The new mathematical framework, which automates the sensor selection decision-making process, can help address issues such as aircraft crashes caused by sensor failures.
"During the early design stage for any control system, critical decisions have to be made about which sensors to use and where to place them so that the system is optimized for measuring certain physical quantities of interest," says Dr. Raktim Bhattacharya, associate professor in the Department of Aerospace Engineering. "With our mathematical formulation, engineers can feed the model with information on what needs to be sensed and with what precision, and the model's output will be the fewest sensors needed and their accuracies."
Complex systems such as cars or aircraft have internal properties that need to be measured. For instance, in an airplane, sensors for angular velocity and acceleration are placed at specific locations to estimate the velocity.
Sensors also have different accuracies - measured by the noise in the sensor measurements. However, accuracies may be defined differently depending on the system and the application. For instance, say the researchers, some systems may require that noise in the predictions do not exceed a certain amount, while others may need the square of the noise to be as small as possible. In all cases, prediction accuracy has a direct impact on the cost of the sensor.
"If you want to get sensor accuracy that is two times more accurate, the cost is likely to be more than double," says Bhattacharya. "Furthermore, in some cases, very high accuracy is not even required. For example, an expensive 4K HD vehicle camera for object detection is unnecessary because first, fine features are not needed to distinguish humans from other cars and second, data processing from high-definition cameras becomes an issue."
Even if the sensors are extremely precise, say the researchers, knowing where to put the sensor is critical because one might place an expensive sensor at a location where it