Skip to main navigation
Skip to search
Skip to main content
Beijing Institute of Technology Home
English
中文
Home
Profiles
Research units
Research output
Prizes
Search by expertise, name or affiliation
Noise-Adaption Extended Kalman Filter Based on Deep Deterministic Policy Gradient for Maneuvering Targets
Jiali Li, Shengjing Tang,
Jie Guo
*
*
Corresponding author for this work
School of Aerospace Engineering
Beijing Institute of Technology
Research output
:
Contribution to journal
›
Article
›
peer-review
1
Citation (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Noise-Adaption Extended Kalman Filter Based on Deep Deterministic Policy Gradient for Maneuvering Targets'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Engineering
Extended Kalman Filter
100%
Measurement Noise
100%
Process Noise
100%
Simulation Result
50%
Obtains
50%
Target Tracking
50%
Illustrates
50%
Simulation Experiment
50%
Decision Process
50%
Recursive Estimation
50%