Online Optimization and Feedback Elman Neural Network for Maneuvering Target Tracking

2017 ◽  
Author(s):  
Liwei Xia ◽  
Ya Zhang ◽  
Huajun Liu
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Online Optimization ◽  
Maneuvering Target Tracking ◽  
Elman Neural Network ◽  
Maneuvering Target

scholarly journals Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1596 ◽  
Author(s):  
Huajun Liu ◽  
Liwei Xia ◽  
Cailing Wang
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Learning Algorithm ◽  
Simultaneous Optimization ◽  
Optimization Strategy ◽  
Maneuvering Target Tracking ◽  
Elman Neural Network ◽  
Maneuvering Target ◽  
Feedback Learning

Tracking maneuvering targets is a challenging problem for sensors because of the unpredictability of the target’s motion. Unlike classical statistical modeling of target maneuvers, a simultaneous optimization and feedback learning algorithm for maneuvering target tracking based on the Elman neural network (ENN) is proposed in this paper. In the feedback strategy, a scale factor is learnt to adaptively tune the dynamic model’s error covariance matrix, and in the optimization strategy, a corrected component of the state vector is learnt to refine the final state estimation. These two strategies are integrated in an ENN-based unscented Kalman filter (UKF) model called ELM-UKF. This filter can be trained online by the filter residual, innovation and gain matrix of the UKF to simultaneously achieve maneuver feedback and an optimized estimation. Monte Carlo experiments on synthesized radar data showed that our algorithm had better performance on filtering precision compared with most maneuvering target tracking algorithms.

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