Sensor fusion (SF) is in high demand due to the availability of sensor data from a variety of sources. Due to the inherent advantages and disadvantages of various sensor types, a good algorithm will also prioritise certain data points over others. SF techniques combine sensory input to assist in reducing ambiguity in machine perception when appropriately synthesised. They are tasked with the responsibility of integrating data from many sensors. Bayesian methods like Kalman Filters are frequently used to perform the fusion. There are a few more algorithms that are employed in the fusion process.
Existing Sensor Fusion Algorithms
SF algorithms combine all inputs and generate accurate and dependable output, even when individual measurements are incorrect. Let’s have a look at some of the existing SF algorithms.
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- The Kalman Filter: It is the most extensively used prediction-correction filtering method in sensor fusion and is especially effective in navigation and positioning technologies.
- Bayesian Network: These methods, which are based on Bayes’ rule and emphasise probability, predict the likelihood of contributing components from many hypotheses.
- Central Limit Theorem (CLT): Based on the law of large numbers, CLT algorithms collect several samples or readings in order to calculate the most accurate average value for the dataset, which is generally represented by a bell curve.
- Convolutional Neural Network: These algorithms classify findings by fusing image recognition data from multiple sources.
- Dempster-Shafer: Often referred to as a generalised form of Bayesian theory, these algorithms employ uncertainty management and inference techniques that closely resemble human thinking and perception.
What data type does SF deal with?
The type of data utilised as inputs to algorithms can also be used to define the degree of sensor fusion.
- At the data level, the fusion algorithm is fed raw data from a variety of sources.
- At the feature level, the fusion algorithm is fed data or features from a range of individual sensors.
- After data and feature-level sensor fusion, decision-level sensor fusion occurs when a hypothesis is chosen from a set of hypotheses.
How does sensor-to-sensor communication happen?
- Complementary: When “sensors are not directly dependent on one another but can be coupled to produce a more complete image,” which is advantageous for motion detection jobs.
- Competitive or Redundant: When each sensor “provides separate measurements of the same attribute,” this is advantageous for error correction.
- Cooperative: When data from separate sensors is used to “deduce information that would not be available from single sensors,” as is the case when analysing human motion in science and medicine.
SF has a wide range of functions and applications. It is used in the following levels:
Level 0: Alignment of data
Level 1: Assessment of entities
Level 2: Situational analysis
Level 3: Impact evaluation at the third level
Level 4: Process optimisation
Level 5: Refinement of the user
Where is Sensor Fusion most frequently used?
Sensors are employed in an infinite number of applications across 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, military, oil exploration, etc.
A 19.7% CAGR is expected to take the Sensor Fusion market to a global value of $19.84 billion by 2030, as per Allied Market Research. There are extremely few contributions to this field of research. Many AI-based approaches to sensor fusion have been created in recent studies to determine multiple sensor information contributions based on unique requirements, conditions, and tasks. Moreover, new sensor technologies are being integrated with 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 rise in tandem with algorithm complexity.