Abstract:

In order to detect, track, and recognize multitarget jointly by fusion multiple dissimilar sensors in the airborne warning system, the theoretical models and processing framework for multitarget joint detection, tracking and recognition of dissimilar sensors are studied based on the random finite set theory. By describing the single target’s kinematics states and recognition attribute states unifiedly, the multitarget states are modeled as a global state that is described by the random finite set. By analyzing the models of a kinematic sensor and an attribute sensor, the dissimilar sensors are modeled as a global sensor, and the measurements of those dissimilar sensors are modeled as a global measurement. Based on the models of global state and global measurement, the process of multitarget detection, tracking and recognition of dissimilar sensors are described by Bayes filtering, and the structure of multitarget recognition of dissimilar sensors fusion is established. Simulation results suggest that the proposed models and processing framework are executable and effective.