Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624548
Haoyu Yang, Zheng Hu, Dongchen Li, Tiancheng Li
We propose an effective target locating approach based on the fingerprint fusion positioning (FFP) method which combines the time-difference of arrival (TDOA) and the received signal strength in the stationary 3D scenarios. The FFP method fuses pedestrian dead reckoning (PDR) estimation to solve the moving target localization problem. We also introduce new auxiliary parameters to estimate the target motion state. For the case study, eight access stationary points are placed on a bookshelf and hypermarket; one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf. We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor, weighted k-nearest neighbor, pure TDOA and Bayesian frameworks. The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error, especially in the 3D scenarios. Simulation results corroborate the effectiveness of our approach.
{"title":"An Effective 3D Indoor Localization Approach Based on Fingerprint Fusion Positioning","authors":"Haoyu Yang, Zheng Hu, Dongchen Li, Tiancheng Li","doi":"10.1109/ICCAIS52680.2021.9624548","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624548","url":null,"abstract":"We propose an effective target locating approach based on the fingerprint fusion positioning (FFP) method which combines the time-difference of arrival (TDOA) and the received signal strength in the stationary 3D scenarios. The FFP method fuses pedestrian dead reckoning (PDR) estimation to solve the moving target localization problem. We also introduce new auxiliary parameters to estimate the target motion state. For the case study, eight access stationary points are placed on a bookshelf and hypermarket; one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf. We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor, weighted k-nearest neighbor, pure TDOA and Bayesian frameworks. The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error, especially in the 3D scenarios. Simulation results corroborate the effectiveness of our approach.","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124912802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624509
Changbeom Shim, D. Kim
The problem of tracking multiple objects has been investigated in various research and industrial fields. Among existing methods, random finite set (RFS) solutions such as the generalized labeled multi-Bernoulli (GLMB) filter has provided efficient solutions with solid theoretical justifications. Furthermore, implementations show that the GLMB approach is efficient under challenging scenarios. In this paper, we study an RFS-based method for multi-object tracking (MOT) through a simple data structure for label partitioning. Specifically, grid index structure based techniques for splitting a label space and a label-partitioned GLMB tracker are investigated. We finally evaluate the performance of label partitioning and the GLMB filter via various means such as visualization, execution time, and MOT metrics.
{"title":"Space-oriented Label Partitioning for Multi-object Tracking","authors":"Changbeom Shim, D. Kim","doi":"10.1109/ICCAIS52680.2021.9624509","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624509","url":null,"abstract":"The problem of tracking multiple objects has been investigated in various research and industrial fields. Among existing methods, random finite set (RFS) solutions such as the generalized labeled multi-Bernoulli (GLMB) filter has provided efficient solutions with solid theoretical justifications. Furthermore, implementations show that the GLMB approach is efficient under challenging scenarios. In this paper, we study an RFS-based method for multi-object tracking (MOT) through a simple data structure for label partitioning. Specifically, grid index structure based techniques for splitting a label space and a label-partitioned GLMB tracker are investigated. We finally evaluate the performance of label partitioning and the GLMB filter via various means such as visualization, execution time, and MOT metrics.","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126741884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624650
Huifen Hong, Wenwu Yu, He Wang
This paper deals with output feedback consensus problem for second-order multi-agent systems with heterogeneous nonlinearities and disturbances. A novel distributed observer is first designed for each agent to estimate its velocity information in finite time when there exist Lipschitz-type nonlinearity and bounded disturbance simultaneously. Then based on the observers and homogenous method, a new finite-time consensus protocol is proposed. Finally, a numerical example is given to demonstrate the effectiveness of the proposed protocol.
{"title":"Finite-time output feedback consensus for second-order heterogeneous multi-agent systems","authors":"Huifen Hong, Wenwu Yu, He Wang","doi":"10.1109/ICCAIS52680.2021.9624650","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624650","url":null,"abstract":"This paper deals with output feedback consensus problem for second-order multi-agent systems with heterogeneous nonlinearities and disturbances. A novel distributed observer is first designed for each agent to estimate its velocity information in finite time when there exist Lipschitz-type nonlinearity and bounded disturbance simultaneously. Then based on the observers and homogenous method, a new finite-time consensus protocol is proposed. Finally, a numerical example is given to demonstrate the effectiveness of the proposed protocol.","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115548898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624554
Jiawei Tang, Weifeng Liu, Seng Wang, Shibo Gao
Aiming at the path planning and hunting control problem of multiple dynamic targets in a first-order nonlinear system, an algorithm based on an event-triggered strategy is proposed: the path planning process first uses the Kalman tracking algorithm to obtain the target state estimation, and then design the controller Control the multi agent to track and generate the path according to the estimation of the target. This article uses matrix theory, graph theory and Lyapunov stability methods to obtain the conditions for system stability. The whole round-up process is divided into three stages. The first is the cruise stage, where multiple agents maintain their leadership and follow the formation to cruise and move. The second is the pre round up stage. When the distance between the agent and the target is less than a certain value, an event is triggered. Pre round up, and the third is final round-up. The event is triggered after maintaining the pre round up formation for a certain period of time, and the round-up range is reduced to achieve round-up. Simulation experiments show that the event triggered joint path planning and hunting control algorithm enables the multi agent system to form and maintain multiple hunting formations to hunt multiple dynamic targets, and realize the hunting of multiple dynamic targets.
