Sensor fusion (SF) is in high demand due to the availability of sensor data from various sources. Due to the inherent advantages and disadvantages of different types of sensors, a good algorithm will also prioritize some data points over others. SF techniques combine sensory inputs to help reduce ambiguity in machine perception when synthesized appropriately. They are responsible for integrating data from many sensors. Bayesian methods such as Kalman filters are frequently used to perform the fusion. There are a few other algorithms that are used in the merge process.

Existing sensor fusion algorithms

SF algorithms combine all inputs and generate accurate and reliable output, even when individual measurements are incorrect. Let’s take a look at some of the existing SF algorithms.

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• The Kalman filter: this is the prediction-correction filtering method most used in sensor fusion and is particularly effective in navigation and positioning technologies.

• Bayesian Network: These methods, which are based on Bayes’ rule and emphasize probability, predict the probability of component contribution from many assumptions.

• Central Limit Theorem (CLT): Based on the law of large numbers, CLT algorithms collect multiple samples or readings in order to calculate the most accurate mean value for the data set, which is usually represented by a bell curve.

• Dempster-Shafer: Often referred to as a generalized form of Bayesian theory, these algorithms use uncertainty management and inference techniques that closely resemble human thought and perception.

What kind of data does SF process?

The type of data used as inputs for the algorithms can also be used to define the degree of fusion of the sensors.

• At the data level, the merge algorithm is fed with raw data from various sources.
• At the functionality level, the fusion algorithm is fed with data or functionality from a range of individual sensors.
• After merging the data and feature level sensors, decision level sensor merging occurs when a hypothesis is chosen from a set of hypotheses.

How does the sensor-to-sensor communication work?

• Complementary: when “the sensors do not depend directly on each other but can be coupled to produce a more complete image”, which is advantageous for motion detection work.
• Competitive or redundant: When each sensor “provides separate measurements of the same attribute”, this is beneficial for error correction.
• Cooperative: When data from separate sensors is used to “infer information that would not be available from single sensors”, as is the case when analyzing human movement in science and medicine.

SF has a wide range of functions and applications. It is used in the following levels:

Level 0: Data alignment

Level 1: Entity assessment

Level 2: Situational analysis

Level 3: Impact assessment at the third level

Level 4: Process optimization

Level 5: User refinement

Where is Sensor Fusion used most often?

Sensors are used in an endless number of applications in a wide variety of industries and sectors. Some of the industries that benefit from SF are the automotive industry, climate monitoring, computer software, consumer electronics, healthcare, home automation, industrial control, Internet of Things, manufacturing, l army, oil exploration, etc.

A 19.7% CAGR is expected to bring the sensor fusion market to a global value of $ 19.84 billion by 2030, according to Allied Market Research. There are extremely few contributions to this area of ​​research. Many AI-based approaches for sensor fusion have been created in recent studies to determine multiple contributions of sensor information based on unique requirements, conditions, and tasks. Additionally, new sensor technologies are being incorporated into AI-based solutions in real-world applications on a daily basis, now that Industry 4.0 has been introduced. The cost of software and processing power will increase along with the complexity of algorithms.

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