We compare the performance of two distributed nonlinear estimators for a multi-vehicle flocking system using range measurements only. The estimators are the Distributed Extended Kalman Filter (DEKF) and the Markov Cha...
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We compare the performance of two distributed nonlinear estimators for a multi-vehicle flocking system using range measurements only. The estimators are the Distributed Extended Kalman Filter (DEKF) and the Markov Chain Distributed Particle Filter (MCDPF), where the distributed implementation in both cases is done using consensus-type algorithms. The performance of the estimators is compared as the system complexity (number of vehicles) and measurement frequency are varied. It is shown that for simple systems (few vehicles) or high measurement frequency the DEKF method has lower expected error than MCDPF, while for complex systems (many vehicles) or low measurement frequency the MCDPF method is both more robust and more accurate.
For the modelling of time series, multivariate linear and nonlinear systems of equations became a standard tool. These models are also applied for non-stationary processes. However, estimation results in finite sample...
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For the modelling of time series, multivariate linear and nonlinear systems of equations became a standard tool. These models are also applied for non-stationary processes. However, estimation results in finite samples might depend on the specification of the model dynamics. We propose a method for automatic identification of the dynamic part of VEC–models. Model selection is based on a modified information criterion. The resulting complex discrete optimization problem is tackled using a hybrid heuristic. We present the algorithm and results of a simulation study indicating the performance both with regard to the dynamic structure and the rank selection in the VEC–model.
This paper deals with the adaptive control of linearly parameterized discrete-time nonlinear system in the presence of bounded disturbances and unmodeled dynamics. A new adaptive law is presented. No a priori knowledg...
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This paper deals with the adaptive control of linearly parameterized discrete-time nonlinear system in the presence of bounded disturbances and unmodeled dynamics. A new adaptive law is presented. No a priori knowledge about the bounds on both the plant parameters and unmodeled uncertainty is required to implement the estimation algorithm. The adaptive scheme is free from singularity. This is achieved through the use of some time-varying adaptation gain. It is established that if the plant model nonlinearity is sector-bounded then the BIBO stability of the closed-loop system will be guaranteed. Simulation results are given to demonstrate the performance of the adaptive controller.
Relying on a simple flag-assisted mechanism, a multigroup priority queueing (MGPQ) medium access control (MAC) protocol is proposed for the wireless networks with multipacket reception ( MPR). The proposed MGPQ scheme...
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Relying on a simple flag-assisted mechanism, a multigroup priority queueing (MGPQ) medium access control (MAC) protocol is proposed for the wireless networks with multipacket reception ( MPR). The proposed MGPQ scheme is capable of overcoming two major performance bottlenecks inherent in the existing MPR MAC protocols. First, the proposed solution can automatically produce the list of active users by observing the network traffic conditions, remove the need of active user estimation algorithm, and thus can largely reduce the algorithm complexity. Second, the packet blocking constraint imposed on the active users for keeping compliant with prediction is relaxed. As a result, the proposed MGPQ is not only applicable to both homogeneous and heterogeneous cases, but also outperforms the existing MPRMAC protocols. Simulation results show that the network throughput can be improved by 40% maximum and 14% average as compared with the well- known dynamic queue (DQ) MAC protocol.
This paper discusses navigation algorithm architecture for estimating aircraft position and attitude during approach and landing using landmarks image processing of the Earth. In general, automatic landing can be real...
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This paper discusses navigation algorithm architecture for estimating aircraft position and attitude during approach and landing using landmarks image processing of the Earth. In general, automatic landing can be realized with the help of high accuracy real-time estimation of aircraft position and attitude. In the proposed algorithm, the reference images of the Earth surface saved in the onboard memory are matched with the current images captured from onboard electro-optical camera. The matching information is used for INS correction by means of the Kalman filter with a radio altimeter. For application of this algorithm architecture, image matching algorithm and decision of more informative reference images for improvement in matching efficiency have been studied. The results indicate that it is possible to use the proposed navigation algorithm for aircraft automatic landing without the use of ground equipment.
Here we consider a corner target positioning problem under the condition of unknown walls' positions using time-division multiple-input-multiple-output (MIMO) radar. Firstly, we propose an estimation algorithm for...
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Here we consider a corner target positioning problem under the condition of unknown walls' positions using time-division multiple-input-multiple-output (MIMO) radar. Firstly, we propose an estimation algorithm for the walls' positions using the Back-Projection (BP) imaging algorithm. Then, after obtaining the BP image of target, we extract the focus regions produced by different multipaths. Finally, we obtain the position of the target based on the extracted walls' positions and the focus regions. Simulations and experiments results validate the proposed algorithm.
This paper is concerned with the mechanism of blackouts in China power system from the viewpoint of self-organized criticality. By using two estimation algorithms of scaled window variance （SWV） and rescaled rangest...
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This paper is concerned with the mechanism of blackouts in China power system from the viewpoint of self-organized criticality. By using two estimation algorithms of scaled window variance （SWV） and rescaled rangestatistics （R/S）, this paper studies the blackout data in China power system during 1988-1997. The result of analysis shows that the blackout data of 1994-1997 coincides well with the autocorrelation. Furthermore, it is found that the function of blackout probability vs. blackout size exhibits power law distribution.
The paper is concerned with parameter estimation of an ideal controller for a static plant. An estimation algorithm is proposed and the estimate convergence to a bounded domain as determined by the covariance matrix o...
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The paper is concerned with parameter estimation of an ideal controller for a static plant. An estimation algorithm is proposed and the estimate convergence to a bounded domain as determined by the covariance matrix of the vector of observed variables is provable. An algorithm modification is proposed for the case when a priori information on the parameters to be estimated is known. The algorithm is applied to control of the oxygen-converter steel melting.
Internal temperature is crucial to plant growth in the greenhouse. We investigated the patterns of constructing and managing greenhouses in Chongqing, and developed an algorithm of heating temperature for closed winte...
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Internal temperature is crucial to plant growth in the greenhouse. We investigated the patterns of constructing and managing greenhouses in Chongqing, and developed an algorithm of heating temperature for closed winter plastic greenhouses under the conditions of no man-made illumination, no ventilation and hot wind machine as the heating equipment, which are the most adopted pattern of greenhouses in Chongqing area. The algorithm includes two functions of temperature outside the greenhouse, which calculate the values of the warming estimation coefficient (WEC) and the gap between temperatures inside and outside the greenhouse with the measured data of outside temperature, and then give the value of internal temperature; the heat rating of heating facilities required by a greenhouse can be determined by this algorithm with given values of floor area and internal temperature, measured outside temperature and calculated WEC. Verification of the algorithm demonstrates a desirable accuracy of estimation. algorithms of computing heating temperature for greenhouses of different constructing and managing patterns and in different geographic conditions can also be derived in a similar way. This research presents a paradigm for developing a feasible method to fit out greenhouses with appropriate heating facilities, aiming at energy efficient and cost efficient production.
The inversion of physics based models presents an alternative to empirical relationships for the retrieval of forest structure from Synthetic Aperture Radar (SAR) data. A major disad-vantage of such techniques is inst...
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ISBN:
(纸本)9781467311601
The inversion of physics based models presents an alternative to empirical relationships for the retrieval of forest structure from Synthetic Aperture Radar (SAR) data. A major disad-vantage of such techniques is instability in the presence of moderate levels of noise. The effects of noise on the accuracy with which parameters can be retrieved is evaluated in this study under a number of conditions.
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