Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities

Feihu Zhang,Le Li,Guang Pan,Lichuan Zhang

doi:10.1109/access.2019.2963445

Abstract

The key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association. However, traditional approaches suffer both the computational time and accuracy issues. Here, an improved algorithm under Random Finite Set statistics (RFSs) is proposed, in which the Probability Hypothesis Density (PHD) filter is utilized to address the aforementioned issues, by jointly estimating both the number of vessels and the corresponding states in complex environments. Furthermore, to avoid the prior requirement constrain with respect to the PHD filter, the pattern recognition method is simultaneously utilized to calculate the birth intensities. Simulation results exhibit the proposed approach performs better than normal PHD for multiple vessel cooperative localization, in scenarios of unknown birth intensity.

Highlights

Cooperative localization plays an important role for tasks of safety navigation [1], [2]
The performance of multiple vessel cooperative localization has been significantly improved, which is guaranteed by information sharing from both proprioceptive and exteroceptive sensors [29]
This paper extends our previous work for applying Probability Hypothesis Density (PHD) filter and point matching method to jointly estimate both states and corresponding parameters

Summary

INTRODUCTION

Cooperative localization plays an important role for tasks of safety navigation [1], [2]. Classical tracking methods have been extensively studied and considered as a divide-and-conquer strategy: first calculate the measurement-to-target association, and use filtering technologies to estimate the states [12]–[14]. F. Zhang et al.: Multiple Vessel Cooperative Localization Under RFS Framework With Unknown Birth Intensities. The PHD filter is proposed to solve cooperative issues, preliminary conditions are required, such as the knowledge of the birth intensity (regions of new targets). This is quite challenging to acquire in practice [23].

BACKGROUND

POINT MATCHING ALGORITHM

IMPLEMENTATION DETAILS

SIMULATION

CONCLUSION