Yongmin Zhong - Academia.edu (original) (raw)

Papers by Yongmin Zhong

Research paper thumbnail of Virtual factory for manufacturing process visualization

Complexity International, 2005

... They simulate the behaviours of entities when an event occurs at a distinct point of time [Ho... more ... They simulate the behaviours of entities when an event occurs at a distinct point of time [Hoeger and Jones (1996), Kheir (1996) and Praehofer et al (1999)]. ... Based upon the Java Beans component model, this library supports rapid development of new objects. ...

Research paper thumbnail of Characterizing the Disruption of HEK-293 Cell Membrane in AFM-based Indentation Using Energy Limiter Method

2019 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

The disruption of the cell membrane is an essential stage in adherent cell microinjection. With t... more The disruption of the cell membrane is an essential stage in adherent cell microinjection. With the assistant of atomic force microscopy system, the interaction force between the cell and the AFM probe during the indentation can be measured. Mechanical response of adherent cells in AFM-based indentation were explained by different models. However, the disruption of the cell membrane has not been theoretically explained. In this study, an energy limiter method is introduced to the continuum model of the adherent cells. The strain energy density function in the continuum model is modified by involving the energy limiter. As a result, on the computational nominal stress-indentation distance curve, a global maximum value of the nominal stress on the contact boundary between the cell membrane and the AFM probe can be found. Therefore, the location of the global maximum value can be used to characterize the disruption of the cell membrane. Indentation was conducted on the human kidney embryos 293 cells, and the performance of the energy limiter method in characterizing the disruption of the cell membrane was evaluated, which shows higher consistency than the performance of the experimental criterion. These results represent a feasible approach to understand the mechanism of the disruption of the cell membrane in microinjection.

Research paper thumbnail of Design of a 3-DOF parallel mechanism for the enhancement of endonasal surgery

2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017

Additive manufacturing was used to build a large scale prototype of a 3-DOF, 3-PRS parallel manip... more Additive manufacturing was used to build a large scale prototype of a 3-DOF, 3-PRS parallel manipulator designed for enhancing the mobility of instruments used in endonasal neurosurgery. The prototype is 5 times larger than the design, with an outer diameter of 25mm. It uses custom ball and socket joints to provide pitch and roll capabilities of ±60° while remaining compact. Testing of the robots range of motion shows the desired workspace is achievable. Visual tracking was used to record the precision of this motion. Positioning errors were relatively low with a root mean squared error of 1.1°. The prototype exhibits high strength and is able to withstand axial forces of 80 N and torques up to 300 N mm. Future research will focus on the development of a 5 mm diameter manipulator.

Research paper thumbnail of Robust Adaptive Gaussian Mixture Sigma Point Particle Filter

This paper presents a new robust adaptive Gaussian mixture sigma-point particle filter by adoptin... more This paper presents a new robust adaptive Gaussian mixture sigma-point particle filter by adopting the concept of robust adaptive estimation to the Gaussian mixture sigma-point particle filter. This method approximates state mean and covariance via Sigma-point transformation combined with new available measurement information. It enables the estimations of state mean and covariance to be adjusted via the equivalent weight function and adaptive factor, thus restraining the disturbances of singular measurements and kinematic model noise. It can also obtain efficient predict prior and posterior density functions via Gaussian mixture approximation to improve the filtering accuracy for nonlinear and non-Gaussian systems. Simulation results and comparison analysis demonstrate the proposed method can effectively restrain the disturbances of abnormal measurements and kinematic model noise on state estimate, leading to improved estimation accuracy.

Research paper thumbnail of A Review of Mass Spring Method Improvements for Modeling Soft Tissue Deformation

Human-Centered Technology for a Better Tomorrow, 2021

Research paper thumbnail of Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system

Information Fusion, 2020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Research paper thumbnail of Cubature rule-based distributed optimal fusion with identification and prediction of kinematic model error for integrated UAV navigation

