Zihan Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Zihan Zhang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Bo Wang
School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
2 shared publications - Mengyin Fu
School of Automation, Beijing Institute of Technology, Beijing, China
2 shared publications - Zhihong Deng
School of Automation, Beijing Institute of Technology, Beijing, China
2 shared publications Wei Xiao
School of Automation, Beijing Institute of Technology, Beijing, China
1 shared publication
Latest Publications
- 14 May 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Sensors Journal
- Vol. 26 (12) , 19382-19394
Nested Preanalyzed and Real-Time Corrected Matching Area Selection Method for Underwater Gravity-Aided Inertial Navigation
Gravity-aided inertial navigation system (GAINS) is an effective passive navigation method, which represents significant progress in underwater navigation. The matching area selection algorithm is one of the key techniques. Distinctive from conventional methods, in this article, a nested method for matching area selection is proposed, which establishes a joint observation of gravity anomalies at the current positions and auxiliary positions to obtain more abundant observational information. Comprehensively considering factors such as sensors, gravity anomaly map, and trajectory, etc., this method measures the matching capacity from a system perspective rather than the 2-D image characteristics, and quantitatively estimates the matching biases. The possible maneuvering navigation and other changes of trajectory and attitude angles are focused on, and a preanalysis and real-time correction method is designed accordingly. Experimental results show that the proposed method can select matching area more accurately and efficiently, and makes it easier to set the matching area selection criteria than conventional algorithms.
- 14 May 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Sensors Journal
- Vol. 26 (12) , 19382-19394
Nested Preanalyzed and Real-Time Corrected Matching Area Selection Method for Underwater Gravity-Aided Inertial Navigation
Gravity-aided inertial navigation system (GAINS) is an effective passive navigation method, which represents significant progress in underwater navigation. The matching area selection algorithm is one of the key techniques. Distinctive from conventional methods, in this article, a nested method for matching area selection is proposed, which establishes a joint observation of gravity anomalies at the current positions and auxiliary positions to obtain more abundant observational information. Comprehensively considering factors such as sensors, gravity anomaly map, and trajectory, etc., this method measures the matching capacity from a system perspective rather than the 2-D image characteristics, and quantitatively estimates the matching biases. The possible maneuvering navigation and other changes of trajectory and attitude angles are focused on, and a preanalysis and real-time correction method is designed accordingly. Experimental results show that the proposed method can select matching area more accurately and efficiently, and makes it easier to set the matching area selection criteria than conventional algorithms.
- 10 April 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Sensors Journal
- Vol. 25 (10) , 18543-18552
An Analytic Quantification Method of Matching Accuracy Based on Particle Filter in Gravity-Assisted Inertial Navigation
Gravity-aided inertial navigation system (GAINS) represents a crucial advancement in underwater navigation. And the matching areas selection algorithm is one of the key techniques. Different from previous researches, Estimated Overall Matching Biases (EOMBs), new indexes for matching areas selection, are proposed, and consider the matching accuracy to be the capability of system rather than the feature of gravity anomaly map itself. The key factors affecting EOMBs are analyzed, such as gyro drifts, accelerometer biases, velocity, initial positioning errors, overall standard deviation of gravity anomaly observations, and so on. Taking these conditions as a prior information, the method to calculate EOMBs, which are able to quantify gravity matching precision based on particle filter (PF), is proposed. The simulation results and practical tests show that the proposed method can select matching areas more accurately and efficiently than conventional algorithms. And the threshold is easy to set because it is directly related to matching accuracy.
- 10 April 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Sensors Journal
- Vol. 25 (10) , 18543-18552
An Analytic Quantification Method of Matching Accuracy Based on Particle Filter in Gravity-Assisted Inertial Navigation
Gravity-aided inertial navigation system (GAINS) represents a crucial advancement in underwater navigation. And the matching areas selection algorithm is one of the key techniques. Different from previous researches, Estimated Overall Matching Biases (EOMBs), new indexes for matching areas selection, are proposed, and consider the matching accuracy to be the capability of system rather than the feature of gravity anomaly map itself. The key factors affecting EOMBs are analyzed, such as gyro drifts, accelerometer biases, velocity, initial positioning errors, overall standard deviation of gravity anomaly observations, and so on. Taking these conditions as a prior information, the method to calculate EOMBs, which are able to quantify gravity matching precision based on particle filter (PF), is proposed. The simulation results and practical tests show that the proposed method can select matching areas more accurately and efficiently than conventional algorithms. And the threshold is easy to set because it is directly related to matching accuracy.