Cubature-based Kalman filters for positioning

Pesonen, Henri; Piché, Robert

doi:10.1109/wpnc.2010.5653829

Cited by 47 publications

(35 citation statements)

References 7 publications

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“…[86] for bearings-only tracking (BOT) problem. Pesonen and Piché [87] compared a UKF and CKF using an extensive set of positioning benchmarks, including real and simulated data from GPS and mobile phone base stations, and resulting in conclusion that in tested scenarios no particular filter in this family is clearly superior.…”

Section: Ckfmentioning

confidence: 99%

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

Kong

Bai

et al. 2018

Applied Sciences

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Mobility is a significant robotic task. It is the most important function when robotics is applied to domains such as autonomous cars, home service robots, and autonomous underwater vehicles. Despite extensive research on this topic, robots still suffer from difficulties when moving in complex environments, especially in practical applications. Therefore, the ability to have enough intelligence while moving is a key issue for the success of robots. Researchers have proposed a variety of methods and algorithms, including navigation and tracking. To help readers swiftly understand the recent advances in methodology and algorithms for robot movement, we present this survey, which provides a detailed review of the existing methods of navigation and tracking. In particular, this survey features a relation-based architecture that enables readers to easily grasp the key points of mobile intelligence. We first outline the key problems in robot systems and point out the relationship among robotics, navigation, and tracking. We then illustrate navigation using different sensors and the fusion methods and detail the state estimation and tracking models for target maneuvering. Finally, we address several issues of deep learning as well as the mobile intelligence of robots as suggested future research topics. The contributions of this survey are threefold. First, we review the literature of navigation according to the applied sensors and fusion method. Second, we detail the models for target maneuvering and the existing tracking based on estimation, such as the Kalman filter and its series developed form, according to their model-construction mechanisms: linear, nonlinear, and non-Gaussian white noise. Third, we illustrate the artificial intelligence approach-especially deep learning methods-and discuss its combination with the estimation method.

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Section: Ckfmentioning

confidence: 99%

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

Kong

Bai

et al. 2018

Applied Sciences

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“…We implement an augmented approach which can be considered as an extension of the originally proposed CKF or CQKF [19], [22] in the similar way it has been done before for UKF [23]. The CQKF can be considered as a generalization of the CKF for multiple Gauss-Laguerre points [20], where for single GaussLaguerre quadrature point it coincides with classical CKF [19].…”

Section: A Filter Designmentioning

confidence: 99%

Robust position and velocity estimation methods in integrated navigation systems for inland water applications

Medina

Romanovas

Pinzón

et al. 2016

2016 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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As the Global Navigation Satellite Systems (GNSS) are intensively used as main source of Position, Navigation and Timing (PNT) information for maritime and inland water navigation, it becomes increasingly important to ensure the reliability of GNSS-based navigation solutions for challenging environments. Although an intensive work has been done in developing GNSS Receiver Autonomous Integrity Monitoring (RAIM) algorithms, a reliable procedure to mitigate multiple simultaneous outliers is still lacking. The presented work evaluates the performance of several methods for multiple outlier mitigation based on robust estimation framework and compares them to the performance of state-of-the-art RAIM methods. The relevant methods include M-estimation, S-estimation, LMS and RANSAC-based approaches as well as corresponding modifications for C/N0-based weighting schemes. The snapshot positioning methods are also tested within the quaternion-based Cubature Quadrature Kalman filter for integrated inertial/GNSS solution. The presented schemes are evaluated using real measurement data from challenging inland water scenarios with multiple bridges and a waterway lock. The initial results are encouraging and clearly indicate the potential of the discussed methods both for classical snapshot solutions as well for the methods with complementary sensors.

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“…Spherical-radial cubature criterion uses spherical radial transform to calculate equation (11). Take The Square-root strategy for CKF is as follows [7][8]:…”

Section: Isrckfmentioning

confidence: 99%

Strap-down Inertial Navigation System Initial Alignment Based on Iterative Square-Root Cubature Kalman Filter

Li¹,

Jiang²

2016

IJCA

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Cubature-based Kalman filters for positioning

Cited by 47 publications

References 7 publications

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

State-of-the-Art Mobile Intelligence: Enabling Robots to Move Like Humans by Estimating Mobility with Artificial Intelligence

Robust position and velocity estimation methods in integrated navigation systems for inland water applications

Strap-down Inertial Navigation System Initial Alignment Based on Iterative Square-Root Cubature Kalman Filter

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