The use of autonomous underwater vehicles (AUV) for the exploration and oceanology science has been a field of interest of several research centers around the world in the last decade. The inertial navigation system (INS) has commonly been used as the principal means of localization for autonomous underwater vehicles. The main drawback of using only the inertia navigation system is the escalating error in the estimated position and attitude due to the error in the output of the inertia measurement unit and its integration. Traditionally external sensors such as: GPS, acoustic transponders, Doppler velocity logs (DVL), or cameras have been used to aid the solution of the inertial navigator by constraining the errors in the estimated positions. These external sensors have several practical disadvantages, basically related to their reliance on external information. One source of information that can assist in the localization of the vehicle, without the need for extra additional external sensing, is the vehicle’, s dynamic model. The model of the vehicle is capable of representing the attitude of the system according to the control inputs and the external forces acting on it. In this work, we follow a model-aided inertial navigation system (MA-INS) for Remus 100 AUV based on a 6-DOF non-linear dynamic model. The proposed navigation system utilizes the knowledge of the device dynamics through an experimentally validated mathematical model. The results show that the proposed navigation system improves underwater navigation capabilities for the systems that lack conventional aiding equipment.

The flight of unmanned aerial vehicles is often associated with model uncertainties, measurement noises, and environmental disturbances such as wind gust. To mitigate these challenges, the accurate estimation of states is vital. Moreover, the wind model and its parameters should also be estimated and compensated during the flight. In this paper, a model-aided inertial navigation is implemented for this purpose. To investigate the performance of the model-aided inertial navigation, two different models including constant wind and “1-cosine” model are considered. The model-aided inertial navigation integrates the output from a dynamics model of unmanned aerial vehicle in the navigation system to simultaneously estimate the model of wind as well as the current states. The results show that the model-aided inertial navigation provides good performance and the wind model is properly estimated. Moreover, small estimation errors, obtained from the simulations, prove the good performance of this approach in estimation of states and wind model.

The main task of the study is to estimate the position error in an inertial navigation system by integrating it with the visual system. The case study is a spacecraft that must accurately measure its position relative to a predetermined landing point. The spacecraft is assumed to be augmented GNSS navigation. Therefore, when satellite signals are dropped out or when landing on a moving marine platform, the data of the vision navigation system replaces the information of the satellite navigation system and improves the accuracy of the spacecraft navigation system. An Extended Kalman filter has been used to integrate inertial and vision navigation system information. In addition, the output data of the vision system, in order to be used in the Kalman filter measurement equations, is first processed by the recursive least square filter. The relevant relations are given and based on the results of software simulation, the efficiency of the proposed method is shown.

New users such as pedestrians are added to navigation systems with developing lightweight, portable, low-cost technologies. The pedestrian navigation systems are currently applied in miscellaneous fields including medicine, sport, military services, animation, robotics, etc. This amount of use has attracted the attention of many scholars over the last few decades. In this paper, the paths of a firefighter, as a pedestrian, was estimated approximately by the help of an inertial measurement unit (IMU) and acceleration sensors. To reduce the measured errors and noises by the sensor, zero velocity update (ZUPT) method and Kalman filter are exploited in a pedestrian navigation system. Due to the fact that the error in blind navigation is divergent over time if the filter is not used, the use of conventional accelerometer sensors cannot produce a satisfactory result. using the combined module of an inertial measurement sensor that includes accelerometer and gyroscope, it is possible to track the person’ s position at any moment while the sensor is tracked on the shoe. The ability of ZUPT in navigation system has been discussed and interpreted by measuring a path using a sensor installed on a person’ s shoe and comparing the results with the desired predetermined path.

