Estimation methods

One of the simplest and most intuitive fusion methods is to take the weighted average of redundant information coming from a group of sensors and use it as the fusion value. This method allows real-time processing of data at a low level, the Kalman Filter is predominantly preferred as it provides a method that is close

in the processing requirements and, in contrast to the weighted average, the fusion result is optimal, in a statistical sense.

Inference methods

Information fusion based on Bayesian inference provides a formalism for combining evidence according to the rules of probability theory. Uncertainty is represented in terms of conditional probabilities that describe beliefs and that can have values ​​in the interval [0,1] where 0 indicates total lack of belief and 1 absolute belief.

Artificial intelligence methods

High-level inferences require human reasoning such as pattern recognition, planning, deduction, and learning. The inference processes used by expert systems begin with a set of initial data (a prior data) and some basic rules.

Neural networks and fuzzy logic are examples of this type of methods.

• - Consumer electronics.

• - Geomatics.

• - Bioinformatics.

• - Sensor network.

• - Intelligent transportation systems.

• - Data mining.

• - Kalman filter.

• - IMU.

• - Liggins, Martin E., David L. Hall, and James Llinas. (2008) Multisensor Data Fusion, Second Edition. Theory and Practice (Multisensor Data Fusion). CRC, ISBN 978-1-4200-5308-1 (in English).

• - David L. Hall, Sonya A. H. McMullen (2004) Mathematical Techniques in Multisensor Data Fusion, ISBN 1-58053-335-3 (in English).

• - H. B. Mitchell (2007) Multi-sensor Data Fusion – An Introduction, Springer-Verlag, Berlin, ISBN 978-3-540-71463-7 (in English).

• - S. Das (2008) High-Level Data Fusion Artech House Publishers, Norwood, MA, ISBN 978-1-59693-281-4 and 1596932813 (in English).

• - Erik P. Blasch, Eloi Bosse, and Dale A. Lambert (2012), High-Level Information Fusion Management and System Design, Artech House Publishers, Norwood, MA. ISBN 1-60807-151-0 | ISBN 978-1-60807-151-7 (in English).

Estimation methods

One of the simplest and most intuitive fusion methods is to take the weighted average of redundant information coming from a group of sensors and use it as the fusion value. This method allows real-time processing of data at a low level, the Kalman Filter is predominantly preferred as it provides a method that is close

in the processing requirements and, in contrast to the weighted average, the fusion result is optimal, in a statistical sense.

Inference methods

Information fusion based on Bayesian inference provides a formalism for combining evidence according to the rules of probability theory. Uncertainty is represented in terms of conditional probabilities that describe beliefs and that can have values ​​in the interval [0,1] where 0 indicates total lack of belief and 1 absolute belief.

Artificial intelligence methods

High-level inferences require human reasoning such as pattern recognition, planning, deduction, and learning. The inference processes used by expert systems begin with a set of initial data (a prior data) and some basic rules.

Neural networks and fuzzy logic are examples of this type of methods.

• - Consumer electronics.

• - Geomatics.

• - Bioinformatics.

• - Sensor network.

• - Intelligent transportation systems.

• - Data mining.

• - Kalman filter.

• - IMU.

• - Liggins, Martin E., David L. Hall, and James Llinas. (2008) Multisensor Data Fusion, Second Edition. Theory and Practice (Multisensor Data Fusion). CRC, ISBN 978-1-4200-5308-1 (in English).

• - David L. Hall, Sonya A. H. McMullen (2004) Mathematical Techniques in Multisensor Data Fusion, ISBN 1-58053-335-3 (in English).

• - H. B. Mitchell (2007) Multi-sensor Data Fusion – An Introduction, Springer-Verlag, Berlin, ISBN 978-3-540-71463-7 (in English).

• - S. Das (2008) High-Level Data Fusion Artech House Publishers, Norwood, MA, ISBN 978-1-59693-281-4 and 1596932813 (in English).

• - Erik P. Blasch, Eloi Bosse, and Dale A. Lambert (2012), High-Level Information Fusion Management and System Design, Artech House Publishers, Norwood, MA. ISBN 1-60807-151-0 | ISBN 978-1-60807-151-7 (in English).