An interval Kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applica...An interval Kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applicable only to linear interval systems, the extended interval Kalman filter (EIKF) algorithm for non linear integrated systems is developed. A high dynamic aircraft trajectory is designed to test the algorithm developed. The results of computer simulation indicate that the EIKF algorithm is consistent with the traditional SKF scheme, and is also effective for uncertain non linear integrated system.展开更多
To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two diff...To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.展开更多
Global Positioning System （GPS） /Inertial Navigation System （INS） integrated system is continuously gaining research interests in many positioning and navigation fields. Kalman filtering-based integrated algorithm...Global Positioning System （GPS） /Inertial Navigation System （INS） integrated system is continuously gaining research interests in many positioning and navigation fields. Kalman filtering-based integrated algorithm has some drawbacks on stability, computation load, robustness, and system observability performances. Based on neural network technology, a new GPS/INS integration filtering algorithm is studied for an integration scheme of the attitude determination GPS/INS integrated navigation system. Through some theoretic analysis, this algorithm not only has good estimation performance, but also has better robustness to the system model and noise than the traditional Kalman algorithm. To assess the performance of the proposed integrated model more deeply, some simulation is done to compare with the traditional Kalman filter model. The results indicate that the proposed model provides a significant improvement in some performance, such as accuracy, stability, robustness, and so on.展开更多
This paper presents a new approach to estimate the true position of an unmanned aerial vehicle （UAV） in the conditions of spoofing attacks on global positioning system （GPS） receivers. This approach consists of tw...This paper presents a new approach to estimate the true position of an unmanned aerial vehicle （UAV） in the conditions of spoofing attacks on global positioning system （GPS） receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system （INS） and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted particle swarm optimization filter （PSOF） to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle＇s experiences, application of PSOF algorithm in the INS/GPS integ- ration system leads to more precise positioning compared with general particle filter （PF） and adaptive unscented particle filer （AUPF） in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estim- ating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error （RMSE） test.展开更多
Does the Brain Regulate the Immune System?While long considered to be separate entities,increasing evidence has demonstrated that thebrain and theperipheral immune system interact through bidirectional feedback[1].The...Does the Brain Regulate the Immune System?While long considered to be separate entities,increasing evidence has demonstrated that thebrain and theperipheral immune system interact through bidirectional feedback[1].The brain hosts a vibrant immune environment(e.g.microglia and perivascular macrophages)that is responsible for maintaining the integrity of the central nervous system[2].In addition,the brain is the central regulator of the rest of the body and its homeostasis.展开更多
Integrity is an important index for GNSS-based navigation and positioning, and the receiver autonomous integrity monitoring (RAIM) algorithm has been presented for integrity applications. In the integrated navigation ...Integrity is an important index for GNSS-based navigation and positioning, and the receiver autonomous integrity monitoring (RAIM) algorithm has been presented for integrity applications. In the integrated navigation systems of a global navigation satellite system (GNSS) and inertial navigation system (INS),the conventional RAIM algorithm has been developed to extended receiver autonomous integrity monitoring (ERAIM). However, the ERAIM algorithm may fail and a false alarm may generate once the measurements are contaminated by significant outliers, and this problem is rarely discussed in the existing literatures. In this paper, a robust fault detection and the corresponding data processing algorithm are proposed based on the ERAIM algorithm and the robust estimation. In the proposed algorithm, weights of the measurements are adjusted with the equivalent weight function, and the efficiency of the outlier detection and identification is improved, therefore, the estimates become more reliable, and the probability of the false alarm is decreased. Experiments with the data collected under actual environments are implemented, and results indicate that the proposed algorithm is more efficient than the conventional ERAIM algorithm for multiple outliers and a better filtering performance is achieved.展开更多
This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the ...This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test.展开更多
Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide metho...Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to detect the position of car accident. The models consider GPS/INS-based navigation algorithm, calibration of navigational sensors, a de-nosing method as long as vehicle accident, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, the location-based accident detection model is tested in different scenarios. The results illustrate that under harsh environments with no GPS signal, location of accident can be detected. Also results confirm that calibration of sensors has an important role in position correction algorithm. Finally, the results present that the proposed accident detection algorithm can recognize accidents and related its positions.展开更多
Nowadays, GPS （global positioning system） receivers are aided by INS （inertial navigation systems） to achieve more precision and stability in land-vehicular navigation. KF （Kalman filter） is a conventional metho...Nowadays, GPS （global positioning system） receivers are aided by INS （inertial navigation systems） to achieve more precision and stability in land-vehicular navigation. KF （Kalman filter） is a conventional method which is used for the navigation system to estimate the navigational parameters, when INS measurements are fused with GPS data. However, new generation of INS, which relies on MEMS （micro-electro-mechanical systems） based low-cost IMUs （inertial measurement units） for the land navigation systems, decreases the accuracy and the robustness of navigation system due to their inherent errors. This paper provides a new method for fusing the low-cost IMU and GPS measurements. The proposed method is based on KF aided by AF1S （adaptive fuzzy inference systems） as a promising solution to overcome the mentioned problems. The results of this study show the efficiency of the proposed method to reduce the navigation system errors in comparison with KF alone.展开更多
文摘An interval Kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applicable only to linear interval systems, the extended interval Kalman filter (EIKF) algorithm for non linear integrated systems is developed. A high dynamic aircraft trajectory is designed to test the algorithm developed. The results of computer simulation indicate that the EIKF algorithm is consistent with the traditional SKF scheme, and is also effective for uncertain non linear integrated system.
