Topic
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.
Papers published on a yearly basis
Papers
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TL;DR: A new approach to tightly integrate the multi-GNSS PPP and INS together in the observation level is presented and the position accuracy can be improved significantly, but very little improvement in velocity and attitude is achieved.
Abstract: Precise point positioning (PPP) using the Global Positioning System (GPS) is widely recognized as an efficient approach for providing precise positioning services. However, its accuracy and reliability could be significantly degraded by unexpected observation discontinuities and unfavorable tracking geometry which are unavoidable, especially in severe environments such as city canyons. Therefore, in the last decades inertial navigation system (INS) has been integrated to overcome such drawbacks. Recently, multi-Global Navigation Satellite Systems (GNSS) were applied to enhance the PPP performance by appropriate usage of the increased number of satellites. We present a new approach to tightly integrate the multi-GNSS PPP and INS together in the observation level. The inter-system bias and inter-frequency bias of multi-GNSS and the hardware errors of INS sensors are estimated to improve the position accuracy and to shorten the convergence time of PPP. In order to demonstrate the impact of multi-GNSS observations and INS data on the derived position, velocity, attitude, and the convergence time of PPP, the new approach is validated through an experimental test with a set of land vehicle data. The results show that the position accuracy can be improved by multi-GNSS and INS significantly, but very little improvement in velocity and attitude is achieved. The position root-mean-square improves from 23.3, 19.8, and 14.9 cm of the GPS PPP/INS tightly coupled integration (TCI) solution to 7.9, 3.3, and 5.1 cm of multi-GNSS PPP/INS TCI in north, east, and up components, respectively. Furthermore, GNSS outages are simulated and their effect on the performance of multi-GNSS PPP/INS TCI is investigated to demonstrate the contribution of the multi-GNSS PPP/INS TCI during GNSS outages. In addition, the convergence test also shows that both multi-GNSS and INS can improve the PPP convergence performance noticeably.
64 citations
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TL;DR: The light-pulse atom interferometry method is reviewed in this article, where applications of the method to inertial navigation and tests of the equivalence principle are discussed, as well as its application in test cases of the Equivalence Principle.
Abstract: The light-pulse atom interferometry method is reviewed. Applications of the method to inertial navigation and tests of the Equivalence Principle are discussed.
64 citations
TL;DR: In this paper, an airborne laser altimetry experiment was conducted over the Long Valley caldera, California, in which each of two scanning laser altimeters (dubbed SLICER and RASCAL) were flown in a NASA T-39 jet aircraft.
Abstract: Between 28 September and 7 October 1995, we conducted an airborne laser altimetry experiment over the Long Valley caldera, California, in which each of two scanning laser altimeters (dubbed SLICER and RASCAL) were flown in a NASA T-39 jet aircraft. Operating concurrently were a Global Positioning System (GPS) guidance system and dual frequency receivers for precise navigation and post-flight calculation or the airplane trajectory relative to a ground station, and an inertial navigation system (INS) for attitude determination. Reduction of raw laser ranges requires merging the differential kinematic GPS aircraft trajectory and the INS data with the laser data, and determination of the atmospheric delay. Data geolocation consists of obtaining the centre location and the mean elevation within each footprint in a geodetic coordinate system. The elevation of Crowley Lake is recovered to an accuracy of approximately 3 cm or better from 3 km above ground level and crossover analysis indicates that the elevation estimates are consistent from pass to pass. We test our geolocation procedures by comparing laser-derived elevations with those determined in situ for recognizable ground features. A comparison of laser and GPS-derived positions shows that the horizontal accuracy is better than the diameter of the footprint and vertical accuracy is within the error inherent in the range measurement. A comparison of SLICER elevation data with digital elevation models (DEMs) of the region shows that the DEM data provides surface topography to within stated accuracy limits. Although research continues to utilize the full potential of laser altimetry data, our results constitute a successful demonstration that the technique may be used to perform geodetic monitoring of surface topographic changes.
64 citations
TL;DR: This paper presents an unscented Kalman filter based multi-sensor optimal data fusion methodology for INS/GNSS/CNS integration based on nonlinear system model that refrains from the use of covariance upper bound to eliminate the correlation between local states.
Abstract: This paper presents an unscented Kalman filter (UKF) based multi-sensor optimal data fusion methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integration based on nonlinear system model. This methodology is of two-level structure: at the bottom level, the UKF is served as local filters to integrate GNSS and CNS with INS respectively for generating the local optimal state estimates; and at the top level, a novel optimal data fusion approach is derived based on the principle of linear minimum variance for the fusion of local state estimates to obtain the global optimal state estimation. The proposed methodology refrains from the use of covariance upper bound to eliminate the correlation between local states. Its efficacy is verified through simulations, practical experiments and comparison analysis with the existing methods for INS/GNSS/CNS integration.
64 citations
TL;DR: The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems.
Abstract: This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.
64 citations