This paper aims is to design an integrated navigation system constituted by low-cost inertial sensors to estimate the orientation of an Autonomous Underwater Vehicle (AUV) during all phases of under water and surface missions. The proposed approach relied on global positioning system, inertial measurement unit (accelerometer & rate gyro), magnetometer and complementary filter technique. Complementary filter operates based on low pass filter and high pass filter to remove noise and bias error of measurement data in the integrated navigation structure, respectively. Consequently, a relatively accurate orientation estimation is provided for guidance/control system. The most important feature of the proposed approach is the ability of switching between GPS and magnetometer sensor consistent with phase-change in the AUV motion. This brings about more accurate estimation of heading angle in both the surface and underwater phase compared to gyro-based navigation. The performance of the proposed algorithm is assessed in a field test executed on a research AUV and in comparison, with Kalman filter.

Navigation errors in synthetic aperture radar (SAR) applications lead to phase errors in SAR echo signal and image quality degradation. Among different phase errors, zero and first order errors have no effect on image quality, while second and higher order terms are very important. The majority of second and higher order phase errors are due to initial alignment errors, and bias and random errors of inertial sensors. Integrated INS/GPS navigation systems are used for solving this problem. However, discontinuities in integrated navigation data lead to severe SAR image quality degradation. In this paper, the effect of different navigation approaches on SAR azimuth impulse response was studied and the importance of specially-designed algorithms to integrate INS and GPS was illustrated. Then, the proposed algorithm for navigation in SAR application was presented according to navigation errors behavior, and its benefit in azimuth impulse response, and imaging quality was illustrated. ‌‌

A Remotely operated vehicle (ROV) requires a precise navigation system for positioning, path tracking, guidance, and control. Due to the limitation of the global positioning system in the underwater environment, the inertial navigation system is the most important positioning system in an underwater vehicle. In this paper, based on the nonlinear dynamics of the underwater robot to improve the performance of the underwater robot in path tracking, we propose an integrated navigation system based on inertial sensors, compass, and Doppler velocity log. A continuous-time extended Kalman filter was used to combine the sensor data and estimate the robot's position. The simulation results compared to the navigation systems based on linear Kalman filter and discrete-time extended Kalman filter show that the proposed integrated navigation system based on continuous-time extended Kalman filter can estimate the attitude and position of the robot in the control loop with high accuracy

Antenna positioning is very important in synthetic aperture radar systems and it is necessary in the design of a navigation subsystem to account for different sources of error and their effects on the accuracy of navigation results. According to the error behavior of inertial and satellite navigation systems, integration of inertial sensors with satellite positioning systems is a common method to achieve high accuracy navigation results. However, synthetic aperture radar considerations, lead to some problems in the utilization of integrated navigation results in the imaging period. Therefore, only the inertial navigation results are used in the imaging period, and integrated navigation results are used just as the initial conditions for the algorithm. This paper studies the estimation of these initial conditions and predicts the navigation error growth caused by them. Since the extended Kalman filter is the most common tool for the integration of inertial sensors and satellite data, the elements of the corresponding state covariance matrix represent the accuracy of integrated navigation results. In this study for a synthetic aperture radar flight scenario, in which the nominal path is a straight path with a constant velocity, the state covariance matrix is calculated analytically both in the steady-state conditions and after the GNSS data outage. These analytical results are verified with simulations. Specifically, the estimation accuracy of antenna position, velocity and attitude are calculated with respect to the noise level of inertial sensors and GNSS data. Results can be used in the design and/or selection of a proper navigation system in synthetic aperture radar applications.

The navigation system of vehicles calculates the speed, position and attitude of the moving device relative to a reference frame and provides it to the guidance system. One of the most widely used navigation systems is the inertial navigation system. Due to the increasing error of the navigation system over time, the integrated navigation system is usually used for long-term navigation. One of the most common integrated navigation systems is the INS integrated navigation system with GPS, each of them which has advantages and disadvantages that cover the other. In this paper, two GPS and INS data integration algorithms with loosely and tightly coupled integration are implemented and compared. In the loosely coupled method, GPS measurements include positions and speeds. In the tightly coupled method, a model for GPS error is considered, which includes bias dynamics and GPS clock drift. The result of combining GPS and INS data in this way is closer to the truth, but in the method of loosely coupled, the result of the combination follows the average of GPS data. In the implementation of the combined algorithm with tightly coupled, raw GPS data is used, which is pseudo-range and pseudo-range rate along with astronomical information. In this paper, an extended Kalman filter is used to integrate the data of two measurement data. The simulation results show the superiority of tightly over loosely connection performance. Also, the integration algorithm with a loosely approach has been implemented in hardware and car testing has been done in two scenarios of connecting and disconnecting GPS.

