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factor graph Optimization Localization Method Based on GNSS Performance Evaluation and Prediction in Complex Urban Environment

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IEEE SENSORS JOURNAL 2025年第7期25卷 12455-12465页

作者： Xu, Xiaowei Yang, Xiaolin Lyu, Pin Li, Lijuan Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211800 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

This article proposes an online global navigation satellite system (GNSS) positioning performance evaluation and position prediction method to handle the degradation of positioning accuracy due to the complex urban denial environment. A dynamic trust (DT) function is constructed by combining multiparameter metrics to dynamically filter inavailable information and optimize information utilization. An improved indirect position prediction model based on bi-directional long short-term memory (BiLSTM) and strapdown inertial navigation system toward the heading error divergence model (SINS-HEDM) is constructed to enhance the accuracy of the navigation system. In order to reduce the interference of human driving behavior on the direction information in the position, the position is decomposed into distance and direction. BiLSTM is employed to predict vehicle movement distances between adjacent moments, and SINS-HEDM is designed to compensate for heading errors in SINS. A robust factor graph optimized (FGO) fusion method is presented for achieving reliable vehicle positioning in urban GNSS-denied environments. A comparative experiment is adopted to demonstrate the superiority of the proposed method.

关键词： Global navigation satellite system Predictive models Vehicle dynamics Satellites Bidirectional long short term memory Accuracy Receivers Position measurement Performance evaluation Inertial navigation Bi-directional long short-term memory (BiLSTM) dynamic trust (DT) function factor graph heading error dispersion model vehicle position prediction

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Collaborative navigation method based on adaptive time-varying factor graph

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AERONAUTICAL JOURNAL 2025年第1333期129卷 788-801页

作者： Wang, H. Hu, L. Tao, J. Harbin Engn Univ Sch Informat & Commun Engn Harbin Peoples R China

Aiming at the problems of poor coordination effect and low positioning accuracy of unmanned aerial vehicle (UAV) formation cooperative navigation in complex environments, an adaptive time-varying factor graph framework UAV formation cooperative navigation algorithm is proposed. The proposed algorithm uses the factor graph to describe the relationship between the navigation state of the UAV fleet and its own measurement information as well as the relative navigation information, and detects the relative navigation information at each moment by the double-threshold detection method to update the factor graph model at the current moment. And the robust estimation is combined with the factor graph, and the weight function measurements are used in the construction of the factor nodes for adaptive adjustment to make the system highly robust. The simulation results show that the proposed method realises the effective fusion of airborne multi-source sensing information and relative navigation information, which effectively improves the UAV formation cooperative navigation accuracy.

关键词： factor graph cooperative navigation UAV formation double-threshold detection robust estimation

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A distributed factor graph model solving method for cooperative localization of UAV swarms

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷

作者： Yang, Pu Ye, Guo-Yang Shao, Chun-Li Yang, Shuang-Long Huang, Ze-Xia Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China Jianghuai Adv Technol Ctr Hefei 230000 Peoples R China Hefei Univ Technol Sch Elect & Automat Engn Hefei 230009 Peoples R China

Accurate position information is crucial for unmanned aerial vehicles (UAV) to execute tasks. To balance the contradiction between the payload and localization accuracy of rotary UAVs, a cooperative localization method for UAV swarms based on the factor graph model is studied in this paper. Each UAV is equipped with a local factor graph model. A distributed factor graph model-solving method, AGA-Gauss-Newton conjugate gradient (GNCG), which combines an adaptive genetic algorithm and an improved GNCG algorithm, is proposed. The issue of falling into local optimal solutions was addressed by configuring the crossover and mutation behaviors of the genetic algorithm into an adaptive mode. The Gauss-Newton algorithm (GNQR) was improved using a conjugate gradient iteration, which effectively reduced the operation time of the algorithm. The simulation results indicate that the AGA-GNCG algorithm improves the localization accuracy with respect to the East-North-Up (ENU) frame by 58.8%, 60.6%, and 57.4% relative to the GNQR. Moreover, as the number of UAVs increases, the improved GNCG algorithm exhibits a significant improvement in computational efficiency compared to that of the GNQR algorithm.

