δ‐GLMB filter based on DI in a clutter

For the problem that the performance of existing multi‐target tracking algorithm's serious degrades in a dense clutter environment, a novel Doppler information assistant δ ‐ generalised labelled multi‐Bernoulli (DI‐δ ‐ GLMB) filter is proposed. By introducing DI, a new measurement likelihood function is established, and the improved update equation based on the δ ‐ GLMB filter framework is derived. In addition, a sequential Monte Carlo implementation method is given under the non‐linear model. Simulation results show that compared with the DI probability hypothesis density filter and the standard δ ‐ GLMB filter, the estimation of the proposed algorithm is more accurate and stable.