Abstract: Embodiments of the present invention proposes a method to automatically identify the flying targets by physical information (coordinates, heading, speed), time, and identification information (3/A code). This method includes two steps: features extraction and building machine learning model. In the features extraction step, features which are extracted include: cell indexes corresponding to coordinates, information of flight path, time in day/night format, heading, speed, and 3/A code, constructing n-dimensional vector. This vector is used as input for training a Random Forest model, to automatically identify class label of flying targets.

Abstract: Embodiments of the present invention proposes a method to automatically identify the flying targets by physical information (coordinates, heading, speed), time, and identification information (3/A code). This method includes two steps: features extraction and building machine learning model. In the features extraction step, features which are extracted include: cell indexes corresponding to coordinates, information of flight path, time in day/night format, heading, speed, and 3/A code, constructing n-dimensional vector. This vector is used as input for training a Random Forest model, to automatically identify class label of flying targets.

Abstract: Embodiments of the present invention include the different methods for data fusion from multi dissimilar sensors to reduce the noise of the tracking the 3D target in Cartesian coordinates. Accuracy of this invention is precise and more stable than the conventional methods that use geometric calculations of 2D radars to track 3D targets. The results of this invention are using the same 3D radars in the tracking system. These methods are not only implemented in existing tracking centers, but also handle the tradeoff between the data transmission capacity at the command center and the computational speed of system. This invention performs the sequential steps: determining the dynamical motion model of target, state prediction and measurement update. Wherein, the variation of steps is shown in the embodiment of this invention by the following different approaches: selective measurement; parallel filtering; state vector fusion; feedback state vector fusion; measurement fusion state vector fusion.