Aerospace Science and Technology, 2021

Abstract Integrated MIMU/GNSS/CNS (micro-electro-mechanical system-based inertial measurement uni... more Abstract Integrated MIMU/GNSS/CNS (micro-electro-mechanical system-based inertial measurement unit/global navigation satellite system/celestial navigation system) is a promising strategy for UAV (unmanned aerial vehicle) navigation. However, given its strong nonlinearity and involvement of kinematic model error, integrated MIMU/GNSS/CNS UAV navigation presents the difficulty in achieving optimal navigation solutions. This paper presents a method of cubature rule-based distributed optimal fusion combined with identification and prediction of kinematic model error to address the above problem in nonlinear integrated MIMU/GNSS/CNS. This method is in a distributed structure to simultaneously process observations from integrated MIMU/GNSS and MIMU/CNS subsystems for the subsequent global fusion. A theory for identification of kinematic model error is established using the concept of Mahalanobis distance. Further, the standard cubature Kalman filter is modified with the model prediction filter to serve as the local filters in integrated MIMU/GNSS and MIMU/CNS subsystems to hinder the disturbance of kinematic model error. Based on the above, an optimal fusion technique is developed to fuse the filtering results of each subsystem for achieving globally optimal state estimation in the sense of mean square error. Simulations and experimental results as well as comparison analysis demonstrate that the proposed distributed optimal fusion method can effectively identify and predict kinematic model error and further achieve globally optimal fusion results, leading to improved performance for integrated MIMU/GNSS/CNS UAV navigation.

Research paper thumbnail of Cubature Kalman Filter With Both Adaptability and Robustness for Tightly-Coupled GNSS/INS Integration

IEEE Sensors Journal, 2021

Tightly-coupled GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) integrat... more Tightly-coupled GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) integration is of importance to vehicle positioning. However, this integration technology has difficulty in achieving optimal positioning solutions for the dynamic systems involving strong nonlinearity and systematic modelling error. This paper proposes a new methodology to address the problem of tightly-coupled GNSS/INS integration. This methodology rigorously derives a novel adaptive CKF (Cubature Kalman Filter) with fading memory for kinematic modelling error and a new robust CKF with emerging memory for observation modelling error, using the concept of Mahalanobis distance without involving artificial empiricism. Based on this, a new CKF with both adaptability and robustness is further developed by fusing the results of the standard CKF, adaptive CKF and robust CKF via the principle of interacting multiple model (IMM). Simulation and experiment results together with comparison analysis prove that the proposed methodology can curb the interferences of both kinematic and observation modelling errors on state estimation, leading to improved positioning accuracy for vehicle positioning via tightly-coupled GNSS/INS integration.

Research paper thumbnail of Maximum Likelihood-Based Measurement Noise Covariance Estimation Using Sequential Quadratic Programming for Cubature Kalman Filter Applied in INS/BDS Integration

Mathematical Problems in Engineering, 2021

With the completion of the Beidou-3 system (BDS) in China, INS/BDS integration will become a prom... more With the completion of the Beidou-3 system (BDS) in China, INS/BDS integration will become a promising navigation and positioning strategy. However, due to the nonlinear propagation characteristic of INS error and inevitable involvement of inaccurate measurement noise statistics, it is difficult to achieve the optimal solution through the INS/BDS integration. This paper proposes a method of cubature Kalman filter (CKF) with the measurement noise covariance estimation by using the maximum likelihood principle to solve the abovementioned problem. It establishes an estimation model for measurement noise covariance according to the maximum likelihood principle, and then, its estimation is calculated by utilizing the sequential quadratic programming. The estimated measurement noise covariance will be fed back to the procedure of CKF to improve its adaptability. Simulation and comparison analysis verify that the proposed method can accurately estimate measurement noise covariance to effec...

Research paper thumbnail of Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization

Sensors, 2018

This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online ... more This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis distance to identify contact model error, and further incorporates a scaling factor in predicted state covariance to compensate identified model error. This scaling factor is determined according to the principle of innovation orthogonality to avoid the cumbersome computation of Jacobian matrix, where the random weighting concept is adopted to improve the estimation accuracy of innovation covariance. A master-slave robotic indentation system is developed to validate the performance of the proposed method. Simulation and experimental results as well as comparison analyses demonstrate that the efficacy of the proposed method for online characterization of soft tissue parameters in the presence of contact model error.

Research paper thumbnail of Sensing and Modelling Mechanical Response in Large Deformation Indentation of Adherent Cell Using Atomic Force Microscopy

Sensors, 2020

The mechanical behaviour of adherent cells when subjected to the local indentation can be modelle... more The mechanical behaviour of adherent cells when subjected to the local indentation can be modelled via various approaches. Specifically, the tensegrity structure has been widely used in describing the organization of discrete intracellular cytoskeletal components, including microtubules (MTs) and microfilaments. The establishment of a tensegrity model for adherent cells has generally been done empirically, without a mathematically demonstrated methodology. In this study, a rotationally symmetric prism-shaped tensegrity structure is introduced, and it forms the basis of the proposed multi-level tensegrity model. The modelling approach utilizes the force density method to mathematically assure self-equilibrium. The proposed multi-level tensegrity model was developed by densely distributing the fundamental tensegrity structure in the intracellular space. In order to characterize the mechanical behaviour of the adherent cell during the atomic force microscopy (AFM) indentation with larg...

