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Ji P, Duan Z, Xu W. A Combined UWB/IMU Localization Method with Improved CKF. SENSORS (BASEL, SWITZERLAND) 2024; 24:3165. [PMID: 38794017 PMCID: PMC11124853 DOI: 10.3390/s24103165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local approximation or even the inability to converge due to the initial value not being set near the optimal solution in the process of solving the UWB position by the least-squares method, the Levenberg-Marquardt algorithm (L-M) is adopted to optimally solve the UWB position. Secondly, because UWB and IMU information are centrally fused, an adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise, and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional CKF algorithm. Finally, the performance of the proposed combined localization method is verified by field experiments in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. The results show that the proposed method can maintain high localization accuracy in both LOS and NLOS scenarios. Compared with the Extended Kalman filter (EKF), unbiased Kalman filter (UKF), and CKF algorithms, the localization accuracies of the proposed method in NLOS scenarios are improved by 25.2%, 18.3%, and 11.3%, respectively.
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Affiliation(s)
| | - Zhongxing Duan
- College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710311, China; (P.J.); (W.X.)
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Wen T, Liu J, Cao Y, Roberts C. Parallel Kalman filter group integrated particle filter method for the train nonlinear operational status high-precision estimation under non-Gaussian environment. ACCIDENT; ANALYSIS AND PREVENTION 2023; 190:107158. [PMID: 37354851 DOI: 10.1016/j.aap.2023.107158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/29/2023] [Accepted: 06/06/2023] [Indexed: 06/26/2023]
Abstract
For the problem of multi-mode state estimation in actual train operation, this paper proposes a nonlinear non-gaussian high-precision parallel Kalman filter group (NN-HEKFG) integrated Particle Filter. A multi-model Gaussian decomposition of the probability density function for state equations and measurement equations is performed, and each local state model is represented by a multi-dimensional high-order polynomial to establish the expanded dimensional state model. Then, by updating the mean and variance of the local state expanded dimensional model and in turn solving the particle filtering posterior probability density distribution function, the global estimation results are obtained. In reducing the number of Gaussian terms, a new parameter reduction criterion is established, which can effectively carry out the re-identification of parameters such as weights and means, so as to avoid the problem of parameter explosion. The superiority of NN-HEKFG over particle filters and Gaussian sum filters and its effectiveness for train running state estimation are verified by simulating the multi-model running state of trains.
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Affiliation(s)
- Tao Wen
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
| | - Jinzhuo Liu
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Yuan Cao
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Clive Roberts
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom
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Dong J, Lian Z, Xu J, Yue Z. UWB Localization Based on Improved Robust Adaptive Cubature Kalman Filter. SENSORS (BASEL, SWITZERLAND) 2023; 23:2669. [PMID: 36904872 PMCID: PMC10007378 DOI: 10.3390/s23052669] [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: 02/01/2023] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Aiming at the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate kinematic model in ultra-wideband (UWB) systems, this paper proposed an improved robust adaptive cubature Kalman filter (IRACKF). Robust and adaptive filtering can weaken the influence of observed outliers and kinematic model errors on filtering, respectively. However, their application conditions are different, and improper use may reduce positioning accuracy. Therefore, this paper designed a sliding window recognition scheme based on polynomial fitting, which can process the observation data in real-time to identify error types. Simulation and experimental results indicate that compared to the robust CKF, adaptive CKF, and robust adaptive CKF, the IRACKF algorithm reduces the position error by 38.0%, 45.1%, and 25.3%, respectively. The proposed IRACKF algorithm significantly improves the positioning accuracy and stability of the UWB system.
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Luo Q, Yan X, Wang C, Shao Y, Zhou Z, Li J, Hu C, Wang C, Ding J. A SINS/DVL/USBL integrated navigation and positioning IoT system with multiple sources fusion and federated Kalman filter. JOURNAL OF CLOUD COMPUTING: ADVANCES, SYSTEMS AND APPLICATIONS 2022. [DOI: 10.1186/s13677-022-00289-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractThe navigation and positioning subsystem offers important position information for an autonomous underwater vehicle (AUV) system. It plays a crucial role during the underwater exploration and operations of AUV. Many scholars research underwater navigation and positioning. Various improved methods and systems were presented. However, as the diversity of the ocean environment, the random drift of the gyroscope, error accumulation, the variety of tasks, and other negative factors, the navigation and positioning results are uncertain and incredible. The accuracy, stability, and robustness are not guaranteed, which cannot meet the increasing application requirement. Therefore, we put forward a SINS/DVL/USBL integrated navigation and positioning IoT system with multiple resource fusion and a federated Kalman filter. In this method, we first present an improved SINS/DVL combined subsystem with a filtering gain compensation strategy. So we can enhance the accuracy and stability of the navigation and position system. Secondly, we proposed a USBL positioning subsystem with the Kalman filtering acoustic signals to improve USBL positioning performance. Lastly, we present a federated Kalman filter to fuse the positioning information from the SINS/DVL combined positioning subsystem and the USBL positioning subsystem. Through the three methods, we can enhance the positioning accuracy and robustness. Comprehensive simulation results indicated the feasibility and effectiveness of the proposed SINS/DVL/USBL integrated navigation and positioning system, which provides critical reference for other positioning method, and it also offers crucial position information for AUV to achieve high accuracy and efficiency tasks.
