Efficient box particle implementation of the multi-sensor GLMB filter in the presence of triple measurement uncertainty

Abstract The multi-sensor generalized labeled multi-Bernoulli (MSGLMB) filter is a tractable solution to multi-sensor multi-target tracking problems. The particle MSGLMB filter, which is time-consuming, can be used for the situation where some sensors return point measurements, while the others return box measurements. The box particle MSGLMB filter, which is potentially time-saving, can also be used for the situation if the point measurements are first transformed into boxes by their three-sigma supports. However, such box particle MSGLMB filter can be inefficient due to the rapidly multiplying contracted versions of box particles during multi-sensor update especially when the number of sensors or measurements therefrom is large. To reduce the inefficiency, we seek to reduce the number of contracted versions by processing point measurements directly with a new likelihood, namely the likelihood of a point measurement given a box particle, which does not require point measurements to contract box particles. Although an exact form of the said likelihood is unobtainable, two crude approximations of it are derived and studied, based on which an efficient box particle implementation for the situation is contributed. Simulation results prove that the proposed approach is effective.