{"title":"Path planning and hunting control of multiple dynamic targets based on event-triggered strategy","authors":"Jiawei Tang, Weifeng Liu, Seng Wang, Shibo Gao","doi":"10.1109/ICCAIS52680.2021.9624554","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624554","url":null,"abstract":"Aiming at the path planning and hunting control problem of multiple dynamic targets in a first-order nonlinear system, an algorithm based on an event-triggered strategy is proposed: the path planning process first uses the Kalman tracking algorithm to obtain the target state estimation, and then design the controller Control the multi agent to track and generate the path according to the estimation of the target. This article uses matrix theory, graph theory and Lyapunov stability methods to obtain the conditions for system stability. The whole round-up process is divided into three stages. The first is the cruise stage, where multiple agents maintain their leadership and follow the formation to cruise and move. The second is the pre round up stage. When the distance between the agent and the target is less than a certain value, an event is triggered. Pre round up, and the third is final round-up. The event is triggered after maintaining the pre round up formation for a certain period of time, and the round-up range is reduced to achieve round-up. Simulation experiments show that the event triggered joint path planning and hunting control algorithm enables the multi agent system to form and maintain multiple hunting formations to hunt multiple dynamic targets, and realize the hunting of multiple dynamic targets.","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"334 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116439668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624637
Chaoqun Niu, Dongjie Zhao, A. Nemati, Wanyue Jiang, Xian Li, S. Ge
Aiming at the problems of low degree of automation, troublesome technical measurement method and low efficiency in the current elevator track deformation detection method, a high degree of automation elevator track quality detection robot is designed and developed in this paper. The robot fully considers practicability and high efficiency in mechanical design and adds a feedback mechanism to the control unit for control optimization, so that it can climb the elevator track quickly and smoothly, and uses robot vision to detect the track deformation, main control principle is to use the computer to smoothly regulate the speed of the motor, to detect the posture of the robot in the climbing process in real-time, and to feed it back to the micro industrial computer for autonomous adjustment, to ensure that the robot runs smoothly on the elevator guide rail at all times.
{"title":"A Robotic Device For Measuring Alignment Of Elevator Guide Rails","authors":"Chaoqun Niu, Dongjie Zhao, A. Nemati, Wanyue Jiang, Xian Li, S. Ge","doi":"10.1109/ICCAIS52680.2021.9624637","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624637","url":null,"abstract":"Aiming at the problems of low degree of automation, troublesome technical measurement method and low efficiency in the current elevator track deformation detection method, a high degree of automation elevator track quality detection robot is designed and developed in this paper. The robot fully considers practicability and high efficiency in mechanical design and adds a feedback mechanism to the control unit for control optimization, so that it can climb the elevator track quickly and smoothly, and uses robot vision to detect the track deformation, main control principle is to use the computer to smoothly regulate the speed of the motor, to detect the posture of the robot in the climbing process in real-time, and to feed it back to the micro industrial computer for autonomous adjustment, to ensure that the robot runs smoothly on the elevator guide rail at all times.","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128246182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624499
Lin Sun, Jianpo Liu, Yuanqing Liu, Baoqing Li
Radar high-resolution range profile (HRRP) target recognition is an active part of radar target recognition (ATR). The current radar HRRP target data has the characteristics of less data volume. At the same time, support vector data description has limited ability to extract the deep features of the signal. To address this issue, we propose a soft-boundary Deep SVDD with LSTM (long short-term memory). The framework consists of an autoencoder, an LSTM neural network layer, and an SVDD hyper-sphere. The autoencoder generates the deep signal features, and the LSTM layer can extract the time-related features. The distance from the feature point to the center of the hyper-sphere is the classification judgment condition. The neural network parameters and the hyper-sphere are trained to be the optimal value. We carry out experiments on a dataset with a small volume. The result and the Received operation characteristic (ROC) curve show that the classifier has good performance. The area under ROC (AUC) value is close to 87%–94%.