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Tang C, He C, Dou L. An IMU/ODM/UWB Joint Localization System Based on Modified Cubature Kalman Filtering. SENSORS 2021; 21:s21144823. [PMID: 34300563 PMCID: PMC8309941 DOI: 10.3390/s21144823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/07/2021] [Accepted: 07/12/2021] [Indexed: 11/16/2022]
Abstract
In this article, a multisensor joint localization system is proposed based on modified cubature Kalman filtering, which aims to improve the accuracy of state estimation under a moderate computational burden in the presence of high process noise. Specifically, first, the covariance of process noise is matched based on adaptive filtering. The inertial measurement unit (IMU), odometer (ODM), and ultra-wideband (UWB) information acquired by the associated sensors is then employed to augment the system state and are fused to lower the influence of process noise. In the presented localization setting, all sensors (IMU/ODM/UWB) are set to work in parallel under the federated Kalman filter (FKF) framework, which can correct the cumulative error of the internal sensor and and can improve the computational efficiency. Two sets of numerical simulations were performed to show that the proposed method can obtain accurate state estimation with a slightly increased computational burden.
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Affiliation(s)
- Chao Tang
- School of Automation, Beijing Institute of Technology, Beijing 100081, China; (C.T.); (C.H.)
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, China
| | - Chengyang He
- School of Automation, Beijing Institute of Technology, Beijing 100081, China; (C.T.); (C.H.)
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, China
| | - Lihua Dou
- School of Automation, Beijing Institute of Technology, Beijing 100081, China; (C.T.); (C.H.)
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, China
- Correspondence:
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Distance Measurements in UWB-Radio Localization Systems Corrected with a Feedforward Neural Network Model. SENSORS 2021; 21:s21072294. [PMID: 33806012 PMCID: PMC8036969 DOI: 10.3390/s21072294] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/19/2021] [Accepted: 03/22/2021] [Indexed: 11/25/2022]
Abstract
An ultra-wideband (UWB) localization system is an alternative in a GPS-denied environment. However, a distance measurement with UWB modules using a two-way communication protocol induces an orientation-dependent error. Previous research studied this error by looking at parameters such as the received power and the channel response signal. In this paper, the neural network (NN) method for correcting the orientation-induced distance error without the need to calculate the signal strength, obtain the channel response or know any parameters of the antenna and the UWB modules is presented. The NN method utilizes only the measured distance and the tag orientation, and implements an NN model obtained by machine learning, using measurements at different distances and orientations of the two UWB modules. The verification of the experimental setup with 12 anchors and a tag shows that with the proposed NN method, 5 cm better root mean square error values (RMSEs) are obtained for the measured distance between the anchors and the tag compared to the calibration method that did not include orientation information. With the least-square estimator, 14 cm RMSE in 3D is obtained with the NN model corrected distances, with a 9 cm improvement compared to when raw distances are used. The method produces better results without the need to obtain the UWB module’s diagnostics parameters that are required to calculate the received signal strength or channel response, and in this way maintain the minimum packet size for the ranging protocol.
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Yuan Y, Wen C, Qiu Y, Sun X. Three State Estimation Fusion Methods Based on the Characteristic Function Filtering. SENSORS 2021; 21:s21041440. [PMID: 33669528 PMCID: PMC7922971 DOI: 10.3390/s21041440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/08/2021] [Accepted: 02/11/2021] [Indexed: 11/29/2022]
Abstract
There are three state estimation fusion methods for a class of strong nonlinear measurement systems, based on the characteristic function filter, namely the centralized filter, parallel filter, and sequential filter. Under ideal communication conditions, the centralized filter can obtain the best state estimation accuracy, and the parallel filter can simplify centralized calculation complexity and improve feasibility; in addition, the performance of the sequential filter is very close to that of the centralized filter and far better than that of the parallel filter. However, the sequential filter can tolerate non-ideal conditions, such as delay and packet loss, and the first two filters cannot operate normally online for delay and will be invalid for packet loss. The performance of the three designed fusion filters is illustrated by three typical cases, which are all better than that of the most popular Extended Kalman Filter (EKF) performance.
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Affiliation(s)
- Yiran Yuan
- Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Y.Y.); (Y.Q.); (X.S.)
| | - Chenglin Wen
- Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Y.Y.); (Y.Q.); (X.S.)
- School of Automation, Guangdong University of Pertrochemical Technology, Maoming 525000, China
- Correspondence: ; Tel.: +86-138-1946-1626
| | - Yiting Qiu
- Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Y.Y.); (Y.Q.); (X.S.)
| | - Xiaohui Sun
- Institute of Systems Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Y.Y.); (Y.Q.); (X.S.)
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Tang C, Dou L. An Improved Game Theory-Based Cooperative Localization Algorithm for Eliminating the Conflicting Information of Multi-Sensors. SENSORS 2020; 20:s20195579. [PMID: 33003410 PMCID: PMC7582326 DOI: 10.3390/s20195579] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/23/2020] [Accepted: 09/24/2020] [Indexed: 11/28/2022]
Abstract
In this article, an improved game theory-based co-localization algorithm is proposed to precisely and cooperatively locate the multi-robot system in the wireless sensor network and efficiently eliminate the information conflict caused by multi-sensor. Specifically, the extended Kalman filter in the original algorithm is replaced by the unscented Kalman filter in the optimized algorithm, which contributes to lower linearization errors and higher localization precision. Then, the computational complexity is analyzed, and the derivative method is introduced to reduce the extra computation burden brought by the unscented Kalman filter. Subsequently, the stability issue resulting from the derivative method is addressed by introducing the singular value decomposition (SVD). In this context, the optimized algorithm is capable of precisely locating the multi-robot system, while maintaining the stability and not increasing the computational burden. Moreover, as demonstrated by the simulation results, the optimized algorithm has greater localization precision than the original algorithm, while they have similar computational burdens.
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Affiliation(s)
- Chao Tang
- School of Automation, Beijing Institute of Technology, Beijing 100081, China;
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, China
| | - Lihua Dou
- School of Automation, Beijing Institute of Technology, Beijing 100081, China;
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, China
- Correspondence:
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