Research paper thumbnail of Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty

Sensors, 2020

This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kal... more This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the linearization of the nonlinear system model via a Taylor series expansion, this method introduces a new UBB error term by combining the linearization error with systematic UBB error through the Minkowski sum. Subsequently, an optimal Kalman gain is derived to minimize the mean squared error of the state estimate in the KF framework by taking both stochastic and UBB errors into account. The proposed SM-HKF handles the systematic UBB error, stochastic error as well as the linearization error simultaneously, thus overcoming the limitations of the extended Kalman filter (EKF). The effectiveness and superiority of the proposed SM-HKF have been verified through simulations and comparison analysi...

Research paper thumbnail of Random Weighting-Based Nonlinear Gaussian Filtering

IEEE Access, 2020

The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, ... more The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics. However, it is difficult to satisfy this condition in engineering practice, making the Gaussian filtering solution deviated or diverged. This paper adopts the random weighting concept to address the limitation of the nonlinear Gaussian filtering. It establishes the random weighting estimations of system noise characteristics on the basis of the maximum a-posterior theory, and further develops a new Gaussian filtering method based on the random weighting estimations to restrain system noise influences on system state estimation by adaptively adjusting the random weights of system noise characteristics. Simulation, experimental and comparison analyses prove that the proposed method overcomes the limitation of the traditional Gaussian filtering in requirement of system noise characteristics, leading to improved estimation accuracy. INDEX TERMS Nonlinear system state estimation, Gaussian filtering, system noise characteristics, random weighting.

Research paper thumbnail of Model Predictive Filtering Based Neural Networks for GPS GDOP Approximation

Journal of Aerospace Engineering and Mechanics, 2016

This paper presents a new method to calculate the geometric dilution of precision (GDOP) of GPS b... more This paper presents a new method to calculate the geometric dilution of precision (GDOP) of GPS by incorporating the concept of model predictive filtering in the training process of neural networks to learn the relationship between GDOP and the azimuth and elevation of satellite. This method overcomes the shortcomings of the traditional back propagation neural networks, such as the slow convergence speed and easily falling into local minimum. A model predictive filtering algorithm is developed by using network weights as system state variables to optimize the network weights based on the neural network's error correction. During the training process, the neural network model error is corrected by compensating the deviation between the actual and target output via the model predictive filtering. Experimental results and comparison analysis demonstrate that the proposed method can effectively approximate GDOP with improved accuracy and reduced training time.

Research paper thumbnail of A Modified Particle Filter for SINS/SAR Integrated Navigation

Journal of Aerospace Engineering and Mechanics, 2016

This paper presents a modified particle filter for SINS/SAR (Strap-down Inertial Navigation Syste... more This paper presents a modified particle filter for SINS/SAR (Strap-down Inertial Navigation System / Synthetic Aperture Radar) integrated navigation. This method is developed by adopting Markov Chain Monte Carlo (MCMC) moves to the p article regularization process. It combines local resampling with MCMC moves to prevent particle degeneracy and also guarantee that the resultant particles are in the same distribution as probability distribution function, without causing extra noise on state estimate. Simulation results demonstrate that the proposed method can effectively prevent the problem of particle degeneracy, and its filtering accuracy for SINS/SAR integrated navigation is much higher than that of the classical particle filter and regularized particle filter.

Research paper thumbnail of Soft tissue deformation estimation by spatio-temporal Kalman filter finite element method

Technology and Health Care, 2018

BACKGROUND: Soft tissue modeling plays an important role in the development of surgical training ... more BACKGROUND: Soft tissue modeling plays an important role in the development of surgical training simulators as well as in robot-assisted minimally invasive surgeries. It has been known that while the traditional Finite Element Method (FEM) promises the accurate modeling of soft tissue deformation, it still suffers from a slow computational process. OBJECTIVE: This paper presents a Kalman filter finite element method to model soft tissue deformation in real time without sacrificing the traditional FEM accuracy. METHODS: The proposed method employs the FEM equilibrium equation and formulates it as a filtering process to estimate soft tissue behavior using real-time measurement data. The model is temporally discretized using the Newmark method and further formulated as the system state equation. RESULTS: Simulation results demonstrate that the computational time of KF-FEM is approximately 10 times shorter than the traditional FEM and it is still as accurate as the traditional FEM. The normalized root-mean-square error of the proposed KF-FEM in reference to the traditional FEM is computed as 0.0116. CONCLUSIONS: It is concluded that the proposed method significantly improves the computational performance of the traditional FEM without sacrificing FEM accuracy. The proposed method also filters noises involved in system state and measurement data.

