Adaptive δ-Generalized Labeled Multi-Bernoulli Filter for Multi-Object Detection and Tracking

The delta-generalized labeled multi-Bernoulli (delta-GLMB) filter is an efficient approach for multiobject tracking in case of high clutter density and low detection probability. However, the formulation of the original delta-GLMB filter requires that the birth delta-GLMB filtering density is known a priori. It is inapplicable for the birth object appearing from unknown positions. To address this problem, an adaptive delta-GLMB filter is proposed to detect and track the birth objects with unknown position information. This adaptive filter establishes the birth delta-GLMB filtering density by using measurements at previous three successive times. Simulation results indicate that the proposed adaptive delta-GLMB filter may efficiently detect and track the multiple objects with unknown positions. Simulation results also demonstrate that the proposed adaptive ffi-GLMB filter performs better than the other existing adaptive filters.