sustainable satellite navigation capabilities