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What is Extended Kalman Filter

Chapter 5
Handbook of Research on AI Methods and Applications in Computer Engineering
The non-linear version of the Kalman filter linearizes an estimate of the current mean and covariance. In the case of well-defined transition models, the EKF has been used.
Published in Chapter:
Artificial Intelligence in Navigation Systems
Ghalia Nasserddine (Jinan University, Lebanon) and Amal A. El Arid (Rafik Hariri University, Lebanon)
Copyright: © 2023 |Pages: 22
DOI: 10.4018/978-1-6684-6937-8.ch005
Abstract
The computer-based navigation system computes the object's position, speed, and direction in real-time. In the last decades, many researchers, companies, and industries have been working on improving the existing navigation system due to its vast application in military and civilian activities. Typically, navigation systems are based on integrating inertial navigation systems and global positioning systems using a Bayesian filter, like the Kalman filter. The limitations of the Kalman filter have inspired researchers to consider alternatives based on artificial intelligence. Recently, many types of research have been developed to validate the possibility of using artificial intelligence methods in navigation systems. This chapter aims to review the integration of artificial intelligence techniques in navigation systems.
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Chapter 207
Sequence Processing with Recurrent Neural Networks
An online learning algorithm for determining the weights in a recurrent network given target outputs as it runs. It is based on the idea of Kalman filtering, which is a well-known linear recursive technique for estimating the state vector of a linear system from a set of noisy measurements.
Published in Chapter: Sequence Processing with Recurrent Neural Networks; From: Encyclopedia of Artificial Intelligence
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Chapter 4
EFWA as a Method of Optimizing Model Parameters: Example of an Expensive Function Evaluation
A variant of the Kalman filter used to estimate states of a non-linear system.
Published in Chapter: EFWA as a Method of Optimizing Model Parameters: Example of an Expensive Function Evaluation; From: Handbook of Research on Fireworks Algorithms and Swarm Intelligence
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Chapter 21
Dynamic Modeling and Parameter Identification for Biological Networks: Application to the DNA Damage and Repair Processes
An efficient recursive filter that estimates the state of a non linear dynamic system from a series of noisy measurements.
Published in Chapter: Dynamic Modeling and Parameter Identification for Biological Networks: Application to the DNA Damage and Repair Processes; From: Handbook of Research on Computational and Systems Biology: Interdisciplinary Applications
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