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What is Sensor Fusion?

Sensors are the foundation of the Internet of Things and all our connected devices. That means they must be reliable and consistent with all the information they provide. However, there are instances when they experience noise, that results in miscalculations or malfunctions, not unlike humans.

This is where sensor fusion comes into play.

We use the power of a microcontroller “brain” to combine individual data segments from multiple sensors. In this way, we get a more accurate, reliable view of the data than if we used data from each sensor individually. This is an effective technique that balances the strengths of each sensor and leads to a more complete understanding of the information obtained from the combination of sensors.

Benefits of Sensor Fusion

Sensor fusion has multiple benefits that can be applied to modern technology and industries, let us look at some of the most significant ones. As mentioned above, combining data from multiple sensors improves the overall accuracy, as it compensates for individual sensor errors that may arise. These multiple sensors also provide enhanced functionality, as devices can perform in ways that would otherwise be impossible with a single sensor. Increased robustness is another factor, this simply means that the system remains stable under varying conditions and is less susceptible to failure, as it does not rely on one single source for data.  Sensor fusion also helps to reduce latency as data can be gathered and processed more rapidly, which improves the system’s response time.

Types of Sensor Fusion

In cooperative sensor fusion data provided by two separate sensors is used to produce more detailed information that individual input would allow. An example of this would be merging data from audio and visual sensor systems.

A complementary sensor fusion system relies on sensors that operate independently and do not directly depend on each other. They can, however, be combined to generate a more complete image of the event being observed.

Competitive/redundant sensor fusion relies on sensors to deliver independent information of a shared target. In this example of a redundant system, if two cameras are recording the same object, the field where both overlap is referred to as the redundant sensing field. If it is a case of competitive sensing, each sensor measures the same properties. If the sensors are cameras, they will share the same view. Competitive sensing can be done using two techniques: fusing data from different sensors; or from a single sensor measured at different points in time.

Humans – the prime example of sensor fusion

To better understand sensor fusion, let us consider a familiar example: a human. We have five senses, hearing, sight, smell, taste, and touch. Each provides different sensory information about our environment. This sensory input travels via our peripheral nervous system (PNS) to our brain “microcontroller”. The brain produces a physical reaction. The PNS transmits sensory information but does not react. For instance, you hear a car and then see it speeding toward you, your brain commands your muscles to move out of the way to avoid an accident.

A similar principle is applied to smart sensing technology. Integrating data from multiple sensors creates more accurate and effective sensing. Sensor fusion can lead to higher levels of detection and allow these technologies to respond suitably.

Sensor Fusion in a Biomedical Setting

Sensors have become miniaturized, less invasive, and less expensive. They can be easily implanted or worn without much disturbance. It has also become more common to see medical electronics equipped with sensors that contribute to a better patient experience.

Intelligent sensor systems allow us to obtain all kinds of information. Sensors such as ECG, EMG, thermometers, gyroscopes, accelerometers and magnetometers are being used in many wearable medical devices for patient gait monitoring and rehabilitation.

Faulty sensors or incorrect placement of sensors still generate data, but such data is erroneous and does not produce the insights we require. In the biomedical field, this is a problem that can result in incorrect diagnosis by physicians or ineffective rehabilitation efforts for patients. Sensor fusion does not always solve the problem of sensor failure, but it helps us detect this failure earlier, so that the faulty sensor can be adjusted or replaced to deliver more effective results.

Sensor Fusion Applied to Navigation

Sensor fusion is applied to anything that depends on object detection, obstacle avoidance, and navigation. A simple example is your mobile phone, which provides accurate indoor and outdoor location by using GPS, accelerometers, gyroscopes, and compass data.

Sensor fusion plays a key role in robotics, solving challenges such as navigation, path planning, localization, and collision avoidance. Thanks to the integration of gyroscope and accelerometer data, the robot can sense its environment, understanding its origin, the execution path and where it should return upon completion of the task.

In autonomous vehicles, sensor fusion emulates sensory input that a human driver would confront and react to while on the road. There are GNSS (satellite/location), inertial measurement units, or IMUs (motion), camera (imaging), radar (speed) and LiDAR (distance) sensors.

On their own, any one of these sensors would provide useful data, but when combined, we get a more complete, reliable view of what is going on around the vehicle at any given time.

If a vehicle uses sensor fusion to merge multiple sensors, perception could be improved by taking advantage of overlapping fields of view. For example, with multiple radars observing a vehicle’s surroundings, multiple sensors will detect objects simultaneously. Fusing IMU data with other ADAS systems increases the reliability of object detection around the vehicle and yields a more accurate representation of the environment.

Sensor Fusion in Civil Engineering

A digital twin is a virtual representation of a physical system that can be used for simulation, analysis, and control. The effectiveness of such a system depends on the quality and completeness of the data used to build it. Sensor fusion plays a critical role by providing a more complete and accurate representation of the physical system. In civil engineering, digital twins play several important roles from design optimization to analyzing emergency responses.

Digital twins can be put into play to simulate and analyze various design options, which help engineers to create the best options for sustainability, performance and costs. They also contribute to construction planning, as they provide a virtual representation of the construction site. This allows engineers to visualize the construction process, identify potential difficulties and adjust the construction schedule. In addition, they provide a model of infrastructure assets so that conditions monitoring, and maintenance can be planned and carried out on an optimized schedule.

Digital twins are also used to simulate and evaluate the impact of different emergency scenarios, such as earthquakes, fires, and floods. In this way, engineers can better recognize potential hazards and implement effective response plans.

How Sensor Fusion Compensates for the Weaknesses

As we work to enhance human tasks with technology, it’s crucial to enable sensors to behave similarly to humans by using all available senses to process and react to stimuli.

Sensor fusion allows this by applying software algorithms to merge data from multiple sensors to build a comprehensive and precise model of the environment. This approach overcomes the limitations of individual sensors and eliminates the limitations of single-sensor technology that lacks complete information.

With sensor fusion, the information processing results will be clearer, the decision-making process more accurate, and the final decisions will better reflect the actual situation.

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