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Wang L, Zhao J, Shi L, Liu Y, Zhang J. A GM-JMNS-CPHD Filter for Different-Fields-of-View Stochastic Outlier Selection for Nonlinear Motion Tracking. SENSORS (BASEL, SWITZERLAND) 2024; 24:3176. [PMID: 38794029 PMCID: PMC11125154 DOI: 10.3390/s24103176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/08/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024]
Abstract
Most multi-target movements are nonlinear in the process of movement. The common multi-target tracking filtering methods directly act on the multi-target tracking system of nonlinear targets, and the fusion effect is worse under the influence of different perspectives. Aiming to determine the influence of different perspectives on the fusion accuracy of multi-sensor tracking in the process of target tracking, this paper studies the multi-target tracking fusion strategy of a nonlinear system with different perspectives. A GM-JMNS-CPHD fusion technique is introduced for random outlier selection in multi-target tracking, leveraging sensors with limited views. By employing boundary segmentation from distinct perspectives, the posterior intensity function undergoes decomposition into multiple sub-intensities through SOS clustering. The distribution of target numbers within the respective regions is then characterized by the multi-Bernoulli reconstruction cardinal distribution. Simulation outcomes demonstrate the robustness and efficacy of this approach. In comparison to other algorithms, this method exhibits enhanced robustness even amidst a decreased detection probability and heightened clutter rates.
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Affiliation(s)
- Liu Wang
- The Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130012, China; (L.W.); (L.S.); (Y.L.); (J.Z.)
- Jilin Provincial Key Laboratory of Human Health Status Identification & Function Enhancement, Changchun 130000, China
- College of Computer Science and Technology, Changchun University, Changchun 130000, China
| | - Jian Zhao
- The Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130012, China; (L.W.); (L.S.); (Y.L.); (J.Z.)
- Jilin Provincial Key Laboratory of Human Health Status Identification & Function Enhancement, Changchun 130000, China
- College of Computer Science and Technology, Changchun University, Changchun 130000, China
| | - Lijuan Shi
- The Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130012, China; (L.W.); (L.S.); (Y.L.); (J.Z.)
- Jilin Provincial Key Laboratory of Human Health Status Identification & Function Enhancement, Changchun 130000, China
- College of Electronic and Information Engineering, Changchun University, Changchun 130000, China
| | - Yuan Liu
- The Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130012, China; (L.W.); (L.S.); (Y.L.); (J.Z.)
- Jilin Provincial Key Laboratory of Human Health Status Identification & Function Enhancement, Changchun 130000, China
- College of Electronic and Information Engineering, Changchun University, Changchun 130000, China
| | - Jing Zhang
- The Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130012, China; (L.W.); (L.S.); (Y.L.); (J.Z.)
- Jilin Provincial Key Laboratory of Human Health Status Identification & Function Enhancement, Changchun 130000, China
- College of Electronic and Information Engineering, Changchun University, Changchun 130000, China
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Wang L, Chen G. An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters. SENSORS (BASEL, SWITZERLAND) 2023; 24:117. [PMID: 38202980 PMCID: PMC10781050 DOI: 10.3390/s24010117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/15/2023] [Accepted: 12/16/2023] [Indexed: 01/12/2024]
Abstract
A highly efficient implementation method for distributed fusion in sensor networks based on CPHD filters is proposed to address the issues of unknown cross-covariance fusion estimation and long fusion times in multi-sensor distributed fusion. This method can effectively and efficiently fuse multi-node information in multi-target tracking applications. Discrete gamma cardinalized probability hypothesis density (DG-CPHD) can effectively reduce the computational burden while ensuring computational accuracy similar to that of CPHD filters. Parallel inverse covariance intersection (PICI) can effectively avoid solving high-dimensional weight coefficient convex optimization problems, reduce the computational burden, and efficiently implement filtering fusion strategies. The effectiveness of the algorithm is demonstrated through simulation results, which indicate that PICI-GM-DG-CPHD can substantially reduce the computational time compared to other algorithms and is more suitable for distributed sensor fusion.
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Affiliation(s)
| | - Guifen Chen
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China;
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Wang L, Chen G, Chen G. Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD): A Distributed Filter Based on the Intersection of Parallel Inverse Covariances. SENSORS (BASEL, SWITZERLAND) 2023; 23:2921. [PMID: 36991631 PMCID: PMC10053049 DOI: 10.3390/s23062921] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/02/2023] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
A distributed GM-CPHD filter based on parallel inverse covariance crossover is designed to attenuate the local filtering and uncertain time-varying noise affecting the accuracy of sensor signals. First, the GM-CPHD filter is identified as the module for subsystem filtering and estimation due to its high stability under Gaussian distribution. Second, the signals of each subsystem are fused by invoking the inverse covariance cross-fusion algorithm, and the convex optimization problem with high-dimensional weight coefficients is solved. At the same time, the algorithm reduces the burden of data computation, and data fusion time is saved. Finally, the GM-CPHD filter is added to the conventional ICI structure, and the generalization capability of the parallel inverse covariance intersection Gaussian mixture cardinalized probability hypothesis density (PICI-GM-CPHD) algorithm reduces the nonlinear complexity of the system. An experiment on the stability of Gaussian fusion models is organized and linear and nonlinear signals are compared by simulating the metrics of different algorithms, and the results show that the improved algorithm has a smaller metric OSPA error than other mainstream algorithms. Compared with other algorithms, the improved algorithm improves the signal processing accuracy and reduces the running time. The improved algorithm is practical and advanced in terms of multisensor data processing.
