Particle filter with one-step randomly delayed measurements and unknown latency probability

Abstract

In this paper, a new particle filter is proposed to solve the nonlinear and non-Gaussian filtering problem when measurements are randomly delayed by one sampling time and the latency probability of the delay is unknown. In the proposed method, particles and their weights are updated in Bayesian filtering framework by considering the randomly delayed measurement model, and the latency probability is identified by maximum likelihood criterion. The superior performance of the proposed particle filter as compared with existing methods and the effectiveness of the proposed identification method of latency probability are both illustrated in two numerical examples concerning univariate non-stationary growth model and bearing only tracking.

Keywords:

• state estimation
• particle filter
• randomly delayed measurements
• identification of latency probability
• maximum likelihood criterion

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 61001154], [grant number 61201409], [grant number 61371173]; China Postdoctoral Science Foundation [grant number 2013M530147], [grant number 2014T70309]; Heilongjiang Postdoctoral Fund [grant number LBH-Z13052], [grant number LBH-TZ0505]; Fundamental Research Funds for the Central Universities of Harbin Engineering University [grant number HEUCFX41307].

Notes on contributors

Yonggang Zhang

Yonggang Zhang received his BS and MS degrees from the Department of Automation, Harbin Engineering University, Harbin, China, in 2002 and 2004, respectively. He received his PhD degree in electronic engineering from Cardiff University, UK in 2007 and worked as a post-doctoral fellow at Loughborough University, UK from 2007 to 2008 in the area of adaptive signal processing. Currently, he is a professor of navigation, guidance and control in Harbin Engineering University (HEU) in China. His current research interests include signal processing, information fusion and their applications in navigation technology, such as fibre optical gyroscope, inertial navigation and integrated navigation.

Yulong Huang

Yulong Huang received his BS degree from the Department of Automation, Harbin Engineering University, Harbin, China, in 2012, and is currently pursuing a PhD degree in control science and engineering. His current research interests include state estimation, system identification and information fusion.

Ning Li

Ning Li received her BS and MS degrees from the Department of Automation, Harbin Engineering University (HEU), Harbin, China, in 2002 and 2005, respectively. She received her PhD degree in Navigation, guidance and control from HEU in 2009. Currently, she is an associate professor in Harbin Engineering University in China. Her current research interests include integrated navigation and adaptive filtering theory.

Lin Zhao

Lin Zhao received his BS and MS degrees from the Department of Automation, Harbin Shipbuilding Engineering Institute, Harbin, China, in 1989 and 1992, respectively. He received his PhD degree from the School of Astronautics, Harbin Institute of Technology, Harbin, in 1995. He was with the College of Automation, Harbin Engineering University, Harbin, in 1992. He worked as a post-doctoral fellow at the Imperial College London, UK, from 2001 to 2003. Currently, he is a professor of navigation, guidance and control in Harbin Engineering University. His current research interests include global positioning system navigation, integrated navigation, control theory, information processing and computer simulation.