关键词： cooperative localization factor graph adaptive genetic algorithm conjugate gradient

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BLITZCRANK: factor graph Accelerator for Motion Planning 23

BLITZCRANK: Factor Graph Accelerator for Motion Planning

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Yuhui Hao Yiming Gan Bo Yu Qiang Liu Shao-Shan Liu Yuhao Zhu Tianjin University University of Rochester BeyonCa

ISBN: (纸本)9798350323481

factor graph is a graph representing the factorization of a probability distribution function and serves as a perfect abstraction in many autonomous machine computing stacks, such as planning, localization, tracking and control, which are challenging tasks for autonomous systems with real-time and energy *** this paper, we present BLITZCRANK, an accelerator for motion planning algorithms using the abstraction of a factor graph. By formulating motion planning as a factor graph inference, we successfully reduce the scale of the problem and utilize the inherent matrix sparsity. BLITZCRANK is able to realize the user-defined optimal design by finding the optimal order of the factor graph inference. With a domain specific balancing order, BLITZCRANK achieves up to 7.4× speed up and 29.7× energy reduction compared to the software implementation on Intel CPU.

关键词： factor graph

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factor graph based navigation and positioning for control system design:A review

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Chinese Journal of Aeronautics 2022年第5期35卷 25-39页

作者： Xiwei WU Bing XIAO Cihang WU Yiming GUO Lingwei LI School of Automation Northwestern Polytechnical UniversityXi’an 710072China

Navigation and positioning is an important and challenging problem in many control engineering *** provides feedback information to design controllers for *** this paper,a bibliographical review on factor graph based navigation and positioning is *** specifically,the sensor modeling,the factor graph optimization methods,and the topology factor based cooperative localization are *** navigation and positioning methods via factor graph are considered and *** in the current research of factor graph based navigation and positioning are also discussed with emphasis on its practical *** limitations of the existing methods,some solutions for future techniques,and recommendations are finally given.

关键词： factor graph Navigation Optimization methods Positioning Sensor modeling

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factor graph approach to iterative channel estimation and LDPC decoding over fading channels

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2005年第4期4卷 1345-1350页

作者： Niu, HN Shen, MY Ritcey, JA Liu, H Univ Washington Dept Elect Engn Seattle WA 98195 USA

This letter provides a comparative study of joint channel estimation and low-density parity-check decoding algorithms for flat Rayleigh fading channels based on the receiver factor graphs. Two approaches for joint channel estimation and decoding are proposed. Intensive simulation studies are carried out to evaluate the receiver sensitivity to the choice of the factor graph. Results show that when channel statistical information is not available at the receiver, a low-pass filtering approach gives a more robust and simpler solution, and it is only 0.5 dB worse than the optimum Wiener filtering. All methods have about 2 dB improvements over the noniterative receivers.

关键词： factor graph iterative receiver LDPC PSAM

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factor graph-Based Biomolecular Circuit Analysis for Designing Forward Error Correcting Biosensors

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2009年第3期3卷 150-159页

作者： Liu, Yang Chakrabartty, Shantanu Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

We previously reported the fabrication and the verification of novel biomolecular transistors where electrical conductivity of a "polyaniline nanowires" channel is controlled by antigen-antibody interactions. In this paper, we present a simulation framework for analyzing the reliability of biosensor circuits constructed by using these biomolecular transistors. At the core of the proposed framework is a library of electrical circuit models that capture the stochastic interaction between biomolecules and their variability to environmental conditions and experimental protocols. Reliability analysis is then performed by exploiting probabilistic dependencies between multiple circuit elements by using a factor graph-based decoding technique. The proposed computational approach facilitates rapid evaluation of forward error correction (FEC) strategies for biosensors without resorting to painstaking and time-consuming experimental procedures. The analysis presented in this paper shows that an asymmetric FEC biosensor code outperforms a repetition FEC biosensor code which has been proposed for microarray technology. In addition, we also show that the proposed analysis leads to a novel "co-detection" protocol that could be used for reliable detection of trace quantities of pathogens.

