Beijing Institute of Technology (2025), PhD. studentNow I am a fifth-year PhD student in School of Automation, Beijing Institute of Technology. I graduated from School of Xuteli, Beijing Institute of Technology with a bachelor’s degree in 2020.
I am very fortunate to be advised by Prof. Zhihong Deng and Prof. Xuan Xiao of Navigation, Guidance and Control Lab from School of Automation, Beijing Institute of Technology. My research interest includes strapdown inertial navigation system, integrated navigation, terrain matching navigation and gravity matching navigation.
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Beijing Institute of TechnologySchool of Automation
Ph.D. Student major in Navigation, Guidance and ControlSep. 2020 - present -
Beijing Institute of TechnologySchool of Xuteli
B.S. in AutomationSep. 2016 - Jul. 2020
🏆 Honors & Awards
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National Scholarship for Ph.D students 🚩 (ranked 1st)2024
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Technical Invention Award of Chinese Society of Inertial Technology (First Prize🥇, ranked 8th)2022
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APMCM Mathematical Modeling (First Prize🥇, TOP 4, ranked 1st)2022
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Huawei Cup Mathematical Modeling (Third Prize, ranked 1st)2021
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MathorCup Mathematical Modeling (Third Prize, ranked 1st)2021
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Outstanding graduate of School of Xuteli2020
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The Artificial Intelligence Challenge of Robomaster, perception (Excellence Award, ranked 1st)2020
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NXP National College Student Smart Car Competition (Second Prize in North China, ranked 1st)2020
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China Undergraduate Mathematical Contest in Modeling (Second Prize in Beijing, ranked 1st)2019
📚 Service
- Reviewer of IEEE Sensors Journal
News
Selected Publications
(view all ) 
Adaptive Point Mass Filter and Its Application in Terrain Matching Navigation
Shengwu Zhao, Zhihong Deng#, Wenzhe Zhang, Yu Wang (# corresponding author)
IEEE Transactions on Instrumentation and Measurement (IEEE TIM) 2025 Journal
Terrain matching algorithm is the core part of terrain-aided inertial navigation system (INS), and point mass filter (PMF) is often used as a terrain matching algorithm. In PMF, the construction method and calculation amount of the point mass set are important parts which affect the accuracy. To comprehensively consider the construction of point mass set and calculation amount, this article proposes an adaptive construction method of point mass set based on probability distribution. According to the characteristics of the probability distribution to be approximated, the method transforms the preset point mass set to obtain point mass set of the current moment. Aiming at the problem of calculation error, which is caused by the process noise intensity being smaller than the resolution of the point mass set, a local convolution method is proposed in this article, in which the convolution result is obtained by subdividing the local area near the point mass set. Finally, the framework of the proposed adaptive PMF (APMF) in terrain matching is given. Numerical simulations show the superiority of the proposed APMF, and the experiment shows the effectiveness of the proposed method in terrain-aided INS.
Adaptive Point Mass Filter and Its Application in Terrain Matching Navigation
Shengwu Zhao, Zhihong Deng#, Wenzhe Zhang, Yu Wang (# corresponding author)
IEEE Transactions on Instrumentation and Measurement (IEEE TIM) 2025 Journal
Terrain matching algorithm is the core part of terrain-aided inertial navigation system (INS), and point mass filter (PMF) is often used as a terrain matching algorithm. In PMF, the construction method and calculation amount of the point mass set are important parts which affect the accuracy. To comprehensively consider the construction of point mass set and calculation amount, this article proposes an adaptive construction method of point mass set based on probability distribution. According to the characteristics of the probability distribution to be approximated, the method transforms the preset point mass set to obtain point mass set of the current moment. Aiming at the problem of calculation error, which is caused by the process noise intensity being smaller than the resolution of the point mass set, a local convolution method is proposed in this article, in which the convolution result is obtained by subdividing the local area near the point mass set. Finally, the framework of the proposed adaptive PMF (APMF) in terrain matching is given. Numerical simulations show the superiority of the proposed APMF, and the experiment shows the effectiveness of the proposed method in terrain-aided INS.
Terrain Matching Algorithm Based on Trajectory Reconstruction and Correlation Analysis of Sliding Measurement Sequence
Shengwu Zhao, Zhihong Deng#, Qingzhe Wang, Wenzhe Zhang, Xun Gong (# corresponding author)
IEEE/ASME Transactions on Mechatronics (IEEE TMECH) 2024 Journal
Single-point matching algorithm (point mass filter or particle filter) only uses the current time measurement to calculate the likelihood, which is prone to pseudopeak and false peak. In order to solve the problem, this article introduces the sequence correlation analysis into the single point matching algorithm, and uses the sliding measurement sequence to estimate recursively. First, a position sequence estimation method based on trajectory reconstruction is proposed, which calculates the new position sequence by the relationship between INS displacement and heading angle, instead of the direct translation of INS trajectory method in traditional algorithms. After that, the likelihood of the candidate point is calculated by the correlation analysis method using the corresponding sliding measurement sequence at the current time, and a more accurate position estimation is obtained after the measurement update. Simulation and experiments show that the position sequence obtained by the proposed method based on trajectory reconstruction is more accurate than that obtained by the direct translation inertial navigation method. Compared with only using single time measurement information, the likelihood calculation method based on correlation analysis of sliding measurement sequence can significantly reduce pseudopeak and false peak, and the positioning accuracy of terrain matching is improved.
