We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the prob-lem of tracking a ‘smart’ target, that is, a target being able to be aware it is being tracked and act in a...We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the prob-lem of tracking a ‘smart’ target, that is, a target being able to be aware it is being tracked and act in a manner that makes the future track more difficult. We concern here how to accurately track the target with a care on concealing the observer to a possible extent. We propose a PF method, which is tailored to mix a sensor scheduling technique, called covariance control, within its framework. A Rao-blackwellised unscented Kal-man filter (UKF) is used to produce proposal distributions for the PF method, making it more robust and computationally efficient. We show that the proposed method can balance the tracking filter performance with the observer’s concealment.展开更多
A plasmonic filter and sensor is designed based on an end-coupled ring-groove composited resonator(RGCR).According to the magnetic field distributions of the resonance modes,a horizontal or vertical groove is added to...A plasmonic filter and sensor is designed based on an end-coupled ring-groove composited resonator(RGCR).According to the magnetic field distributions of the resonance modes,a horizontal or vertical groove is added to the perfect ring resonator,and the transmission peaks for the 1st and the 2nd modes can be linearly changed by the length of the groove.In this case,the proposed structure can act as an on-chip optical filter with flexible wavelength manipulation.When the groove is rotated with an angle of?/4,Fano resonance arises due to the mode interference.Dual asymmetric sharp transmission peaks are achieved around the wavelength of the former 2nd resonance mode.High figure of merit and high sensitivity are obtained for the structure,and it is believed that the device can find widely applications in the biochemistry sensing area.The corresponding spectra and the propagation characteristics are numerically investigated by using the finite-difference time-domain method.展开更多
This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Informa...This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Information filter (SCIF). The SCIF propagates the square-root information matrices derived from numerically stable matrix operations and is therefore numerically robust. The SCIF is applied to a highly maneuvering target tracking problem in a distributed sensor network with feedback. The SCIF’s performance is finally compared with the regular cubature information filter and the traditional extended information filter. The results, presented herein, indicate that the SCIF is the most reliable of all three filters and yields a more accurate estimate than the extended information filter.展开更多
Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that di...Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.展开更多
To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary stat...To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary state vector, the attitude measurement system subjected to the attitude sensor fault is modeled by the discrete-time descriptor system. The condition of estimability of such systems is given. And then a Kalman filter of the discrete-time descriptor system is established based on the methodology of the maximum likelihood estimation. With the descriptor Kalman filter, the state vector of the original system and sensor fault can be estimated simultaneously. The proposed method is able to esti-mate an abrupt sensor fault as well as the incipient one. Moreover, it is also effective in the multiple faults scenario. Simulations are conducted to confirm the effectiveness of the proposed method.展开更多
Digital sun sensor is one of the most important sensors used in the Attitude Determination System(ADS)of the satellite.Due to the harsh environmental conditions that exist in the space,various distortions may occur in...Digital sun sensor is one of the most important sensors used in the Attitude Determination System(ADS)of the satellite.Due to the harsh environmental conditions that exist in the space,various distortions may occur in the sun sensor optical system that lead to the reduced accuracy of this equipment.So,it is necessary to recalibrate the optical parameters of the aforementioned sensors.For this purpose,first a novel attitude independent error model is proposed for the SS-411 sun sensor that includes the central point of the CCD array,installation error,filter thickness and sensor misalignment.So,the mutual interfaces between the sensor parameters are considered in the developed model.In order to extract the sensor parameters,a nonlinear optimization technique called the Levenberg–Marquardt is applied to the developed model as a batch algorithm.In addition,the Extended Kalman Filter(EKF)and the Unscented Kalman Filter(UKF)have been utilized as sequential strategies.It will be shown that by considering a worst case of variation amount for sensor parameters,an accuracy improvement of about 17°is achieved by the developed calibration algorithms.Comparison between the developed algorithms represents that UKF has higher accuracy,shorter time convergence but higher computational load.展开更多
WiFi fingerprinting is the method of recording WiFi signal strength from access points （AP） along with the positions at which they were recorded, and later matching those to new mea- surements for indoor positioning...WiFi fingerprinting is the method of recording WiFi signal strength from access points （AP） along with the positions at which they were recorded, and later matching those to new mea- surements for indoor positioning. Inertial positioning utilizes the accelerometer and gyroscopes for pedestrian positioning. However, both methods have their limitations, such as the WiFi fluctuations and the accumulative error of inertial sensors. Usually, the filtering method is used for integrating the two approaches to achieve better location accuracy. In the real environments, especially in the indoor field, the APs could be sparse and short range. To overcome the limitations, a novel particle filter approach based on Rao Blackwellized particle filter （RBPF） is presented in this paper. The indoor environment is divided into several local maps, which are assumed to be independent of each other. The local areas are estimated by the local particle filter, whereas the global areas are com- bined by the global particle filter. The algorithm has been investigated by real field trials using a WiFi tablet on hand with an inertial sensor on foot. It could be concluded that the proposed method reduces the complexity of the positioning algorithm obviously, as well as offers a significant improvement in position accuracy compared to other conventional algorithms, allowing indoor positioning error below 1.2 m.展开更多