关键词： Biomolecular circuits biosensors computer-aided design (CAD) factor graph forward error correction co-detection polyaniline reliability

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factor graph weight particles aided distributed underwater cooperative positioning algorithm

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TELECOMMUNICATION SYSTEMS 2022年第2期79卷 181-191页

作者： Tang, Chengkai Yu, Baoguo Zhang, Lingling Zhang, Yi Song, Houbing Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China China Elect Technol Grp Corp Res Inst 54 Shijiazhuang Hebei Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Embry Riddle Aeronaut Univ Dept Elect Comp Software & Syst Engn Daytona Beach FL 32114 USA

The growing scale of marine exploration requires high-resolution underwater localization, which necessitates cooperation among underwater network nodes and consideration of the channel complexity and power efficiency. In this paper, we proposed a factor graph weight particle-aided distributed underwater node cooperative positioning algorithm (WP-DUCP). The algorithm capitalizes on the factor graph and sum-product algorithm to decompose the global optimization to the product of several local optimization functions. Combined with Gaussian parameters used to construct weighted particles and to realize belief transfer, the algorithm shows low complexity and high efficiency and is suitable for energy-restricted and communication distance-limited underwater networks. In terms of convergence, localization resolution, and computational complexity, we conducted simulations and real tests with comparisons to the existing colocalization methods. The results verified the higher resolution of the proposed method with no extra computational burden.

关键词： Distributed colocalization Underwater node factor graph Weighted particle

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factor graph aided multiple hypothesis tracking

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Science China(Information Sciences) 2013年第10期56卷 284-289页

作者： WANG Huan SUN JinPing LU SongTao WEI ShaoMing School of Electronic and Information Engineering Beihang University Department of Electrical and Computer Engineering Iowa State University

Since closely moving targets exist extensively in the ground moving target tracking,the uncertainty of data association greatly increases making the measurement-to-track association more ***,traditional multiple hypothesis tracking(MHT)has high false tracking rate and track *** paper frst investigates the measurement based factor graph in data association,and gives the corresponding message passing ***,a factor graph aided multiple hypothesis tracking(FGA-MHT)method is proposed,which introduces factor graph based m-best hypothesis producing technique and exploits factor graph based probability refnement algorithm to reduce the uncertainty of measurement-to-track *** results demonstrate that FGA-MHT reduces times of track swap and increases the correct data association rate in closely moving target tracking.

关键词： multiple hypothesis tracking(MHT) data association factor graph message passing algorithm sum-product algorithm factor graph aided multiple hypothesis tracking(FGA-MHT

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factor graph localization for mobile robots using Google Indoor Street View and CNN-based place recognition

DRONE SYSTEMS AND APPLICATIONS

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DRONE SYSTEMS AND APPLICATIONS 2023年 11卷 1-19页

作者： Tennakoon, Kusal B. De Silva, Oscar Jayasiri, Awantha Mann, George K. I. Gosine, Raymond G. Mem Univ Newfoundland Dept Mech Engn St John NF Canada Natl Res Council Canada Aerosp Res Ctr Ottawa ON Canada

This article proposes a mobile robot localization system developed using Google Indoor Street View (GISV) and Convolutional Neural Network (CNN)-based visual place recognition. The proposed localization system consists of two main modules. The first is a place recognition module based on GISV and a net Vector of Locally Aggregated Descriptors (VLAD)-based CNN. The second is a factor graph-based optimization module. In this work, we show that a CNN-based approach can be utilized to overcome the lack of visually distinct features in indoor environments and changes in images that can occur when using different cameras at different points in time for localization. The proposed CNN-based localization system is implemented using reference and query images obtained from two different sources (GISV and a camera attached to a mobile robot). It has been experimentally validated using a custom indoor dataset captured at the Memorial University of Newfoundland engineering building basement. The main results of this paper show that GISV-based place recognition reduces the percentage drift by 4% for the dataset and achieves a Root Mean Square Error (RMSE) of 2 m for position and 2.5 degrees for orientation.

关键词： topological localization indoor localization factor graph place recognition

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