Exact joint estimation of registration error and target states based on GLMB filter
Minzhe Li, Zhongliang Jing, Miaomiao Hu, Peng Dong · 2017
In this paper, a new centralized algorithm is developed to estimate the registration error and target states jointly based on the generalized labeled multi-Bernoulli (GLMB) filter. The bias pseudo-measurements are calculated with the tracks generated by the GLMB filter. Then, the bias estimates are computed to compensate the measurements for multi-target tracking. Since the estimates of the sensor biases and target states are calculated exactly with no approximations, the estimation performance of the proposed algorithm is better than the method based on the probability hypothesis density (PHD) filter. The effectiveness and superiority of the proposed algorithm are verified by numerical simulations.