The Multiple Model Labeled Multi-Bernoulli Filter

In many applications, multi-object tracking algorithms are either required to handle different types of objects or rapidly maneuvering objects. In both cases, the usage of multiple motion models is essential to obtain excellent tracking results. In the field of random finite set based tracking algorithms, the Multiple Model Probability Hypothesis Density (MM-PHD) filter has recently been applied to tackle this problem. However, the MM-PHD filter requires error-prone post-processing to obtain target tracks and its cardinality estimate is fluctuating. The Labeled Multi-Bernoulli (LMB) filter is an accurate and computationally efficient approximation of the multi-object Bayes filter which provides target tracks. In applications using only a single motion model, LMB filter has been shown to significantly outperform the PHD filter. In this contribution, the Multiple Model Labeled Multi-Bernoulli (MM-LMB) filter is proposed. The MM-LMB filter is applied to scenarios with rapidly maneuvering objects and its performance is compared to the single model LMB filter using simulated data.