Multi‐target trackers using cubature Kalman filter for Doppler radar tracking in clutter

A major feature of the Gaussian mixture probability hypothesis density (GM‐PHD) filter is that it does not require any measurement‐to‐track association to complete its update step. This, according to the authors, should constitute significant advantage over conventional data‐association based methods, especially in presence of high false‐alarm rate, frequent miss‐detections and targets in close proximity. To test this hypothesis, a multi‐target tracking (MTT) problem using Doppler radar is considered, where the performance of GM‐PHD algorithm is compared against six data‐association based MTT filters in aforementioned adverse tracking conditions. To handle the non‐linearity due to Doppler, cubature Kalman filter (CKF) is used in the framework of all MTT algorithms. Detailed mathematical framework of a new non‐linear variant of GM‐PHD using CKF has been derived using fundamental principles of non‐linear Bayesian filtering. It is named as CK‐GM‐PHD. CK‐GM‐PHD is formulated using approximated Gaussian mixture assumption and follows track‐oriented approach. Cubature integration method is used to numerically compute mean and covariance of components in the Gaussian mixture. Simulation results support the hypothesis by revealing substantial performance improvement of CK‐GM‐PHD algorithm over conventional data‐association based approaches while tested in moderate to heavy clutter rate with lower detection probability and closely spaced target scenarios.