Navigation is a term with broad usage in everyday life and can mean the act of moving, moving through an unknown environment, ascertaining a craft's position, or even browsing on the Internet. Originally, the term meant the skill of sailing a ship, in particular steering and maneuvering, and is derived from the Latin words navis and agere, meaning "ship" and "to drive." The modern field of study of navigation in a scientific context usually relates to the ascertainment or determination of position, velocity, and orientation of some object relative to a reference point and is used for all type of crafts and vehicles, such as aircraft, ships, submarines, and cars.

In this article, we suggest that a strapdown inertial navigation system based on microelectromechanical system (MEMS) inertial sensors is a useful addition to a vessel with dynamic positioning (DP). We conduct full-scale experiments with MEMS inertial sensors on board a DP vessel operating off the Norwegian coast. The vessel operates in different scenarios, and the purpose is to showcase how low-cost MEMS sensors may complement or replace existing DP sensor systems. Employing nonlinear observers for estimating attitude, heave, velocity, and position, we go through the benefits and disadvantages, and some caveats, for the sensors and methods used in this article. Two different MEMS units are evaluated, aided by gyrocompasses and position reference systems. We evaluate the attitude, heave, and dead reckoning capabilities obtained with the presented estimators, in relation to relevant class notation, ultimately motivating the inclusion of new sensors and methods for dynamic positioning. The results related to attitude and heave are compared with data from well-proven industry standard vertical reference units while dead reckoning is evaluated with respect to the onboard position reference systems.

We present two alternative methods for fault detection and isolation (FDI) with redundant Microelectromechanical system (MEMS) inertial measurement units (IMUs) in inertial navigation systems (INS) based on nonlinear observers (NLOs). The first alternative is based on the parity space method, while the second is expanded with quaternion-based averaging and FDI. Both alternatives are implemented and validated using data gathered in a full-scale experiment on an offshore vessel. Data from three identical MEMS IMUs and the vessel's own industrial sensors are used to verify the methods' FDI capabilities. The results reveal that when it comes to FDI of the IMUs' angular rate sensors, there are differences between the two methods. The navigation algorithm based on quaternion weighting is essentially unaffected by the failure of an angular rate sensor, while the parity-space-method-based alternative experiences a perturbation.

This article considers the problem of constructing an observer for estimating position, velocity, attitude, underwater wave speed, rate sensor bias, and accelerometer bias that has both proven stability and close-to-optimal performance with respect to noise. The observer takes pseudorange, pseudorange difference, depth, and inertial measurements as input, and has a cascade structure for which the equilibrium point is proven to be locally exponentially stable due to the singularities in the attitude representation. The design of the observer is based on the exogenous Kalman filter principle, in which estimators with proven stability provide a linearization point for a linearized Kalman filter, to achieve both proven stability and close-to-optimal noise properties. Experimental validation is provided, with ground truth values generated by a camera positioning system with millimeter accuracy. The observer is compared to an extended Kalman filter and to a nonimplementable linearized Kalman filter using the true state as the linearization point, and the estimation error is almost identical to the linearized Kalman filter using the true state as a linearization point.

The introduction of direction finding in the Bluetooth standard enabled the use of antenna arrays for locating Bluetooth devices, using carrier phase measurements to estimate the direction from the array to a moving device. In this work, this feature is utilized for outdoor localization. We show how using repeated measurements from all array elements, instead of only the initial single-element reference samples as often suggested, can contribute to an improved estimate of the signal’s unknown carrier frequency offset, thereby improving the direction estimation performance. To run the direction-of-arrival estimation in real-time with high angular resolution on an embedded computer we propose a pseudo-spectrum peak search strategy that combines a coarse search, where the resolution is decided based on the array’s main lobe width, with a local nonlinear optimization for estimate refinement. We consider practical aspects relating to the phase sampling configuration and demonstrate direction estimation at up to 700m range with insignificant packet loss within 500m, and without significant loss of angular precision even when the received signal is near the receiver’s signal strength sensitivity threshold. In an open outdoor environment, using a square antenna array with 12 elements, the azimuthal performance is found to be very consistent with range, with noise standard deviation typically around 1°. While the elevation is significantly affected by multipath at lower elevation angles, with visible disagreement between frequency channels, it is shown to be consistent with simulations of ground reflection multipath.

The measurement quality of Global Navigation Satellites Systems (GNSS) during marine operations will vary over time. Inherently, GNSS quality changes should be handled when GNSS is utilized as aid in inertial navigation systems. In this paper we present an observer for estimating position, velocity and attitude with time-varying gains for high-performance sensor fusion based on GNSS quality and other quality indicators. The origin of the error dynamics is proven to be uniformly semiglobal exponentially stable. The concept is illustrated by simulating a vessel operating in dynamic positioning with GNSS and inertial sensors.