Research paper thumbnail of A Robust Cubature Kalman Filter with Abnormal Observations Identification Using the Mahalanobis Distance Criterion for Vehicular INS/GNSS Integration

Sensors, 2019

INS/GNSS (inertial navigation system/global navigation satellite system) integration is a promisi... more INS/GNSS (inertial navigation system/global navigation satellite system) integration is a promising solution of vehicle navigation for intelligent transportation systems. However, the observation of GNSS inevitably involves uncertainty due to the vulnerability to signal blockage in many urban/suburban areas, leading to the degraded navigation performance for INS/GNSS integration. This paper develops a novel robust CKF with scaling factor by combining the emerging cubature Kalman filter (CKF) with the concept of Mahalanobis distance criterion to address the above problem involved in nonlinear INS/GNSS integration. It establishes a theory of abnormal observations identification using the Mahalanobis distance criterion. Subsequently, a robust factor (scaling factor), which is calculated via the Mahalanobis distance criterion, is introduced into the standard CKF to inflate the observation noise covariance, resulting in a decreased filtering gain in the presence of abnormal observations....

Research paper thumbnail of Randomly Weighted CKF for Multisensor Integrated Systems

Journal of Sensors, 2019

The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However,... more The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

Research paper thumbnail of Adaptive Square-Root Unscented Particle Filtering Algorithm for Dynamic Navigation

Sensors, 2018

This paper presents a new adaptive square-root unscented particle filtering algorithm by combinin... more This paper presents a new adaptive square-root unscented particle filtering algorithm by combining the adaptive filtering and square-root filtering into the unscented particle filter to inhibit the disturbance of kinematic model noise and the instability of filtering data in the process of nonlinear filtering. To prevent particles from degeneracy, the proposed algorithm adaptively adjusts the adaptive factor, which is constructed from predicted residuals, to refrain from the disturbance of abnormal observation and the kinematic model noise. Cholesky factorization is also applied to suppress the negative definiteness of the covariance matrices of the predicted state vector and observation vector. Experiments and comparison analysis were conducted to comprehensively evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm exhibits a strong overall performance for integrated navigation systems.

Research paper thumbnail of Modeling of soft tissue thermal damage based on GPU acceleration

Computer Assisted Surgery, 2019

Hyperthermia treatments require precise control of thermal energy to form the coagulation zones w... more Hyperthermia treatments require precise control of thermal energy to form the coagulation zones which sufficiently cover the tumor without affecting surrounding healthy tissues. This has led modeling of soft tissue thermal damage to become important in hyperthermia treatments to completely eradicate tumors without inducing tissue damage to surrounding healthy tissues. This paper presents a methodology based on GPU acceleration for modeling and analysis of bio-heat conduction and associated thermal-induced tissue damage for prediction of soft tissue damage in thermal ablation, which is a typical hyperthermia therapy. The proposed methodology combines the Arrhenius Burn integration with Pennes' bio-heat transfer for prediction of temperature field and thermal damage in soft tissues. The problem domain is spatially discretized on 3-D linear tetrahedral meshes by the Galerkin finite element method and temporally discretized by the explicit forward finite difference method. To address the expensive computation load involved in the finite element method, GPU acceleration is implemented using the High-Level Shader Language and achieved via a sequential execution of compute shaders in the GPU rendering pipeline. Simulations on a cube-shape specimen and comparison analysis with standalone CPU execution were conducted, demonstrating the proposed GPU-accelerated finite element method can effectively predict the temperature distribution and associated thermal damage in real time. Results show that the peak temperature is achieved at the heat source point and the variation of temperature is mainly dominated in its direct neighbourhood. It is also found that by the continuous application of point-source heat energy, the tissue at the heat source point is quickly necrotized in a matter of seconds, while the entire neighbouring tissues are fully necrotized in several minutes. Further, the proposed GPU acceleration significantly improves the computational performance for soft tissue thermal damage prediction, leading to a maximum reduction of 55.3 times in computation time comparing to standalone CPU execution.

