Centralized tracking with multiple sensors involves fusing the measurements from different sensors into a common state representation and updating a single track for the target. Here's a general outline of how you can achieve centralized tracking using trackerGNN, trackerJPDA, and trackerMHT with multi-sensor data:
- Sensor Measurements and Fusion:
- Obtain measurements from your camera and radar sensors. These measurements will be in their respective sensor frames.
- Implement a sensor fusion algorithm to convert the measurements from different sensors into a common world coordinate frame. This process is called "sensor fusion" or "data association."
- The goal is to establish correspondences between measurements from different sensors and associate them with the same target. Various techniques like Kalman filters, data association algorithms (e.g., nearest neighbor, Hungarian algorithm), or probabilistic methods (e.g., Joint Probabilistic Data Association - JPDA) can be used for this purpose.
- Centralized Tracking:
- Implement your selected centralized tracking algorithm (e.g., trackerGNN, trackerJPDA, trackerMHT) using the fused measurements in the common world coordinate frame.
- These algorithms use the fused measurements to update the target track's state and perform data association, as necessary, to handle sensor uncertainties and associations.
- Measurement Function and Conversion:
- In your tracking Extended Kalman Filter (EKF) implementation, you'll have a measurement function that converts the predicted state to a measurement in the sensor's frame.
- Since your tracks' state is in the world coordinate frame, you need to convert the measurements from the camera and radar sensors to the world coordinate frame using the converted measurement model. This involves transforming the sensor measurements from their respective frames to the world coordinate frame.
- The converted measurements in the world coordinate frame will then be used for data association and track updates in the centralized tracking algorithm.
- State and Measurement Covariance:
- When fusing measurements from multiple sensors, it's crucial to consider the covariance of both the state and measurements.
- The covariance reflects the uncertainty associated with the sensor measurements and the track's state, and it plays a crucial role in the data association process.
In summary, to achieve centralized tracking with multi-sensor data, you need to properly fuse the measurements from different sensors into a common world coordinate frame and then use these fused measurements in your centralized tracking algorithms like trackerGNN, trackerJPDA, or trackerMHT. Your converted measurements in the world coordinate frame will be used for the data association and track updates within the centralized tracker.