{"title":"HRRP Target Recognition Based On Soft-Boundary Deep SVDD With LSTM","authors":"Lin Sun, Jianpo Liu, Yuanqing Liu, Baoqing Li","doi":"10.1109/ICCAIS52680.2021.9624499","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624499","url":null,"abstract":"Radar high-resolution range profile (HRRP) target recognition is an active part of radar target recognition (ATR). The current radar HRRP target data has the characteristics of less data volume. At the same time, support vector data description has limited ability to extract the deep features of the signal. To address this issue, we propose a soft-boundary Deep SVDD with LSTM (long short-term memory). The framework consists of an autoencoder, an LSTM neural network layer, and an SVDD hyper-sphere. The autoencoder generates the deep signal features, and the LSTM layer can extract the time-related features. The distance from the feature point to the center of the hyper-sphere is the classification judgment condition. The neural network parameters and the hyper-sphere are trained to be the optimal value. We carry out experiments on a dataset with a small volume. The result and the Received operation characteristic (ROC) curve show that the classifier has good performance. The area under ROC (AUC) value is close to 87%–94%.","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126704298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624603
Xiaolong Chen, J. Guan, Jibin Zheng, Yong Huang
Radar maneuvering target detection under complex background has always been a worldwide difficult problem in the field of radar signal processing. The range and Doppler migration would cause the target's energy distributed among multiple range or Doppler bins. Moreover, the heavy computational burden of long-time integration is a challenging problem that needs to be solved. A novel non-parametric searching sparse long-time coherent integration (LTCI) method is proposed for highly maneuverable target detection using MIMO radar. Time-reversed and non-uniform resampling (TRNU) are designed to remove the migrations and adaptive sparse Fourier transform is employed to reduce the computational burden. The maneuvering target would appear as peaks in the TRNU-sparse LTCI (SLTCI) domain with less clutter. Experiments using S-band real radar data indicate that the proposed method can achieve a good balance between computational cost and integration performance compared with classical methods, e.g., Radon-Fourier transform (RFT) and Radon-fractional FT (RFRFT).
{"title":"Non-Parametric Searching Sparse Long-Time Coherent Integration Method for Highly Maneuverable Target of MIMO Radar","authors":"Xiaolong Chen, J. Guan, Jibin Zheng, Yong Huang","doi":"10.1109/ICCAIS52680.2021.9624603","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624603","url":null,"abstract":"Radar maneuvering target detection under complex background has always been a worldwide difficult problem in the field of radar signal processing. The range and Doppler migration would cause the target's energy distributed among multiple range or Doppler bins. Moreover, the heavy computational burden of long-time integration is a challenging problem that needs to be solved. A novel non-parametric searching sparse long-time coherent integration (LTCI) method is proposed for highly maneuverable target detection using MIMO radar. Time-reversed and non-uniform resampling (TRNU) are designed to remove the migrations and adaptive sparse Fourier transform is employed to reduce the computational burden. The maneuvering target would appear as peaks in the TRNU-sparse LTCI (SLTCI) domain with less clutter. Experiments using S-band real radar data indicate that the proposed method can achieve a good balance between computational cost and integration performance compared with classical methods, e.g., Radon-Fourier transform (RFT) and Radon-fractional FT (RFRFT).","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123678530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624615
Qing Xiong, Wen Hu, Yue Zhao, Hao Dong, Yirong Yao
In the radar target tracking stage, the repeater deception jamming can produce multiple false targets around the real target after matched filtering, which makes radar lose tracking of the real target. In this paper, a frequency-phase joint agility anti-repeater deception jamming waveform for Multiple Input Multiple Output (MIMO) radar is designed by the memristive radial basis function neural network (memristive-RBFNN), and the optimization of the waveform modulation coding is transformed into the optimization of the basis function parameters of the memristive-RBFNN. The memristive-RBFNN generates the frequency-phase joint agile waveform modulation sequence directly, thus greatly reduced the computational complexity of the waveform optimization algorithm. The designed frequency-phase joint agile waveform reduced the correlation between real target echo signal and repeater deception jamming to realize the suppression of repeater deception jamming. The simulation analyzes the computation complexity of proposed algorithm, and the jamming suppression performance is also given to verify the feasibility of this method proposed in this paper.
