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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
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Journal ArticleDOI
23 Sep 2015-Sensors
TL;DR: An improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency, and a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning.
Abstract: Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.

121 citations

Journal ArticleDOI
TL;DR: A refined strong tracking unscented Kalman filter (RSTUKF) is developed to enhance the UKF robustness against kinematic model error and maintains the optimal UKF estimation in the absence of kinematics model error.

121 citations

Journal ArticleDOI
TL;DR: The achieved relative figure of merits using the collected data validates the reliability of the proposed methods for the desired applications and permits the potential application of the current study in camera-aided inertial navigation for positioning and personal assistance for future research works.
Abstract: This paper presents a method for pedestrian activity classification and gait analysis based on the microelectromechanical-systems inertial measurement unit (IMU). The work targets two groups of applications, including the following: 1) human activity classification and 2) joint human activity and gait-phase classification. In the latter case, the gait phase is defined as a substate of a specific gait cycle, i.e., the states of the body between the stance and swing phases. We model the pedestrian motion with a continuous hidden Markov model (HMM) in which the output density functions are assumed to be Gaussian mixture models. For the joint activity and gait-phase classification, motivated by the cyclical nature of the IMU measurements, each individual activity is modeled by a “circular HMM.” For both the proposed classification methods, proper feature vectors are extracted from the IMU measurements. In this paper, we report the results of conducted experiments where the IMU was mounted on the humans' chests. This permits the potential application of the current study in camera-aided inertial navigation for positioning and personal assistance for future research works. Five classes of activity, including walking, running, going upstairs, going downstairs, and standing, are considered in the experiments. The performance of the proposed methods is illustrated in various ways, and as an objective measure, the confusion matrix is computed and reported. The achieved relative figure of merits using the collected data validates the reliability of the proposed methods for the desired applications.

120 citations

Patent
04 Aug 2009
TL;DR: In this paper, an Ultra-Tightly Coupled GPS-inertial navigation system for use in a moving agile platform includes a range residual extractor that uses best curve fitting of a third order polynomial for estimating range residual.
Abstract: An Ultra-Tightly Coupled GPS-inertial navigation system for use in a moving agile platform includes a range residual extractor that uses best curve fitting of a third order polynomial for estimating range residual. The curve-fitted residual is used to update an error Kalman filter. The error Kalman filter includes correction for navigation solution, and IMU and GPS parameters. The navigation solution together with GPS parameter corrections are used in a Tracking Predictor to generate high-sampling-rate carrier and code replicas. The curve-fitting error covariance indicates signal to noise ratio for the tracked GPS signal and may be used for early indication of interference or jamming.

120 citations

Patent
26 Dec 2000
TL;DR: In this paper, the velocity and acceleration from an inertial navigation processor of the integrated GPS/INS system are used to aid the code and carrier phase tracking of the global positioning system satellite signals.
Abstract: An improved fully-coupled vehicle positioning method and system with differential GPS can substantially solve the problems encountered in either the global positioning system-only or the inertial navigation system-only, such as loss of global positioning satellite signal, sensitivity to jamming and spoofing, and an inertial solution's drift over time. In the present invention, the velocity and acceleration from an inertial navigation processor of the integrated GPS/INS system are used to aid the code and carrier phase tracking of the global positioning system satellite signals, so as to enhance the performance of the global positioning and inertial integration system, even in heavy jamming and high dynamic environments. To improve the accuracy of the integrated GPS/INS navigation system, phase measurements are used and the idea of the differential GPS is employed. A master-slave relative positioning scheme is invented and is effective for high accuracy formation driving and flight.

120 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023309
2022657
2021491
2020889
20191,003
20181,013