A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian ...A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.展开更多
This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchrono...This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability.In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.展开更多
Wireless sensor networks (WSNs) are networked systems that are able to sense various events and report the events to a user to enable appropriate responses. One of security threats to a WSN is false data injection att...Wireless sensor networks (WSNs) are networked systems that are able to sense various events and report the events to a user to enable appropriate responses. One of security threats to a WSN is false data injection attacks in which an attacker steals some sensor nodes in the network and injects forged event messages into the network through the captured nodes. As a result, the intermediate nodes on the forwarding paths of the false event messages waste their limited energy. Additionally, the network cannot provide the user with correct information. There have been many studies on en-route detection of false event messages for WSNs. Yang et al. proposed the commutative cipher-based en-route filtering scheme (CCEF) which establishes a secure session between a sink node and a cluster head (CH) based on the commutative cipher. In CCEF, each intermediate node on the path between the sink node and the CH receives an event message and verifies the authenticity of the session based on a probability. Due to the probabilistic approach, it is hard to adapt to the change of false traffic ratio in the network and energy inefficiency may occur. We propose a filtering scheme which applies a deterministic approach to assign filtering nodes to a given session. The proposed method consumes less energy than that of CCEF without sacrificing security.展开更多
Localization of the sensor nodes is a key supporting technology in wireless sensor networks （WSNs）. In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter （KF） is ...Localization of the sensor nodes is a key supporting technology in wireless sensor networks （WSNs）. In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter （KF） is proposed. Mobile node movement model is analyzed and online sequential iterative method is used to compute location result. The detailed steps of mobile sensor node self-localization adopting extended Kalman filter （EKF） is designed. The simulation results show that the accuracy of the localization estimator scheme designed is better than those of maximum likelihood estimation （MLE） and traditional KF algorithm.展开更多
文摘We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the prob-lem of tracking a ‘smart’ target, that is, a target being able to be aware it is being tracked and act in a manner that makes the future track more difficult. We concern here how to accurately track the target with a care on concealing the observer to a possible extent. We propose a PF method, which is tailored to mix a sensor scheduling technique, called covariance control, within its framework. A Rao-blackwellised unscented Kal-man filter (UKF) is used to produce proposal distributions for the PF method, making it more robust and computationally efficient. We show that the proposed method can balance the tracking filter performance with the observer’s concealment.
基金supported by the National Natural Science Foundation of China under Grants (61405039) and (61475037)Science and Technology Planning Projects of Guangdong Province,China under Grant (2016A 020223013)+4 种基金the Natural Science Foundation of Guangdong Province,China,under Grant (2014A030310300)the Foundation for Distinguished Young Talents in Higher Education of Guangdong,China,under Grant (2014KQNCX066)Research Fund for the Doctoral Program of Higher Education of China under Grant (20134407 110008)Guangzhou Science and Technology Project of Guangdong Province,China under Grant (2016201604030027)the Research Fund of Guangdong University of Technology under Grant (16ZK0041 ,13ZK0387)
文摘A plasmonic filter and sensor is designed based on an end-coupled ring-groove composited resonator(RGCR).According to the magnetic field distributions of the resonance modes,a horizontal or vertical groove is added to the perfect ring resonator,and the transmission peaks for the 1st and the 2nd modes can be linearly changed by the length of the groove.In this case,the proposed structure can act as an on-chip optical filter with flexible wavelength manipulation.When the groove is rotated with an angle of?/4,Fano resonance arises due to the mode interference.Dual asymmetric sharp transmission peaks are achieved around the wavelength of the former 2nd resonance mode.High figure of merit and high sensitivity are obtained for the structure,and it is believed that the device can find widely applications in the biochemistry sensing area.The corresponding spectra and the propagation characteristics are numerically investigated by using the finite-difference time-domain method.
文摘This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Information filter (SCIF). The SCIF propagates the square-root information matrices derived from numerically stable matrix operations and is therefore numerically robust. The SCIF is applied to a highly maneuvering target tracking problem in a distributed sensor network with feedback. The SCIF’s performance is finally compared with the regular cubature information filter and the traditional extended information filter. The results, presented herein, indicate that the SCIF is the most reliable of all three filters and yields a more accurate estimate than the extended information filter.
基金the financial supports of the National Natural Science Foundation of China(No.61304113)the Fundamental Research Funds for the Central Universities,China(No.NS2018018)Qinglan Project of Jiangsu Province
文摘Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.
