Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.

A UWB/Improved PDR Integration Algorithm Applied to Dynamic Indoor Positioning for Pedestrians.

Abstract: Inertial sensors are widely used in various applications, such as human motion monitoring and pedestrian positioning. However, inertial sensors cannot accurately define the process of human movement, a limitation that causes data drift in the process of human body positioning, thus seriously affecting positioning accuracy and stability. The traditional pedestrian dead-reckoning algorithm, which is based on a single inertial measurement unit, can suppress the data drift, but fails to accurately calculate the number of walking steps and heading value, thus it cannot meet the application requirements. This study proposes an indoor dynamic positioning method with an error self-correcting function based on the symmetrical characteristics of human motion to obtain the definition basis of human motion process quickly and to solve the abovementioned problems. On the basis of this proposed method, an ultra-wide band (UWB) method is introduced. An unscented Kalman filter is applied to fuse inertial sensors and UWB data, inertial positioning is applied to compensation for the defects of susceptibility to UWB signal obstacles, and UWB positioning is used to overcome the error accumulation of inertial positioning. The above method can improve both the positioning accuracy and the response of the positioning results. Finally, this study designs an indoor positioning test system to test the static and dynamic performances of the proposed indoor positioning method. Results show that the positioning system both has high accuracy and good real-time performance.

RIDI: Robust IMU Double Integration

Abstract: This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground-truth motions across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our algorithm has surprisingly shown comparable results to full Visual Inertial navigation. To our knowledge, this paper is the first to integrate sophisticated machine learning techniques with inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research.

A Two-Mode INS/CNS Navigation Method for Lunar Rovers

Abstract: A secure and autonomous navigation system is needed for the lunar rover in future lunar missions. The inertial navigation system (INS) and celestial navigation system (CNS) are two usually used autonomous navigation systems for rovers. Because INS and CNS are complementary to each other, INS/CNS integrated navigation becomes a very attractive solution for long-time and long-distance navigation. Traditional INS/CNS methods for aircrafts or ballistic missiles are not optimal for rovers because these methods only work in one motion state, but the rover operates in two motion states. For solving this problem, a two-mode INS/CNS navigation method for lunar rovers is presented in this paper. When the rover is stationary, a tightly coupled mode is used to correct the position and attitude of the rover. When the rover is in motion, a loosely coupled mode is used to correct the attitude of the rover. Two modes use different system models and different measurements to fulfill different targets and keep the error of position and attitude from accumulation. The validity and feasibility of the two-mode method is tested and examined by ground test system. The longitude and latitude errors within 50 m in distance can be achieved by this two-mode method during an 80-min traverse. These results demonstrate that it is a good choice of autonomous and high-accuracy navigation for lunar rovers.

Inertial pointing and positioning system

Abstract: An inertial pointing and control system and method for pointing to a designated target with known coordinates from a platform, to provide accurate position, steering, and command information The system continuously receives GPS signals and corrects Inertial Navigation System (INS) dead reckoning or drift errors An INS is mounted directly on a pointing instrument rather than in a remote location on the platform for monitoring the terrestrial position and instrument attitude, and for pointing the instrument at designated celestial targets or ground based landmarks As a result, the pointing instrument and the INS move independently in inertial space from the platform since the INS is decoupled from the platform Another important characteristic of the present system is that selected INS measurements are combined with predefined coordinate transformation equations and control logic algorithms under computer control in order to generate inertial pointing commands to the pointing instrument More specifically, the computer calculates the desired instrument angles (Phi, Theta, Psi), which are then compared to the Euler angles measured by the instrument-mounted INS, and forms the pointing command error angles as a result of the compared difference

A Novel INS and Doppler Sensors Calibration Method for Long Range Underwater Vehicle Navigation

Abstract: Since the drifts of Inertial Navigation System (INS) solutions are inevitable and also grow over time, a Doppler Velocity Log (DVL) is used to aid the INS to restrain its error growth. Therefore, INS/DVL integration is a common approach for Autonomous Underwater Vehicle (AUV) navigation. The parameters including the scale factor of DVL and misalignments between INS and DVL are key factors which limit the accuracy of the INS/DVL integration. In this paper, a novel parameter calibration method is proposed. An iterative implementation of the method is designed to reduce the error caused by INS initial alignment. Furthermore, a simplified INS/DVL integration scheme is employed. The proposed method is evaluated with both river trial and sea trial data sets. Using 0.03°/h(1σ) ring laser gyroscopes, 5 × 10−5 g(1σ) quartz accelerometers and DVL with accuracy 0.5% V ± 0.5 cm/s, INS/DVL integrated navigation can reach an accuracy of about 1‰ of distance travelled (CEP) in a river trial and 2‰ of distance travelled (CEP) in a sea trial.