Terrain Matching Algorithm Based on Trajectory Reconstruction and Correlation Analysis of Sliding Measurement Sequence
Shengwu Zhao, Zhihong Deng#, Qingzhe Wang, Wenzhe Zhang, Xun Gong (# corresponding author)
IEEE/ASME Transactions on Mechatronics (IEEE TMECH) 2024 Journal
Single-point matching algorithm (point mass filter or particle filter) only uses the current time measurement to calculate the likelihood, which is prone to pseudopeak and false peak. In order to solve the problem, this article introduces the sequence correlation analysis into the single point matching algorithm, and uses the sliding measurement sequence to estimate recursively. First, a position sequence estimation method based on trajectory reconstruction is proposed, which calculates the new position sequence by the relationship between INS displacement and heading angle, instead of the direct translation of INS trajectory method in traditional algorithms. After that, the likelihood of the candidate point is calculated by the correlation analysis method using the corresponding sliding measurement sequence at the current time, and a more accurate position estimation is obtained after the measurement update. Simulation and experiments show that the position sequence obtained by the proposed method based on trajectory reconstruction is more accurate than that obtained by the direct translation inertial navigation method. Compared with only using single time measurement information, the likelihood calculation method based on correlation analysis of sliding measurement sequence can significantly reduce pseudopeak and false peak, and the positioning accuracy of terrain matching is improved.
Gravity Matching Algorithm Based on Backtracking for Small Range Adaptation Area
Shengwu Zhao, Xuan Xiao, Xuan Pang, Yu Wang, Zhihong Deng# (# corresponding author)
IEEE Transactions on Instrumentation and Measurement (IEEE TIM) 2023 Journal
The existing gravity matching algorithms are affected either by gravity measurement error or by the initial position of inertial navigation system (INS). Filter algorithms can solve the problem under the condition of enough measurement data. However, the range of most adaptation areas is small. Due to the long matching period, the available measurement data may be not enough to make the filter converge. Aiming to obtain high-precision position information in the small range adaptation area, backtracking strategies that combine the filter algorithm are first proposed in this article. Next, the observability of gravity-aided navigation system is also analyzed based on graph analysis. Furthermore, the reverse error equations are obtained by analogy corresponding to the reverse solution, and the relationship between forward navigation and reverse navigation is also given. The results of simulation and marine experiment show that the proposed algorithm is superior to the existing gravity matching algorithms, and has a high positioning accuracy in the small range adaptation area.
Gravity Matching Algorithm Based on Backtracking for Small Range Adaptation Area
Shengwu Zhao, Xuan Xiao, Xuan Pang, Yu Wang, Zhihong Deng# (# corresponding author)
IEEE Transactions on Instrumentation and Measurement (IEEE TIM) 2023 Journal
The existing gravity matching algorithms are affected either by gravity measurement error or by the initial position of inertial navigation system (INS). Filter algorithms can solve the problem under the condition of enough measurement data. However, the range of most adaptation areas is small. Due to the long matching period, the available measurement data may be not enough to make the filter converge. Aiming to obtain high-precision position information in the small range adaptation area, backtracking strategies that combine the filter algorithm are first proposed in this article. Next, the observability of gravity-aided navigation system is also analyzed based on graph analysis. Furthermore, the reverse error equations are obtained by analogy corresponding to the reverse solution, and the relationship between forward navigation and reverse navigation is also given. The results of simulation and marine experiment show that the proposed algorithm is superior to the existing gravity matching algorithms, and has a high positioning accuracy in the small range adaptation area.
An Improved Particle Filter Based on Gravity Measurement Feature in Gravity-Aided Inertial Navigation System
Shengwu Zhao, Xuan Xiao#, Yu Wang, Zhihong Deng (# corresponding author)
IEEE Sensors Journal (IEEE JSEN) 2022 Journal
The existing gravity matching algorithms are affected by the initial position error of the inertial navigation system (INS), the gravity measurement error, and the similarity of the gravity background map. Aiming at the above problems, an improved particle filter based on the gravity measurement feature (IPFBGMF) is proposed in this article. In the IPFBGMF, both the value and change characteristic of gravity measurements are considered, and a novel position acquisition method based on the gravity measurement feature is proposed, which can reduce the influence of the initial position error of INS. In addition, a new concept called direction measurement using the heading angle of INS is proposed to optimize the weight of particles in the PF. The PF with direction measurement can reduce the influence of the gravity measurement error and the similarity of the gravity background map. Furthermore, the robustness of the improved PF with the precise position is proven. Finally, a navigation strategy is designed to apply the proposed algorithms. Simulations show that IPFBGMF has the highest positioning accuracy compared with the traditional gravity matching algorithms.
An Improved Particle Filter Based on Gravity Measurement Feature in Gravity-Aided Inertial Navigation System
Shengwu Zhao, Xuan Xiao#, Yu Wang, Zhihong Deng (# corresponding author)
IEEE Sensors Journal (IEEE JSEN) 2022 Journal
The existing gravity matching algorithms are affected by the initial position error of the inertial navigation system (INS), the gravity measurement error, and the similarity of the gravity background map. Aiming at the above problems, an improved particle filter based on the gravity measurement feature (IPFBGMF) is proposed in this article. In the IPFBGMF, both the value and change characteristic of gravity measurements are considered, and a novel position acquisition method based on the gravity measurement feature is proposed, which can reduce the influence of the initial position error of INS. In addition, a new concept called direction measurement using the heading angle of INS is proposed to optimize the weight of particles in the PF. The PF with direction measurement can reduce the influence of the gravity measurement error and the similarity of the gravity background map. Furthermore, the robustness of the improved PF with the precise position is proven. Finally, a navigation strategy is designed to apply the proposed algorithms. Simulations show that IPFBGMF has the highest positioning accuracy compared with the traditional gravity matching algorithms.