The Inertial navigation system is an ideal solution for motion detection with high accuracy with fast dynamics, but the precise location and status of the system output can be significantly reduced over time. On the other hand, global positioning system is able to determine its position with an average accuracy around the earth. But the GPS alone isn’t enough for navigation of orbital modules, because it doesn’t have situation of orbital modules. The integrated inertial navigation system with global positioning system is a low cost method of providing an accurate and reliable navigation system in the civilian and military aerospace applications. In this paper, using the extended Kalman filter, we design an algorithm to estimate error of sensors, navigation and GPS. This method can be widely used in the integrated navigation INS / GPS in aerospace applications and provides an accurate navigation.

The increase in capability and performance of digital cameras, processors and image processing algorithms has caused vision-aided navigation of aerial vehicles to be a hot research of interest. In order to determine pose parameters form vision-aided navigation methods, it is common to use automatic image registration using information of reference databases. However, solving registration issue in automatic navigating of aerial vehicles has been considered a complex manner. In this paper, a novel method for vision-aided navigation of aerial vehicles to increase reliability and accuracy of geo-referencing aerial image is proposed. To have robust evaluation, different aerial images with variety of conditions are utilized to assess this method. Obtained results show high performance of proposed method to solve issues related to automatic GEO-referencing of aerial images.

This paper deals with designing the navigation scheme of a gimballed inertial system. This design is introduced and proved in the form of two theorems. Most of the gimballed navigation schemes proposed in the literature have the drawback of estimating position rates for alignment commands. Not only the estimating position rates are the basic source of the position errors, but also, they make the alignment commands and their implementation more complicated. The major advantage of the proposed design is that it eliminates the errors resulting from the estimation of the longitude and latitude rates because the angular velocity commands of gyroscopes are proportional to accelerations’ integrals and independent of the system position. In this paper, the stabilized platform is modelled, the platform alignment procedure is determined, and the initial conditions of the navigation phase are calculated. The results of the navigation scheme are compared with the wander-azimuth scheme in four scenarios and the performance of the position-independent navigation scheme is evaluated in practical tests and its results are presented.

In this paper, the performance of a Ring Laser Gyro based inertial navigation is investigated. Dynamic and stochastic modelings are applied to gyro simulation and performance evaluation. In the dynamic model, some parameters such as scale factor and environmental sensitivity have been determined, whereas in the stochastic model, the other parameters such as random drift and measurement noise have been computed. The performance of the system is evaluated for several inputs. Also, the parameter variation of output noise as a result of changing the dither characteristics is analyzed.

Implementing a proper integration scheme plays an important role in the performance of integrated navigation systems. Not only does employing a more reliable estimation method improve the accuracy of the integrated navigation system, but this can lead to a more robust solution in the presence of different types of uncertainties. Implementing an integration scheme that has a robust and simple structure is a challenging issue in the design of integrated navigation systems. By inspiring from the concept of PID control, this paper proposes a robust integration scheme for aided inertial navigation systems in the presence of aiding sensor measurement uncertainties. The proposed filter combines the concept of proportional-integral-derivative control theory and the standard Kalman filter estimator to improve the performance of the integration scheme. Thanks to the integral and derivative parts added to the proposed scheme, the integrated system attains a faster and more robust solution in the presence of observation errors and uncertainties. The simulation case studies validate the superior efficacy and capability of the proposed scheme compared to the integration method based on the standard Kalman filter.

Todays, one of the topics that is of particular importance in the field of navigation is the use of navigation error propagation equations in order to integrate the output of an inertial navigation system with an external measurement to take advantages of both the inertial navigation mechanism and the external measurement. Study of past research Show that error propagation equations are generally extracted in a geographic frame that can have weaknesses. This paper demonstrates how to extract the error propagation equations in a tangent frame that not only have more simplicity than the geographic frame but also have a higher accuracy in describing the inertial navigation error propagation and thus increase the efficiency of the integrated navigation system. Finally, simulations in two cases using the tangent error propagation model and the geographic error propagation equations are performed by considering the hypothetical values for the acceleration and angular velocities and random error of the sensors. The results of the simulations also confirm the increasing of accuracy of the proposed error propagation model in this paper. ns in this paper also confirm the validity of this issue.