A track initiation algorithm in aperiodic sparseness sampling environment

This paper proposes a novel track initiation algorithm for the passive sensor network system which sample the measurements asynchronously and aperiodicly. Firstly, the algorithm calculates the heading-angle using the azimuths and the elevations measured by the passive sensor. And then, the candidate trajectory sets are established trough the synoptic data association. Finally, the algorithm predicts the target the azimuth and the elevation, and uses the conjugate gradient method (CGM) to identify the target state vector including the positions. We have implemented simulation experiments to evaluate this algorithm's performance. Their results show that it is appropriate for engineering applications.