Research paper thumbnail of Virtual factory for manufacturing process visualization

Complexity International, 2005

... They simulate the behaviours of entities when an event occurs at a distinct point of time [Ho... more ... They simulate the behaviours of entities when an event occurs at a distinct point of time [Hoeger and Jones (1996), Kheir (1996) and Praehofer et al (1999)]. ... Based upon the Java Beans component model, this library supports rapid development of new objects. ...

Research paper thumbnail of Characterizing the Disruption of HEK-293 Cell Membrane in AFM-based Indentation Using Energy Limiter Method

2019 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

The disruption of the cell membrane is an essential stage in adherent cell microinjection. With t... more The disruption of the cell membrane is an essential stage in adherent cell microinjection. With the assistant of atomic force microscopy system, the interaction force between the cell and the AFM probe during the indentation can be measured. Mechanical response of adherent cells in AFM-based indentation were explained by different models. However, the disruption of the cell membrane has not been theoretically explained. In this study, an energy limiter method is introduced to the continuum model of the adherent cells. The strain energy density function in the continuum model is modified by involving the energy limiter. As a result, on the computational nominal stress-indentation distance curve, a global maximum value of the nominal stress on the contact boundary between the cell membrane and the AFM probe can be found. Therefore, the location of the global maximum value can be used to characterize the disruption of the cell membrane. Indentation was conducted on the human kidney embryos 293 cells, and the performance of the energy limiter method in characterizing the disruption of the cell membrane was evaluated, which shows higher consistency than the performance of the experimental criterion. These results represent a feasible approach to understand the mechanism of the disruption of the cell membrane in microinjection.

Research paper thumbnail of Design of a 3-DOF parallel mechanism for the enhancement of endonasal surgery

2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017

Additive manufacturing was used to build a large scale prototype of a 3-DOF, 3-PRS parallel manip... more Additive manufacturing was used to build a large scale prototype of a 3-DOF, 3-PRS parallel manipulator designed for enhancing the mobility of instruments used in endonasal neurosurgery. The prototype is 5 times larger than the design, with an outer diameter of 25mm. It uses custom ball and socket joints to provide pitch and roll capabilities of ±60° while remaining compact. Testing of the robots range of motion shows the desired workspace is achievable. Visual tracking was used to record the precision of this motion. Positioning errors were relatively low with a root mean squared error of 1.1°. The prototype exhibits high strength and is able to withstand axial forces of 80 N and torques up to 300 N mm. Future research will focus on the development of a 5 mm diameter manipulator.

Research paper thumbnail of Robust Adaptive Gaussian Mixture Sigma Point Particle Filter

This paper presents a new robust adaptive Gaussian mixture sigma-point particle filter by adoptin... more This paper presents a new robust adaptive Gaussian mixture sigma-point particle filter by adopting the concept of robust adaptive estimation to the Gaussian mixture sigma-point particle filter. This method approximates state mean and covariance via Sigma-point transformation combined with new available measurement information. It enables the estimations of state mean and covariance to be adjusted via the equivalent weight function and adaptive factor, thus restraining the disturbances of singular measurements and kinematic model noise. It can also obtain efficient predict prior and posterior density functions via Gaussian mixture approximation to improve the filtering accuracy for nonlinear and non-Gaussian systems. Simulation results and comparison analysis demonstrate the proposed method can effectively restrain the disturbances of abnormal measurements and kinematic model noise on state estimate, leading to improved estimation accuracy.

Research paper thumbnail of A Review of Mass Spring Method Improvements for Modeling Soft Tissue Deformation

Human-Centered Technology for a Better Tomorrow, 2021

Research paper thumbnail of Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system

Information Fusion, 2020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Research paper thumbnail of Cubature rule-based distributed optimal fusion with identification and prediction of kinematic model error for integrated UAV navigation

