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.

System and method for generating lane-level navigation map of unmanned vehicle

Abstract: The invention relates to a system and method for generating a lane-level navigation map of an unmanned vehicle based on multi-source data The lane-level navigation map comprises an offline global map part and an online local map part According to an offline module, within a target region where the unmanned vehicle runs, original road data is acquired through satellite photos (or aerial photos), a vehicle sensor (laser radar and a camera) and a high-precision integrated positioning system (a global positioning system and an inertial navigation system), then the original road data is subjected to offline processing, multiple kinds of road information are extracted, and finally the road information extracting results are fused to generate the offline global map The offline global map is stored through a layered structure According to an online module, when the unmanned vehicle automatically drives in the target region, the road data in the offline global map is extracted according to real-time positioning information, and the online local map with the vehicle as the center within the fixed distance range is drawn The system and method can be applied to fusion sensing, high-precision positioning and intelligent decisions of the unmanned vehicle

Personal navigation using terrain-correlation and/or signal-of-opportunity information

Abstract: A navigation system includes an inertial navigation unit. The navigation system also includes a Kalman filter that generates corrective feedback for use by the inertial navigation unit. The Kalman filter generates the corrective feedback as a function of at least one of GPS/DGPS information, sensor information, user input, terrain correlation information, signal-of-opportunity information, and/or position information output by a motion classifier.

Estimation of deterministic and stochastic IMU error parameters

Abstract: Inertial Measurement Units, the main component of a navigation system, are used in several systems today. IMU's main components, gyroscopes and accelerometers, can be produced at a lower cost and higher quantity. Together with the decrease in the production cost of sensors it is observed that the performances of these sensors are getting worse. In order to improve the performance of an IMU, the error compensation algorithms came into question and several algorithms have been designed. Inertial sensors contain two main types of errors which are deterministic errors like scale factor, bias, misalignment and stochastic errors such as bias instability and scale factor instability. Deterministic errors are the main part of error compensation algorithms. This study explains the methodology of how the deterministic errors are defined by 27 state static and 60 state dynamic rate table calibration test data and how those errors are used in the error compensation model. In addition, the stochastic error parameters, gyroscope and bias instability, are also modeled with Gauss Markov Model and instant sensor bias instability values are estimated by Kalman Filter algorithm. Therefore, accelerometer and gyroscope bias instability can be compensated in real time. In conclusion, this article explores how the IMU performance is improved by compensating the deterministic end stochastic errors. The simulation results are supported by real IMU test data.

An Adaptive Fault-Tolerant Multisensor Navigation Strategy for Automated Vehicles

Abstract: This paper presents a novel adaptive fault-tolerant multisensor navigation strategy for automated vehicles on the automated highway system. To ensure safe and reliable running, the main concern is how we can realize sensor fault detection, isolation, and accommodation. The proposed strategy adopts a hybrid decentralized filter architecture that fuses multiple sensors, including the strapdown inertial navigation system, the carrier phase-differential Global Positioning System (GPS), the wheel encoder, and the electronic compass. Through the integration of multiple redundancy sensors, the navigation system has fault-tolerant potential. The integrated filter model is first established in detail. Then, a highly fault-tolerant navigation filter iterative algorithm is proposed. The proposed algorithm realizes fault tolerance by utilizing fuzzy logic to update the local filter based on a relative degree of mismatch by simultaneously considering sensor failure degree and to make fault isolation decisions. To compare and evaluate the fault-tolerant performance, several traditional navigation methods are also investigated. Simulation results demonstrate that the proposed integrated navigation method can effectively detect, isolate, and accommodate a variety of hard and soft sensor failures, which may even simultaneously occur.