Adaptive Kalman Filtering for INS/GPS

Mohamed, Ashraf A.; Schwarz, K. P.

doi:10.1007/s001900050236

Cited by 929 publications

(587 citation statements)

References 14 publications

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“…This is why the next step consists of blending the GNSS trajectory with information from the IMU using a Kalman Filter [49]. As mentioned before the IMU accurately records the aircraft's attitude, usually at a rate of several hundred times per second.…”

Section: Trajectory Determinationmentioning

confidence: 99%

Now You See It… Now You Don’t: Understanding Airborne Mapping LiDAR Collection and Data Product Generation for Archaeological Research in Mesoamerica

et al. 2014

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Abstract:In this paper we provide a description of airborne mapping LiDAR, also known as airborne laser scanning (ALS), technology and its workflow from mission planning to final data product generation, with a specific emphasis on archaeological research. ALS observations are highly customizable, and can be tailored to meet specific research needs. Thus it is important for an archaeologist to fully understand the options available during planning, collection and data product generation before commissioning an ALS survey, to ensure the intended research questions can be answered with the resultant data products. Also this knowledge is of great use for the researcher trying to understand the quality and limitations of existing datasets collected for other purposes. Throughout the paper we use examples from archeological ALS projects to illustrate the key concepts of importance for the archaeology researcher.

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Section: Trajectory Determinationmentioning

confidence: 99%

Now You See It… Now You Don’t: Understanding Airborne Mapping LiDAR Collection and Data Product Generation for Archaeological Research in Mesoamerica

et al. 2014

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“…In order to overcome these limitations, the Unscented Kalman Filter (UKF) has been proposed by Julier and Uhlmann [14], [15]. Based on EKF and UKF, adaptive Kalman filters have been developed to achieve much better estimation performance for non linear systems by adjusting the noise covariance matrices during estimation [16].…”

Section: Kalman Filtermentioning

confidence: 99%

Performance Comparison of the Kalman Filter Variants for Dynamic Mobile Localization in Urban Area Using Cellular Network

Bouzera¹,

Mezhoud²,

Khireddine³

et al. 2015

IJCTE

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“…For new positioning techniques, the error behavior may not be fully understood, while complex error behavior can be difficult to measure. Adaptive estimation techniques [66][67] [68] provide only a partial solution. Even where the error behavior is well known, it can too complex to practically model within the estimation algorithm.…”

Section: Issues To Resolvementioning

confidence: 99%

The four key challenges of advanced multisensor navigation and positioning

Groves

Wang

Walter

et al. 2014

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014

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Abstract-The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Although many new navigation and positioning methods have been developed in recent years, little has been done to bring them together into a robust, reliable, and cost-effective integrated system. To achieve this, four key challenges must be met: complexity, context, ambiguity, and environmental data handling. This paper addresses each of these challenges. It describes the problems, discusses possible approaches, and proposes a program of research and standardization activities to solve them. The discussion is illustrated with results from research into urban GNSS positioning, GNSS shadow matching, environmental feature matching, and context detection.

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Adaptive Kalman Filtering for INS/GPS

Cited by 929 publications

References 14 publications

Now You See It… Now You Don’t: Understanding Airborne Mapping LiDAR Collection and Data Product Generation for Archaeological Research in Mesoamerica

Now You See It… Now You Don’t: Understanding Airborne Mapping LiDAR Collection and Data Product Generation for Archaeological Research in Mesoamerica

Performance Comparison of the Kalman Filter Variants for Dynamic Mobile Localization in Urban Area Using Cellular Network

The four key challenges of advanced multisensor navigation and positioning

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