Aerospace Science and Technology, 2021

Abstract Integrated MIMU/GNSS/CNS (micro-electro-mechanical system-based inertial measurement uni... more Abstract Integrated MIMU/GNSS/CNS (micro-electro-mechanical system-based inertial measurement unit/global navigation satellite system/celestial navigation system) is a promising strategy for UAV (unmanned aerial vehicle) navigation. However, given its strong nonlinearity and involvement of kinematic model error, integrated MIMU/GNSS/CNS UAV navigation presents the difficulty in achieving optimal navigation solutions. This paper presents a method of cubature rule-based distributed optimal fusion combined with identification and prediction of kinematic model error to address the above problem in nonlinear integrated MIMU/GNSS/CNS. This method is in a distributed structure to simultaneously process observations from integrated MIMU/GNSS and MIMU/CNS subsystems for the subsequent global fusion. A theory for identification of kinematic model error is established using the concept of Mahalanobis distance. Further, the standard cubature Kalman filter is modified with the model prediction filter to serve as the local filters in integrated MIMU/GNSS and MIMU/CNS subsystems to hinder the disturbance of kinematic model error. Based on the above, an optimal fusion technique is developed to fuse the filtering results of each subsystem for achieving globally optimal state estimation in the sense of mean square error. Simulations and experimental results as well as comparison analysis demonstrate that the proposed distributed optimal fusion method can effectively identify and predict kinematic model error and further achieve globally optimal fusion results, leading to improved performance for integrated MIMU/GNSS/CNS UAV navigation.

Research paper thumbnail of Cubature Kalman Filter With Both Adaptability and Robustness for Tightly-Coupled GNSS/INS Integration

IEEE Sensors Journal, 2021

Tightly-coupled GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) integrat... more Tightly-coupled GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) integration is of importance to vehicle positioning. However, this integration technology has difficulty in achieving optimal positioning solutions for the dynamic systems involving strong nonlinearity and systematic modelling error. This paper proposes a new methodology to address the problem of tightly-coupled GNSS/INS integration. This methodology rigorously derives a novel adaptive CKF (Cubature Kalman Filter) with fading memory for kinematic modelling error and a new robust CKF with emerging memory for observation modelling error, using the concept of Mahalanobis distance without involving artificial empiricism. Based on this, a new CKF with both adaptability and robustness is further developed by fusing the results of the standard CKF, adaptive CKF and robust CKF via the principle of interacting multiple model (IMM). Simulation and experiment results together with comparison analysis prove that the proposed methodology can curb the interferences of both kinematic and observation modelling errors on state estimation, leading to improved positioning accuracy for vehicle positioning via tightly-coupled GNSS/INS integration.

Research paper thumbnail of Maximum Likelihood-Based Measurement Noise Covariance Estimation Using Sequential Quadratic Programming for Cubature Kalman Filter Applied in INS/BDS Integration

Mathematical Problems in Engineering, 2021

With the completion of the Beidou-3 system (BDS) in China, INS/BDS integration will become a prom... more With the completion of the Beidou-3 system (BDS) in China, INS/BDS integration will become a promising navigation and positioning strategy. However, due to the nonlinear propagation characteristic of INS error and inevitable involvement of inaccurate measurement noise statistics, it is difficult to achieve the optimal solution through the INS/BDS integration. This paper proposes a method of cubature Kalman filter (CKF) with the measurement noise covariance estimation by using the maximum likelihood principle to solve the abovementioned problem. It establishes an estimation model for measurement noise covariance according to the maximum likelihood principle, and then, its estimation is calculated by utilizing the sequential quadratic programming. The estimated measurement noise covariance will be fed back to the procedure of CKF to improve its adaptability. Simulation and comparison analysis verify that the proposed method can accurately estimate measurement noise covariance to effec...

Research paper thumbnail of Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization

Sensors, 2018

This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online ... more This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis distance to identify contact model error, and further incorporates a scaling factor in predicted state covariance to compensate identified model error. This scaling factor is determined according to the principle of innovation orthogonality to avoid the cumbersome computation of Jacobian matrix, where the random weighting concept is adopted to improve the estimation accuracy of innovation covariance. A master-slave robotic indentation system is developed to validate the performance of the proposed method. Simulation and experimental results as well as comparison analyses demonstrate that the efficacy of the proposed method for online characterization of soft tissue parameters in the presence of contact model error.

Research paper thumbnail of Sensing and Modelling Mechanical Response in Large Deformation Indentation of Adherent Cell Using Atomic Force Microscopy

Sensors, 2020

The mechanical behaviour of adherent cells when subjected to the local indentation can be modelle... more The mechanical behaviour of adherent cells when subjected to the local indentation can be modelled via various approaches. Specifically, the tensegrity structure has been widely used in describing the organization of discrete intracellular cytoskeletal components, including microtubules (MTs) and microfilaments. The establishment of a tensegrity model for adherent cells has generally been done empirically, without a mathematically demonstrated methodology. In this study, a rotationally symmetric prism-shaped tensegrity structure is introduced, and it forms the basis of the proposed multi-level tensegrity model. The modelling approach utilizes the force density method to mathematically assure self-equilibrium. The proposed multi-level tensegrity model was developed by densely distributing the fundamental tensegrity structure in the intracellular space. In order to characterize the mechanical behaviour of the adherent cell during the atomic force microscopy (AFM) indentation with larg...

