Combining Data from a Variety of Sensors
When using data from different sensors and combining their individual and complementary characteristics, the detection of relevant objects becomes more accurate and reliable. Such fully or partially redundant systems are specifically required for safety-related systems. We at Intenta successfully put this know-how into the development of our smart sensors and software.
- Stochastic filtering (e.g., Kalman filter)
- Multi-sensor/Multi-object tracking
- Grid-based clearance estimation (e.g., Evidence Theory)
Our Work Environments
- MATLAB / Simulink
- Vector CANLog