{"title":"Anti-jamming Waveform Design Based on Memristive RBF Neural Network in MIMO Radar","authors":"Qing Xiong, Wen Hu, Yue Zhao, Hao Dong, Yirong Yao","doi":"10.1109/ICCAIS52680.2021.9624615","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624615","url":null,"abstract":"In the radar target tracking stage, the repeater deception jamming can produce multiple false targets around the real target after matched filtering, which makes radar lose tracking of the real target. In this paper, a frequency-phase joint agility anti-repeater deception jamming waveform for Multiple Input Multiple Output (MIMO) radar is designed by the memristive radial basis function neural network (memristive-RBFNN), and the optimization of the waveform modulation coding is transformed into the optimization of the basis function parameters of the memristive-RBFNN. The memristive-RBFNN generates the frequency-phase joint agile waveform modulation sequence directly, thus greatly reduced the computational complexity of the waveform optimization algorithm. The designed frequency-phase joint agile waveform reduced the correlation between real target echo signal and repeater deception jamming to realize the suppression of repeater deception jamming. The simulation analyzes the computation complexity of proposed algorithm, and the jamming suppression performance is also given to verify the feasibility of this method proposed in this paper.","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133910320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624512
Hong-Hai Qiao, Zheng-hong Deng, Li Gao, Qun Song, Qian Chi
Influenza viruses are serious threat to human health in current society. The epidemic features of influenza viruses can be obtained effectively from the statistics of each historical stage. Complex networks theories are reliable tools to handle the issues of historical information division. In this paper, we propose an analysis technique to divide the historical stages of influenza information. Primarily, influenza networks are established using a visibility graph model. After that, the modules of networks are uncovered using the Arenas, Fernández and Gómez algorithm. Finally, on the basis of the clustering results, historical information of influenza virus can be reasonably divided into several stages. The simulations provide some conclusions. Time series networks are established reasonably, which can obtain a better results. The number of stages is reduced rationally, which is beneficial to gather the influenza information. Unreasonable statistics are avoided effectively, and some critical periods affecting the number of influenza like illness can be discovered.
{"title":"Efficiency analysis technique via time series network for the historical stage division of influenza information in China","authors":"Hong-Hai Qiao, Zheng-hong Deng, Li Gao, Qun Song, Qian Chi","doi":"10.1109/ICCAIS52680.2021.9624512","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624512","url":null,"abstract":"Influenza viruses are serious threat to human health in current society. The epidemic features of influenza viruses can be obtained effectively from the statistics of each historical stage. Complex networks theories are reliable tools to handle the issues of historical information division. In this paper, we propose an analysis technique to divide the historical stages of influenza information. Primarily, influenza networks are established using a visibility graph model. After that, the modules of networks are uncovered using the Arenas, Fernández and Gómez algorithm. Finally, on the basis of the clustering results, historical information of influenza virus can be reasonably divided into several stages. The simulations provide some conclusions. Time series networks are established reasonably, which can obtain a better results. The number of stages is reduced rationally, which is beneficial to gather the influenza information. Unreasonable statistics are avoided effectively, and some critical periods affecting the number of influenza like illness can be discovered.","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134360737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-14DOI: 10.1109/ICCAIS52680.2021.9624490
Donglin Zhang, Z. Duan, Pengcheng Wang, Yonghe Zhang
The celebrated Kalman filter (KF) is the workhorse and widely applied to many practical state estimation problems. It is optimal for linear systems with white noise. However, for systems with colored process and measurement noises, the KF loses its optimality and even diverges. In this paper, by modeling the colored noise as ARMA (auto-regressive moving average) model from its spectrum, two state estimators for systems with multichannel higher-order colored noises are proposed. One is state-augmented optimal filter (SAOF), and the other is measurement-differenced optimal one-step lag smoother (MDOLS). These two state estimators are both theoretically optimal in the sense of minimizing the mean square error among all linear state estimators. Illustrative examples demonstrate the effectiveness of the proposed state estimators.
{"title":"Spacecraft State Estimation with Multichannel Higher-order ARMA Colored Noises","authors":"Donglin Zhang, Z. Duan, Pengcheng Wang, Yonghe Zhang","doi":"10.1109/ICCAIS52680.2021.9624490","DOIUrl":"https://doi.org/10.1109/ICCAIS52680.2021.9624490","url":null,"abstract":"The celebrated Kalman filter (KF) is the workhorse and widely applied to many practical state estimation problems. It is optimal for linear systems with white noise. However, for systems with colored process and measurement noises, the KF loses its optimality and even diverges. In this paper, by modeling the colored noise as ARMA (auto-regressive moving average) model from its spectrum, two state estimators for systems with multichannel higher-order colored noises are proposed. One is state-augmented optimal filter (SAOF), and the other is measurement-differenced optimal one-step lag smoother (MDOLS). These two state estimators are both theoretically optimal in the sense of minimizing the mean square error among all linear state estimators. Illustrative examples demonstrate the effectiveness of the proposed state estimators.","PeriodicalId":280912,"journal":{"name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132657562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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