Target Point Tracks Optimal Association Algorithm with Surface Wave Radar and Automatic Identification System

In order to solve the problem that of High Frequency Surface Wave Radar (HFSWR) and Automatic Identification System (AIS) target point tracks fusion, a point tracks association algorithm using Jonker- Volgenant-Castanon (JVC) global optimal matching for different status is proposed. Firstly, the HFSWR and AIS target point tracks are divided into the quasi-static and dynamic data by the radial velocity. Then the radial velocity and spherical distance are selected as the feature parameters, and the different status data are respectively pre-associated by the radial velocity and spherical distance. Finally, the average of relative distance ratio is used to evaluate the effect of association. According to the selection of threshold parameter, the HFSWR and AIS point tracks are optimal associated with the JVC algorithm. The experimental results indicate that the proposed algorithm, in the condition of equal number point tracks associated, is superior to the Nearest Neighbor (NN) algorithm and Munkres association algorithm in the association accuracy, and the associate time is less than the NN algorithm and Munkres association. Moreover, three different time data gained from the target traits measured in nearly three years demonstrate that the feasibility and real-time of the proposed method.