A Multiple Extended Target Multi-Bernouli Filter Based on Star-convex Random Hypersurface Model

Considering the tracking of multi-extended target with irregular shape in complicated and uncertain environment, this paper proposes a multi-extended target multi-Bernoulli filtering algorithm based on star-convex random hypersurface model (RHM). First, in the framework of finite set statistics (FISST), the multi-Bernoulli random finite set (MBer-RFS) and Poisson-RFS are used to model multi-extended target state and measurement respectively, and then the extended target cardinality balanced multi-target multi-Bernoulli (ET-CBMeMBer) filter is given. Subsequently, using RHM to represent the measurement source distribution of any star-convex extended target, this paper proposes the cubature Kalman Gaussian mixture Star-convex multi-extended target multi-Bernoulli filter. Besides, this paper also gives a performance metric which can evaluate the irregular shape estimation of multi-extended target. Finally, the effectiveness of the proposed method is verified by the tracking simulations of multi-extended target and multi-group target with sudden shape change. Copyright © 2020 Acta Automatica Sinica. All rights reserved.