Set-membership PHD filter
MetadataShow full item record
The paper proposes a novel Probability Hypothesis Density (PHD) filter for linear system in which initial state, process and measurement noises are only known to be bounded (they can vary on compact sets, e.g., polytopes). This means that no probabilistic assumption is imposed on the distributions of initial state and noises besides the knowledge of their supports. These are the same assumptions that are used in set-membership estimation. By exploiting a formulation of set-membership estimation in terms of set of probability measures, we derive the equations of the set-membership PHD filter, which consist in propagating in time compact sets that include with guarantee the targets' states. Numerical simulations show the effectiveness of the proposed approach and the comparison with a sequential Monte Carlo PHD filter which instead assumes that initial state and noises have uniform distributions.
Showing items related by title, author, creator and subject.
Oliver, Bobbie (2014)Falling membership numbers and declining union density are issues of concern for many Australian unions. Australian Bureau of Statistics figures show that between 2005 and 2008, trade union membership declined from 22.4% ...
Mullins, Ben; Mead-Hunter, Ryan; Pitta, R.; Kasper, G.; Heikamp, W. (2014)The evolution of pressure drop, drainage rate, saturation, and efficiency of combined philic, and phobic oil mist filters in real-time are examined. The experiments used four different filter configurations, with a ...
Hong Yoon, J.; Kim, Du Yong; Yoon, K. (2012)In this paper, we propose a novel implementation of the probability hypothesis density (PHD) filter based on the sequential Monte Carlo (SMC) method called SMC-PHD filter. The SMC-PHD filter is analogous to the sequential ...