Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member 论文
2015IEEE Transactions on Aerospace and Electronic Systems引用 355
Target Tracking and Data Fusion in Sensor NetworksAnomaly Detection Techniques and ApplicationsWater Systems and Optimization
摘要
Recent developments in random finite sets (RFSs) have yielded a variety of tracking methods that avoid data association. This paper derives a form of the full Bayes RFS filter and observes that data association is implicitly present, in a data structure similar to multiple hypothesis tracking (MHT). Subsequently, algorithms are obtained by approximating the distribution of associations. Two algorithms result: one nearly identical to joint integrated probabilistic data association (JIPDA), and another related to the multiple target multi-Bernoulli (MeMBer) filter. Both improve performance in challenging environments.