Research paper thumbnail of Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty

Sensors, 2020

This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kal... more This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the linearization of the nonlinear system model via a Taylor series expansion, this method introduces a new UBB error term by combining the linearization error with systematic UBB error through the Minkowski sum. Subsequently, an optimal Kalman gain is derived to minimize the mean squared error of the state estimate in the KF framework by taking both stochastic and UBB errors into account. The proposed SM-HKF handles the systematic UBB error, stochastic error as well as the linearization error simultaneously, thus overcoming the limitations of the extended Kalman filter (EKF). The effectiveness and superiority of the proposed SM-HKF have been verified through simulations and comparison analysi...

Research paper thumbnail of Random Weighting-Based Nonlinear Gaussian Filtering

IEEE Access, 2020

The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, ... more The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics. However, it is difficult to satisfy this condition in engineering practice, making the Gaussian filtering solution deviated or diverged. This paper adopts the random weighting concept to address the limitation of the nonlinear Gaussian filtering. It establishes the random weighting estimations of system noise characteristics on the basis of the maximum a-posterior theory, and further develops a new Gaussian filtering method based on the random weighting estimations to restrain system noise influences on system state estimation by adaptively adjusting the random weights of system noise characteristics. Simulation, experimental and comparison analyses prove that the proposed method overcomes the limitation of the traditional Gaussian filtering in requirement of system noise characteristics, leading to improved estimation accuracy. INDEX TERMS Nonlinear system state estimation, Gaussian filtering, system noise characteristics, random weighting.

Research paper thumbnail of Model Predictive Filtering Based Neural Networks for GPS GDOP Approximation

Journal of Aerospace Engineering and Mechanics, 2016

This paper presents a new method to calculate the geometric dilution of precision (GDOP) of GPS b... more This paper presents a new method to calculate the geometric dilution of precision (GDOP) of GPS by incorporating the concept of model predictive filtering in the training process of neural networks to learn the relationship between GDOP and the azimuth and elevation of satellite. This method overcomes the shortcomings of the traditional back propagation neural networks, such as the slow convergence speed and easily falling into local minimum. A model predictive filtering algorithm is developed by using network weights as system state variables to optimize the network weights based on the neural network's error correction. During the training process, the neural network model error is corrected by compensating the deviation between the actual and target output via the model predictive filtering. Experimental results and comparison analysis demonstrate that the proposed method can effectively approximate GDOP with improved accuracy and reduced training time.

Research paper thumbnail of A Modified Particle Filter for SINS/SAR Integrated Navigation

Journal of Aerospace Engineering and Mechanics, 2016

This paper presents a modified particle filter for SINS/SAR (Strap-down Inertial Navigation Syste... more This paper presents a modified particle filter for SINS/SAR (Strap-down Inertial Navigation System / Synthetic Aperture Radar) integrated navigation. This method is developed by adopting Markov Chain Monte Carlo (MCMC) moves to the p article regularization process. It combines local resampling with MCMC moves to prevent particle degeneracy and also guarantee that the resultant particles are in the same distribution as probability distribution function, without causing extra noise on state estimate. Simulation results demonstrate that the proposed method can effectively prevent the problem of particle degeneracy, and its filtering accuracy for SINS/SAR integrated navigation is much higher than that of the classical particle filter and regularized particle filter.

Research paper thumbnail of Soft tissue deformation estimation by spatio-temporal Kalman filter finite element method

Technology and Health Care, 2018

BACKGROUND: Soft tissue modeling plays an important role in the development of surgical training ... more BACKGROUND: Soft tissue modeling plays an important role in the development of surgical training simulators as well as in robot-assisted minimally invasive surgeries. It has been known that while the traditional Finite Element Method (FEM) promises the accurate modeling of soft tissue deformation, it still suffers from a slow computational process. OBJECTIVE: This paper presents a Kalman filter finite element method to model soft tissue deformation in real time without sacrificing the traditional FEM accuracy. METHODS: The proposed method employs the FEM equilibrium equation and formulates it as a filtering process to estimate soft tissue behavior using real-time measurement data. The model is temporally discretized using the Newmark method and further formulated as the system state equation. RESULTS: Simulation results demonstrate that the computational time of KF-FEM is approximately 10 times shorter than the traditional FEM and it is still as accurate as the traditional FEM. The normalized root-mean-square error of the proposed KF-FEM in reference to the traditional FEM is computed as 0.0116. CONCLUSIONS: It is concluded that the proposed method significantly improves the computational performance of the traditional FEM without sacrificing FEM accuracy. The proposed method also filters noises involved in system state and measurement data.

