RHM-δ-GLMB Tracking Algorithm on the Focal Plane for UAV Cluster Targets

Abstract

This paper offers a new tracking algorithm for clustered targets of focal plane unmanned aerial vehicles (UAVs) by integrating the random hypersurface model (RHM) model into the $\delta $ -generalized label multi-Bernoulli ( $\delta $ -GLMB) filter to guarantee a robust UAV cluster target tracking on such focal planes. We first investigated the infrared imaging features of UAV cluster targets by emphasizing the target imaging size and dispersion. Next, we designed an elliptical RHM-based measurement model to map the measurement, state parameters, scaling factors, and errors to pseudo-measurement “0”, followed by establishing a pseudo-measurement equation to reflect the target’s extended shape size. Then, in order to enhance measurement estimation accuracy, the RHM model was implemented with the $\delta $ -GLMB filter and Gamma-Gaussian mixture, which can perform a real-time estimate of the targets’ centroid motion state and extended state. We also used the grid-based fast density-based spatial clustering of applications with noise (DBSCAN) segmentation algorithm to overcome the distance-based segmentation method restrictions for infrared radiation (IR) measurement data and diminish the algorithm’s complexity even more in the measurement update. Extensive simulations demonstrated that this algorithm outperformed existing matching filtering algorithms in the target centroid motion and extended states. Our investigations also revealed that the algorithm was less vulnerable to clutter and more adaptive, making it more straightforward to accomplish reliable tracking of UAV cluster targets.

Keywords

• UAV cluster
• RHM model
• extended multi-target tracking
• δ-GLMB filter
• OSPA distance