基金supported by the National Natural Science Foundation of China (60902055)
文摘To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.
基金National Natural Science Foundation of China (10402034)
文摘Global Positioning System （GPS） /Inertial Navigation System （INS） integrated system is continuously gaining research interests in many positioning and navigation fields. Kalman filtering-based integrated algorithm has some drawbacks on stability, computation load, robustness, and system observability performances. Based on neural network technology, a new GPS/INS integration filtering algorithm is studied for an integration scheme of the attitude determination GPS/INS integrated navigation system. Through some theoretic analysis, this algorithm not only has good estimation performance, but also has better robustness to the system model and noise than the traditional Kalman algorithm. To assess the performance of the proposed integrated model more deeply, some simulation is done to compare with the traditional Kalman filter model. The results indicate that the proposed model provides a significant improvement in some performance, such as accuracy, stability, robustness, and so on.
文摘This paper presents a new approach to estimate the true position of an unmanned aerial vehicle （UAV） in the conditions of spoofing attacks on global positioning system （GPS） receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system （INS） and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted particle swarm optimization filter （PSOF） to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle＇s experiences, application of PSOF algorithm in the INS/GPS integ- ration system leads to more precise positioning compared with general particle filter （PF） and adaptive unscented particle filer （AUPF） in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estim- ating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error （RMSE） test.
文摘Does the Brain Regulate the Immune System?While long considered to be separate entities,increasing evidence has demonstrated that thebrain and theperipheral immune system interact through bidirectional feedback[1].The brain hosts a vibrant immune environment(e.g.microglia and perivascular macrophages)that is responsible for maintaining the integrity of the central nervous system[2].In addition,the brain is the central regulator of the rest of the body and its homeostasis.
基金National Natural Science Foundation of China(No.41774026)。
文摘Integrity is an important index for GNSS-based navigation and positioning, and the receiver autonomous integrity monitoring (RAIM) algorithm has been presented for integrity applications. In the integrated navigation systems of a global navigation satellite system (GNSS) and inertial navigation system (INS),the conventional RAIM algorithm has been developed to extended receiver autonomous integrity monitoring (ERAIM). However, the ERAIM algorithm may fail and a false alarm may generate once the measurements are contaminated by significant outliers, and this problem is rarely discussed in the existing literatures. In this paper, a robust fault detection and the corresponding data processing algorithm are proposed based on the ERAIM algorithm and the robust estimation. In the proposed algorithm, weights of the measurements are adjusted with the equivalent weight function, and the efficiency of the outlier detection and identification is improved, therefore, the estimates become more reliable, and the probability of the false alarm is decreased. Experiments with the data collected under actual environments are implemented, and results indicate that the proposed algorithm is more efficient than the conventional ERAIM algorithm for multiple outliers and a better filtering performance is achieved.
基金The MKE(the Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2012-H0301-12-2006)
文摘This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test.
文摘Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to detect the position of car accident. The models consider GPS/INS-based navigation algorithm, calibration of navigational sensors, a de-nosing method as long as vehicle accident, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, the location-based accident detection model is tested in different scenarios. The results illustrate that under harsh environments with no GPS signal, location of accident can be detected. Also results confirm that calibration of sensors has an important role in position correction algorithm. Finally, the results present that the proposed accident detection algorithm can recognize accidents and related its positions.
文摘Nowadays, GPS （global positioning system） receivers are aided by INS （inertial navigation systems） to achieve more precision and stability in land-vehicular navigation. KF （Kalman filter） is a conventional method which is used for the navigation system to estimate the navigational parameters, when INS measurements are fused with GPS data. However, new generation of INS, which relies on MEMS （micro-electro-mechanical systems） based low-cost IMUs （inertial measurement units） for the land navigation systems, decreases the accuracy and the robustness of navigation system due to their inherent errors. This paper provides a new method for fusing the low-cost IMU and GPS measurements. The proposed method is based on KF aided by AF1S （adaptive fuzzy inference systems） as a promising solution to overcome the mentioned problems. The results of this study show the efficiency of the proposed method to reduce the navigation system errors in comparison with KF alone.