Research paper thumbnail of A Robust Cubature Kalman Filter with Abnormal Observations Identification Using the Mahalanobis Distance Criterion for Vehicular INS/GNSS Integration

Sensors, 2019

INS/GNSS (inertial navigation system/global navigation satellite system) integration is a promisi... more INS/GNSS (inertial navigation system/global navigation satellite system) integration is a promising solution of vehicle navigation for intelligent transportation systems. However, the observation of GNSS inevitably involves uncertainty due to the vulnerability to signal blockage in many urban/suburban areas, leading to the degraded navigation performance for INS/GNSS integration. This paper develops a novel robust CKF with scaling factor by combining the emerging cubature Kalman filter (CKF) with the concept of Mahalanobis distance criterion to address the above problem involved in nonlinear INS/GNSS integration. It establishes a theory of abnormal observations identification using the Mahalanobis distance criterion. Subsequently, a robust factor (scaling factor), which is calculated via the Mahalanobis distance criterion, is introduced into the standard CKF to inflate the observation noise covariance, resulting in a decreased filtering gain in the presence of abnormal observations....

Research paper thumbnail of Randomly Weighted CKF for Multisensor Integrated Systems

Journal of Sensors, 2019

The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However,... more The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

Research paper thumbnail of Adaptive Square-Root Unscented Particle Filtering Algorithm for Dynamic Navigation

Sensors, 2018

This paper presents a new adaptive square-root unscented particle filtering algorithm by combinin... more This paper presents a new adaptive square-root unscented particle filtering algorithm by combining the adaptive filtering and square-root filtering into the unscented particle filter to inhibit the disturbance of kinematic model noise and the instability of filtering data in the process of nonlinear filtering. To prevent particles from degeneracy, the proposed algorithm adaptively adjusts the adaptive factor, which is constructed from predicted residuals, to refrain from the disturbance of abnormal observation and the kinematic model noise. Cholesky factorization is also applied to suppress the negative definiteness of the covariance matrices of the predicted state vector and observation vector. Experiments and comparison analysis were conducted to comprehensively evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm exhibits a strong overall performance for integrated navigation systems.

Research paper thumbnail of Modeling of soft tissue thermal damage based on GPU acceleration

Computer Assisted Surgery, 2019

Hyperthermia treatments require precise control of thermal energy to form the coagulation zones w... more Hyperthermia treatments require precise control of thermal energy to form the coagulation zones which sufficiently cover the tumor without affecting surrounding healthy tissues. This has led modeling of soft tissue thermal damage to become important in hyperthermia treatments to completely eradicate tumors without inducing tissue damage to surrounding healthy tissues. This paper presents a methodology based on GPU acceleration for modeling and analysis of bio-heat conduction and associated thermal-induced tissue damage for prediction of soft tissue damage in thermal ablation, which is a typical hyperthermia therapy. The proposed methodology combines the Arrhenius Burn integration with Pennes' bio-heat transfer for prediction of temperature field and thermal damage in soft tissues. The problem domain is spatially discretized on 3-D linear tetrahedral meshes by the Galerkin finite element method and temporally discretized by the explicit forward finite difference method. To address the expensive computation load involved in the finite element method, GPU acceleration is implemented using the High-Level Shader Language and achieved via a sequential execution of compute shaders in the GPU rendering pipeline. Simulations on a cube-shape specimen and comparison analysis with standalone CPU execution were conducted, demonstrating the proposed GPU-accelerated finite element method can effectively predict the temperature distribution and associated thermal damage in real time. Results show that the peak temperature is achieved at the heat source point and the variation of temperature is mainly dominated in its direct neighbourhood. It is also found that by the continuous application of point-source heat energy, the tissue at the heat source point is quickly necrotized in a matter of seconds, while the entire neighbouring tissues are fully necrotized in several minutes. Further, the proposed GPU acceleration significantly improves the computational performance for soft tissue thermal damage prediction, leading to a maximum reduction of 55.3 times in computation time comparing to standalone CPU execution.