基金supported by the National Natural Science Foundation of China (60874054)
文摘To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary state vector, the attitude measurement system subjected to the attitude sensor fault is modeled by the discrete-time descriptor system. The condition of estimability of such systems is given. And then a Kalman filter of the discrete-time descriptor system is established based on the methodology of the maximum likelihood estimation. With the descriptor Kalman filter, the state vector of the original system and sensor fault can be estimated simultaneously. The proposed method is able to esti-mate an abrupt sensor fault as well as the incipient one. Moreover, it is also effective in the multiple faults scenario. Simulations are conducted to confirm the effectiveness of the proposed method.
文摘Digital sun sensor is one of the most important sensors used in the Attitude Determination System(ADS)of the satellite.Due to the harsh environmental conditions that exist in the space,various distortions may occur in the sun sensor optical system that lead to the reduced accuracy of this equipment.So,it is necessary to recalibrate the optical parameters of the aforementioned sensors.For this purpose,first a novel attitude independent error model is proposed for the SS-411 sun sensor that includes the central point of the CCD array,installation error,filter thickness and sensor misalignment.So,the mutual interfaces between the sensor parameters are considered in the developed model.In order to extract the sensor parameters,a nonlinear optimization technique called the Levenberg–Marquardt is applied to the developed model as a batch algorithm.In addition,the Extended Kalman Filter(EKF)and the Unscented Kalman Filter(UKF)have been utilized as sequential strategies.It will be shown that by considering a worst case of variation amount for sensor parameters,an accuracy improvement of about 17°is achieved by the developed calibration algorithms.Comparison between the developed algorithms represents that UKF has higher accuracy,shorter time convergence but higher computational load.
基金Supported by National Basic Research Program of China （973 Program） （2010CB731800） and National Natural Science Foundation of China （60974059, 60736026, 61021063）
文摘WiFi fingerprinting is the method of recording WiFi signal strength from access points （AP） along with the positions at which they were recorded, and later matching those to new mea- surements for indoor positioning. Inertial positioning utilizes the accelerometer and gyroscopes for pedestrian positioning. However, both methods have their limitations, such as the WiFi fluctuations and the accumulative error of inertial sensors. Usually, the filtering method is used for integrating the two approaches to achieve better location accuracy. In the real environments, especially in the indoor field, the APs could be sparse and short range. To overcome the limitations, a novel particle filter approach based on Rao Blackwellized particle filter （RBPF） is presented in this paper. The indoor environment is divided into several local maps, which are assumed to be independent of each other. The local areas are estimated by the local particle filter, whereas the global areas are com- bined by the global particle filter. The algorithm has been investigated by real field trials using a WiFi tablet on hand with an inertial sensor on foot. It could be concluded that the proposed method reduces the complexity of the positioning algorithm obviously, as well as offers a significant improvement in position accuracy compared to other conventional algorithms, allowing indoor positioning error below 1.2 m.
基金the National "863" High Technology Development Program (2006AA01Z216)the MajorResearch Program of the Science and Technology Commission of Shanghai Municipality of China (054SGA1001).
文摘A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.
基金supported by the National Natural Science Foundation of China(6147322711472222)+3 种基金the Fundamental Research Funds for the Central Universities(3102015ZY001)the Aerospace Technology Support Fund of China(2014-HT-XGD)the Natural Science Foundation of Shaanxi Province(2015JM6304)the Aeronautical Science Foundation of China(20151353018)
文摘This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability.In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.
文摘Wireless sensor networks (WSNs) are networked systems that are able to sense various events and report the events to a user to enable appropriate responses. One of security threats to a WSN is false data injection attacks in which an attacker steals some sensor nodes in the network and injects forged event messages into the network through the captured nodes. As a result, the intermediate nodes on the forwarding paths of the false event messages waste their limited energy. Additionally, the network cannot provide the user with correct information. There have been many studies on en-route detection of false event messages for WSNs. Yang et al. proposed the commutative cipher-based en-route filtering scheme (CCEF) which establishes a secure session between a sink node and a cluster head (CH) based on the commutative cipher. In CCEF, each intermediate node on the path between the sink node and the CH receives an event message and verifies the authenticity of the session based on a probability. Due to the probabilistic approach, it is hard to adapt to the change of false traffic ratio in the network and energy inefficiency may occur. We propose a filtering scheme which applies a deterministic approach to assign filtering nodes to a given session. The proposed method consumes less energy than that of CCEF without sacrificing security.
基金Project supported by the Shanghai Leading Academic Discipcine Project (Grant No.S30108)the National Natural Science Foundation of China (Grant No.60872021)the Science and Technology Commission of Shanghai Municipality (Grant No.08DZ2231100)
文摘Localization of the sensor nodes is a key supporting technology in wireless sensor networks （WSNs）. In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter （KF） is proposed. Mobile node movement model is analyzed and online sequential iterative method is used to compute location result. The detailed steps of mobile sensor node self-localization adopting extended Kalman filter （EKF） is designed. The simulation results show that the accuracy of the localization estimator scheme designed is better than those of maximum likelihood estimation （MLE） and traditional KF algorithm.
