Entomological radar is an effective means of monitoring insect migration, and can realize long-distance and large-scale rapid monitoring. The stable tracking of individual insect targets is the basic premise underlying the identification of insect species and the study of insect migration mechanisms. However, the complex motion trajectory and large number of false measurements decrease the performance of insect target tracking. In this paper, an insect target tracking algorithm in clutter was designed based on the multidimensional feature fusion strategy (ITT-MFF). Firstly, multiple feature parameters of measurements were fused to calculate the membership of measurements and target, thereby improving the data association accuracy in the presence of clutter. Secondly, a distance-correction factor was introduced to the probabilistic data association (PDA) algorithm to accomplish multi-target data association with a low computational cost. Finally, simulation scenarios with different target numbers and clutter densities were constructed to verify the effectiveness of the proposed method. The tracking result comparisons of the experimental data acquired from a Ku-band entomological radar also indicate that the proposed method can effectively reduce computational cost while maintaining high tracking precision, and is suitable for engineering implementation.
Design of Insect Target Tracking Algorithm in Clutter Based on the Multidimensional Feature Fusion Strategy