In the field of target tracking and navigation,multi-sensor data fusion has been widely *** of the data fusion algorithms are built on the premise that the sensor observation information is ***,in practical problems,d...
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ISBN:
(纸本)9781509009107
In the field of target tracking and navigation,multi-sensor data fusion has been widely *** of the data fusion algorithms are built on the premise that the sensor observation information is ***,in practical problems,due to the limitation of communication and sensor fault,etc.,data missing or unreliable measurements will happen *** addition,at present a lot of research is aimed at the situation where measurement noise between various sensors is not relevant,and process noise and measurement noise is *** correlation is more *** this paper,a multi-rate multi-sensor data fusion state estimation algorithm with unreliable observations under correlated noises is presented.A numerical example is given to show the feasibility and effectiveness of the presented algorithm.
In practical problems, the dynamic systems are usually nonlinear. In this case, the traditional Kalman filter cannot be used for state estimation or fault detection. The two typical extension based on Kalman filtering...
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In practical problems, the dynamic systems are usually nonlinear. In this case, the traditional Kalman filter cannot be used for state estimation or fault detection. The two typical extension based on Kalman filtering framework is the Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF). Theoretically speaking, UKF is better than EKF when estimation accuracy is concerned, especially for high degree nonlinear cases. This paper is concerned with the state estimation and fault detection problem for a class of nonlinear dynamic systems. A novel fault detection and analyse method is presented based on the period residual of EKF and UKF. For different kind of faults, mainly, the system parameter error, the sensor/data error, EKF and UKF are used, and the estimation and fault detection effects are compared and analyzed.
A sequential fusion and state estimation algorithm for an asynchronous multirate multisensor dynamic system is presented in this *** dynamic system at the finest scale is *** are multiple sensors observing a single ta...
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A sequential fusion and state estimation algorithm for an asynchronous multirate multisensor dynamic system is presented in this *** dynamic system at the finest scale is *** are multiple sensors observing a single target independently with different sampling rates,and the observations are obtained *** present algorithm is shown to be more effective and efficient than the existed *** on a radar tracking system with three sensors are done and show the effectiveness of the present algorithm.
Images taken by different sensors at different time instant with different resolutions are formulated by state space models, and are fused by use of Multiscale Kalman Filter(MKF). The effectiveness of the presented al...
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ISBN:
(纸本)9781479947249
Images taken by different sensors at different time instant with different resolutions are formulated by state space models, and are fused by use of Multiscale Kalman Filter(MKF). The effectiveness of the presented algorithm is shown by comparing it with the wavelet based method through experiments, where four performance measures are used. The performance evaluation indices are the root mean square errors(RMSE), the information entropy(Entropy), the space frequency(SF) and the space visibility(SV). Theretical analysis and experimental results show the effectiveness of the presented algorithm.
The accurate position estimation plays an critical role in the autonomous navigation for Micro Aerial Vehicles(MAV).Global positioning system(GPS) and inertial measurement unit(IMU) are two common sensors for navigati...
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The accurate position estimation plays an critical role in the autonomous navigation for Micro Aerial Vehicles(MAV).Global positioning system(GPS) and inertial measurement unit(IMU) are two common sensors for navigation widely used on MAVs in the urban environment. Both of them have its distinct disadvantages that the GPS is susceptible to environmental interference and the IMU has accumulative errors. To overcome these problems, a GPS/IMU integrated system based on the factor graph optimization is developed in this paper. Unlike the conventional extended Kalman filter(EKF)-based method, the graph optimization method takes the whole trajectory into consideration so that it can achieve enough accuracy even after a long distance. Furthermore, the IMU preintegration method is used to avoid the repeated computation of high-rate IMU *** with the EKF method, the experimental results on the Zurich urban micro aerial vehicle dataset show the superior accuracy of the proposed factor graph optimization algorithm.
This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are *** simplicity,we consider two sensors where one's sampling rate is t...
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ISBN:
(纸本)9781479900305
This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are *** simplicity,we consider two sensors where one's sampling rate is three times as much as the other'*** noises of different sensors are cross-correlated and are also coupled with the system noise of the previous step. By use of the projection theorem and induction hypothesis repeatedly,a distributed fusion estimation algorithm is *** algorithm is proven to be distributed optimal in the sense of Linear Minimum Mean Square Error(LMMSE) and can effectively reduces the oscillation existed in the sequential ***,a numerical example is shown to illustrate the effectiveness of the proposed algorithm.
A strictly positive real control problem for delta operator systems in a low frequency range is presented by using the generalized Kalman–Yakubovic˘–Popov lemma. The objective of the strictly positive real control p...
A strictly positive real control problem for delta operator systems in a low frequency range is presented by using the generalized Kalman–Yakubovic˘–Popov lemma. The objective of the strictly positive real control problem is to design a controller such that the transfer function is strictly positive real and the resulting closed-loop system is stable. Sufficient conditions for the low frequency strictly positive real controller of the closed-loop delta operator systems are presented in terms of solutions to a set of linear matrix inequalities. A numerical example is given to illustrate the effectiveness and potential for the developed techniques.
Recent work has shown that the activation function of the convolutional neural network can meet the Lipschitz condition, then the corresponding convolutional neural network structure can be constructed according to th...
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This paper deals with analysis and synthesis problems of spatially interconnected systems where communicated information may get lost between subsystems. Spatial shift operator and temporal forward shift operator are ...
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This paper deals with analysis and synthesis problems of spatially interconnected systems where communicated information may get lost between subsystems. Spatial shift operator and temporal forward shift operator are introduced to model the interconnected systems as discrete time-space multidimensional linear systems with Markovian jumping parameters which reflect the state of communication channels. To ensure the whole system s well-posedness and mean square stability for a given packet loss rate, a condition is derived through analysis. Then a procedure of designing distributed dynamic output feedback controllers is proposed. The controllers have the same structure as the plants and are solved within the linear matrix inequality (LMI) framework. Finally, we apply these results to study the effect of communication losses on the multiple vehicle platoon controlsystem, which further illustrates the effectiveness of the proposed model and method.
In this paper, a two-stage approach is proposed for the parameter identification of autoregressive moving average with exogenous (ARMAX) variable model. The proposed approach identifies the autoregressive part with ex...
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In this paper, a two-stage approach is proposed for the parameter identification of autoregressive moving average with exogenous (ARMAX) variable model. The proposed approach identifies the autoregressive part with exogenous variable (ARX) by a bias-eliminated least squares method, and the moving average (MA) part by utilizing the parameter relationship between MA process and its inverse. Finally, the noise variance can be computed by using the identified MA parameters. Numerical simulations validate the effectiveness of the proposed approach.
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