The fusion of data from various sensors to produce a more precise, trustworthy, and contextual view of the data is known as sensor fusion. Algorithms are needed for Sensor Fusion implementations to filter and combine various data sources.
Estimation and sensor fusion are crucial components in the design of many sensors in contemporary sensor systems. Therefore, the main foci of this course are the fundamental comprehension, illustration, and applications of multiple sensor fusion techniques, basic and advanced estimate theories, and their structures, algorithms, and applications. Because of this, you should be able to choose critically and design estimates and multiple sensor fusion strategies suited for your unique challenges based on the types of sensor systems and sensor noise characteristics.
What does a certification program for sensor fusion cover?
Learn about sensor fusion engineering, one of the most important and fascinating topics of robotics. Through the Sensor Fusion training program, you will gain the skills required to track non-linear motion and objects in the surroundings. Utilise the information you learn in this program to start a career working with robots, autonomous vehicles, or another industry.
This class is ideal for you if you’re curious about combining lidar, radar, and camera data. After completing this program, you will have access to a wide range of fields because sensors and sensor data are used in many different applications, from cell phones to robotics and self-driving cars.
You will be prepared to contribute to various sectors as a Sensor Fusion Engineer and qualified for multiple positions if you complete sensor fusion training in Bangalore with placement options in various fields.
Job Positions
- Research Engineer
- Depth Engineer
- Object Tracking Engineer
- Sensor Engineer System
- Integration Engineer
- Imaging Engineer
- Sensor Fusion Engineer
- Perception Engineer
- Automated Vehicle Engineer
What you will discover
This course aims to familiarise you with the fundamentals of estimate theory and help you weigh the advantages and disadvantages of filtering and fusion theories in relation to the challenge of sensor fusion.
After passing, you’ll be able to:
- Explain the nature, function, and design processes of estimate theory and sensor fusion
- Critically comprehend complex issues with traditional estimates and sensor fusion methods.
- Based on the types of system/sensor dynamics and noise characteristics, carefully choose and apply an appropriate filtering technique and sensor fusion method to a particular situation.
The course’s primary content areas include:
- Introduction to sensor fusion and estimation theories
- Analytical statistics Expectation operator, means and variances, maximum likelihood, and Gaussian distributions
- Observers: Using outputs to match internal models, the entire state observer and reduced state observer, and the internal model checking principle
- Estimators: Adaptive Filter (IMM Filter), Linear Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, Information Filter, and Particle Filter
- Central, hierarchical, and decentralised fusion designs for sensor integration
- Combining multiple sensors, Information fusion, State-vector fusion (track-to-track fusion), and Covariance intersection.
Conclusion
Sensor Fusion Training Chennai, or from somewhere else, has many career opportunities and will teach you the necessary abilities to track non-linear motion and objects in the environment. Use the knowledge you gain in this program to launch a career in robots, autonomous vehicles, and other fields.