Due to the importance of navigation accuracy, the inertial navigation system is often combined with one of the other navigation systems. In one of these methods, the Inertial Navigation System is combined with Global Positioning System. An integrated navigation system consisting of both systems provides reliable, accurate, and consistent navigation capabilities. The design of the estimator filter is one of the basic steps in the implementation of integrated navigation systems. Due to the difficulty in obtaining the accurate model of nonlinear systems, also the complexity of noise in practical environments and existence of noise with uncertain statistical characteristics, the accuracy of the KF estimation is greatly reduced. Therefore, in this paper, to improve the integrated navigation performance, a concept of self-adaptation to the KF is introduced, and the modified Sage-Husa adaptive Kalman filter algorithm based on the recursive noise estimator basis of the maximum posterior likelihood estimation is formed to overcome the shortcomings of the KF methods and solve the problem of state estimation in practical environments with complex noise and uncertain statistical characteristics and uncertainty in the model. A vehicle test was used to evaluate the proposed algorithm, and the results showed that the proposed algorithm has very acceptable accuracy and performance. so it was able to improve the RMSE evaluation criteria of position in the direction of height and speed in the vertical direction by about 38% and 25%, respectively and The RMSE evaluation criteria and Std evaluation criteria of the heading angle are 18% and 17%, respectively.

Abstract: The Inertia Navigation Systems (INS) error, which today's basic navigation system for many uses, including military applications increases in time. Therefore, in order to achieve greater accuracy and reliability, especially in long-time navigation, such as in marine applications, an assistance system alongside the inertial navigation system should be used. In this case, the Global Positioning System (GPS) is the best navigational assistance system due to its complementary features. In an integrated navigation system consisting of a basic navigation system, along with a navigation assistance system, the Extended Kalman Filter (EKF) is a very common tool for integrating GPS and INS data. However, measurement and process noise covariance matrices are two important parameters in the Kalman filter, whose correct adjustment during navigation estimation process is very important in reducing the estimation error. In addition, since the governing equations of the inertial system are inherently nonlinear, the process of linearization in the Kalman filter adds an error due to linear approximation. Hence, researchers are looking for alternative algorithms for the Kalman filter, and so far a lot of research has been done. In this paper, an estimator based on particle optimization algorithm (PSO) is used as a substitute for Kalman filter based estimator. In this way, as soon as GPS observations are received, the estimation error of the navigation system based on the PSO algorithm is minimized to estimate the best output for the system. The simulation results and their application to several practical databases show that the proposed method significantly improved the accuracy of the estimation of navigational states by the integrated navigation system compared with conventional integration methods, such as the extended Kalman filter.

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.

In the last decades, the visual navigation system has been investigated by many researchers as an aided navigation system for Inertial Navigation System (INS) in the Unmanned Aerial Vehicles (UAV). In this research, for improving the INS errors a new approach based on feature tracking algorithm is used. In this approach, in order to estimate the feature points in the current image, the INS states, the feature points of the previous image and dynamic equations are used. Also, in this approach, for improving the estimation of terrain points, the outlier estimated feature points delete. Furthermore, in this article, for improving the altitude error, a barometer is used by the mentioned vision navigation system. The simulation results illustrate the desirable accuracy of the vision system and barometer observations in the update step of Extended Kalman Filter (EKF) and remarkable performance of integrated navigation system for calculating the UAV navigation parameters.

In this paper, the purpose is to compensate the errors of velocityandposition,existingatthestartingtimeofstar sensor work in an integrated inertial/celestial navigation system. In an inertial navigation system, there exists attitude error at launch moment. Moreover, while integrating gyros and accelerometers outputs, errors grow in estimation of attitude, velocity, and position on vehicle. On the other hand, because of earth's atmosphere eects, celestial navigation cannot be implemented for a while after launch moment. Then, pure inertial navigation is carried out at this interval. Consequently, large errors of velocity and position exist at starting time of integration. The estimation and integration are carried out using unscented Kalman lter through which current attitude and the gyros xed bias can be estimated accurately. To precise integration, nonlinear navigation equations have been used and propagated implementing an accurate discretization method. Moreover, in all sequences of navigation and estimation, quaternions have beenusedtodealwithattitude. Thiswillreducecomputation costs and immunize integrated system from singularity. Since quaternions have their own vector space, some considerations are applied to estimation procedure which includes sigma point's calculation, propagation, and calculating mean and covariance. On the other hand, velocity and position errors are not observable in anintegratedinertial/celestialnavigationsystem. Then, in this paper, using nonlinear navigation equations and implementing back-propagation and smoothing, initial attitude and accelerometers xed bias are estimated accurately. In addition, the vehicles attitude is acquired at prior time steps while back-propagating. By carrying out a new parallel navigation based on vehicles attitude at prior moments and taking out gyros and accelerometers xed biases, velocity and position errors are com pensated. To demonstrate the validity and performance of the proposed method, navigation of a LEO launch vehicle has been simulated. Results admit great compensations in velocity and position errors.