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Weinert A, Tormey D, O’Hara C, McAfee M. Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies. SENSORS (BASEL, SWITZERLAND) 2023; 23:2313. [PMID: 36850913 PMCID: PMC9966701 DOI: 10.3390/s23042313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Injection moulding (IM) is an important industrial process, known to be the most used plastic formation technique. Demand for faster cycle times and higher product customisation is driving interest in additive manufacturing (AM) as a new method for mould tool manufacturing. The use of AM offers advantages such as greater design flexibility and conformal cooling of components to reduce cycle times and increase product precision. However, shortcomings of metal additive manufacturing, such as porosity and residual stresses, introduce uncertainties about the reliability and longevity of AM tooling. The injection moulding process relies on high volumes of produced parts and a minimal amount of tool failures. This paper reviews the demands for tool condition monitoring systems for AM-manufactured mould tools; although tool failures in conventionally manufactured tooling are rare, they do occur, usually due to cracking, deflection, and channel blockages. However, due to the limitations of the AM process, metal 3D-printed mould tools are susceptible to failures due to cracking, delamination and deformation. Due to their success in other fields, acoustic emission, accelerometers and ultrasound sensors offer the greatest potential in mould tool condition monitoring. Due to the noisy machine environment, sophisticated signal processing and decision-making algorithms are required to prevent false alarms or the missing of warning signals. This review outlines the state of the art in signal decomposition and both data- and model-based approaches to determination of the current state of the tool, and how these can be employed for IM tool condition monitoring. The development of such a system would help to ensure greater industrial uptake of additive manufacturing of injection mould tooling, by increasing confidence in the technology, further improving the efficiency and productivity of the sector.
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Affiliation(s)
- Albert Weinert
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- I-Form SFI Research Centre for Advanced Manufacturing, Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - David Tormey
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- I-Form SFI Research Centre for Advanced Manufacturing, Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - Christopher O’Hara
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- I-Form SFI Research Centre for Advanced Manufacturing, Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - Marion McAfee
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
- I-Form SFI Research Centre for Advanced Manufacturing, Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
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Ross E, Wagterveld R, Stigter J, Mayer M, Keesman K. Sensor data fusion in electrochemical applications: An overview and its application to electrochlorination monitoring. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2022.108128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Pumaricra Rojas D, Noack M, Reger J, Pérez-Zúñiga G. State Estimation for Coupled Reaction-Diffusion PDE Systems Using Modulating Functions. SENSORS 2022; 22:s22135008. [PMID: 35808503 PMCID: PMC9269780 DOI: 10.3390/s22135008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/14/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022]
Abstract
Many systems with distributed dynamics are described by partial differential equations (PDEs). Coupled reaction-diffusion equations are a particular type of these systems. The measurement of the state over the entire spatial domain is usually required for their control. However, it is often impossible to obtain full state information with physical sensors only. For this problem, observers are developed to estimate the state based on boundary measurements. The method presented applies the so-called modulating function method, relying on an orthonormal function basis representation. Auxiliary systems are generated from the original system by applying modulating functions and formulating annihilation conditions. It is extended by a decoupling matrix step. The calculated kernels are utilized for modulating the input and output signals over a receding time window to obtain the coefficients for the basis expansion for the desired state estimation. The developed algorithm and its real-time functionality are verified via simulation of an example system related to the dynamics of chemical tubular reactors and compared to the conventional backstepping observer. The method achieves a successful state reconstruction of the system while mitigating white noise induced by the sensor. Ultimately, the modulating function approach represents a solution for the distributed state estimation problem without solving a PDE online.
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Affiliation(s)
- David Pumaricra Rojas
- Departamento de Ingeniería, Pontificia Universidad Católica del Perú, Avenida Universitaria 1801, Lima 15088, Peru;
| | - Matti Noack
- Control Engineering Group, Technische Universität Ilmenau, 98693 Ilmenau, Germany; (M.N.); (J.R.)
| | - Johann Reger
- Control Engineering Group, Technische Universität Ilmenau, 98693 Ilmenau, Germany; (M.N.); (J.R.)
| | - Gustavo Pérez-Zúñiga
- Departamento de Ingeniería, Pontificia Universidad Católica del Perú, Avenida Universitaria 1801, Lima 15088, Peru;
- Correspondence:
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