Multi-sensor tracking with non-overlapping field for the GLMB filter
Weifeng Liu, Yimei Chen, Hailong Cui, Quanbo Ge · 2017
In this paper, we consider multi-sensor with non-overlapping radar field of view in the framework of labeled random finite sets (L-RFS). In this case, a target may be simultaneously observed by some of the sensors, or even none sensor. It is different from the existing assumption of all sensors with the same fields in tracking community. We first describe the field of view by modeling the detection of probability of individual sensors. Then, a multi-sensor measurement-driven of birth model is proposed. We solve this problem by using the generalized labeled multi-Bernoulli (GLMB) filter. In the final simulation, a three-target & three-sensor is given to verify the effectiveness of the proposed algorithm.