基于学习策略的多速率多传感器融合定位方法

doi:10.7527/S1000-6893.2022.26904

Abstract

Abstract: This paper concerned with a class of problems of moving target tracking with unknown target motion model and multi-sensor asynchronous sampling. A data-driven target tracking algorithm is proposed， which only relies on measurement information. To solve the problem of the unknown motion model， a distributed neural network structure is designed based on the measurement model and measurement range， then the mapping relationship between the observation data and the state variables is established based on the designed neural network. A compensation strategy based on the measurement data at the last sampling instant is introduced to solve the multi-rate and multi-sensor sampling asynchronous problem. We also construct a weight network model with time difference as the input feature to estimate the real target position by iterative learning. An experiment is given to show the superiority and effectiveness of the proposed method.

Key words: target tracking, multi-sensor fusion, neural network, multi-rate sampled-data, data driven

CLC Number:

V219
TP391

CHEN Bo, YUE Kai, WANG Rusheng, HU Mingnan. Learning-based multi-rate multi-sensor fusion localization method[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(S1): 726904-726904.

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Learning-based multi-rate multi-sensor fusion localization method

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