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GPS/INS

About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.


Papers
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Proceedings ArticleDOI
19 May 2005
TL;DR: An organized approach to a common problem is presented along with some control system implementation issues and the results are shown to be applicable as a rate-aiding enhancement to many closed-loop tracking systems.
Abstract: A common requirement in electro-optical surveillance or weapon delivery systems is to point at or scan a target from a moving vehicle. The required gimbal commands are developed and presented for a variety of gimbal configurations using position and / or velocity information such as is commonly available from an onboard INS or GPS system. Both pointing angle and angular rate approaches are considered along with considerations for back-scanning with, for example, a fast steering mirror. The results are also shown to be applicable as a rate-aiding enhancement to many closed-loop tracking systems. While no fundamentally new techniques are presented in this paper, an organized approach to a common problem is presented along with some control system implementation issues.

17 citations

Proceedings ArticleDOI
20 Jun 2010
TL;DR: Simulations show that compared with the conventional Kalman filtering approach, the IMM-UKF algorithm is more stable and effective, thus improving the convergence speed and accuracy.
Abstract: The motivation of INS/GPS integration is to develop a navigation system that overcomes the shortcomings of each system. Its implementation is essentially based on the filter techniques and error models of INS. If the model changes with the environment, the estimation accuracy is degraded. In this paper, an Interacting Multiple Model Unscented Kalman Filter (IMM-UKF) method was proposed to jointly estimate the position information. This modeling approach makes it possible to employ the UKF to deal with the problem of nonlinear filtering with uncertainty noise. The output of the IMM-UKF is the weighted sum of a bank of parallel unscented Kalman filters. Simulations show that compared with the conventional Kalman filtering approach, the IMM-UKF algorithm is more stable and effective, thus improving the convergence speed and accuracy.

17 citations

Proceedings ArticleDOI
15 Aug 2011
TL;DR: The experimental results demonstrate that the improved adaptive Kalman filtering algorithm proposed in this paper has a strong adaptability to time-varying measurement noises, which improves precision of the advanced robot navigation.
Abstract: Navigation technology plays an important role in the designing of advanced robot. An advanced robot navigation system based on GPS/INS is modeled in this paper. According to the model, the causes of the errors in measurement equation are analyzed, concluding that HDOP (Horizontal Dilution of Precision) and VDOP (Vertical Dilution of Precision) provided by GPS receiver are the crucial factors for the change of measurement noise in the mathematical model. Based on the above conclusion, this paper proposes a novel second order fuzzy self-adaptive filter design. Choosing the differences of location and velocity information provided by GPS receiver and INS device as the inputs, this filter modifies the regulation factor based on the residual sequence statistical information and PDOP (Position Dilution of Precision) provided by GPS receiver to correct the outputs of INS device using fuzzy logic. The experimental results demonstrate that the improved adaptive Kalman filtering algorithm proposed in this paper has a strong adaptability to time-varying measurement noises, which improves precision of the advanced robot navigation.

17 citations

Journal ArticleDOI
01 Feb 2021-Robotica
TL;DR: The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments and the results from the experimental platform validate the efficacy of the proposed algorithm.
Abstract: In mobile robot localization with multiple sensors, myriad problems arise as a result of inadequacies associated with each of the individual sensors. In such cases, methodologies built upon the concept of multisensor fusion are well-known to provide optimal solutions and overcome issues such as sensor nonlinearities and uncertainties. Artificial neural networks and fuzzy logic (FL) approaches can effectively model sensors with unknown nonlinearities and uncertainties. In this article, a robust approach for localization (positioning) of a mobile robot in indoor as well as outdoor environments is proposed. The neural network is utilized as a pseudo-sensor that models the global positioning system (GPS) and is used to predict the robot’s position in case of GPS signal loss in indoor environments. The data from proprioceptive sensors such as inertial sensors and GPS are fused using the Kalman and the complementary filter-based fusion schemes in the outdoor case. To eliminate the position inaccuracies due to wheel slippage, an expert FL system (FLS) is implemented and cascaded with the sensor fusion module. The proposed technique is tested both in simulation and in real scenarios of robot movements. The simulations and results from the experimental platform validate the efficacy of the proposed algorithm.

17 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202317
202247
20219
202013
201925
201840