作者：
Sun, QingLim, Cheng-ChewShi, PengLiu, FeiShanghai Univ
Sch Mech Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Jiangnan Univ
Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ Adelaide
Sch Elect & Elect Engn Adelaide SA 5005 Australia
This paper presents a moving horizon algorithm with mode detection for state estimation in Markov jump systems with Gaussian noise. This state estimation scheme is a combination of the maximum-likelihood algorithm and...
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This paper presents a moving horizon algorithm with mode detection for state estimation in Markov jump systems with Gaussian noise. This state estimation scheme is a combination of the maximum-likelihood algorithm and the moving horizon approach. The maximum-likelihood algorithm provides optimal estimate of the mode sequence within a moving fixed-size horizon, and the moving horizon estimation is an optimization-based solution. As a result, a mode detection-moving horizon estimator design method is proposed. Through the stochastic observability properties of the Markov jump linear systems, sufficient conditions for stability are established.
In the paper, the maximum-likelihood (ML) algorithm is employed to resolve the difficulty of direction finding for bistatic multiple-input multiple-output (MIMO) radar. In order to obtain the global optimal solution o...
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ISBN:
(纸本)9781509041657
In the paper, the maximum-likelihood (ML) algorithm is employed to resolve the difficulty of direction finding for bistatic multiple-input multiple-output (MIMO) radar. In order to obtain the global optimal solution of ML algorithm, the cat swarm optimization (CSO) is used to solve the ML equation, which has the advantages of low computational complexity and fast convergence speed. Thus, a novel direction finding approach called CSO-ML is proposed, and it is effective to find coherent and independent signal sources for bistatic MIMO radar. Monte-Carlo simulations show that the proposed CSO-ML can obtain higher accuracy and success rate of estimation compared with previous classical direction finding approaches of bistatic MIMO radar.
In this contribution, a maximum-likelihood-based approach is proposed for estimating the attitudes of sectoring base station (BS) antennas based on the power of the received signals observed by multiple user equipment...
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ISBN:
(纸本)9788890701849
In this contribution, a maximum-likelihood-based approach is proposed for estimating the attitudes of sectoring base station (BS) antennas based on the power of the received signals observed by multiple user equipments (UEs) in a cellular network. The proposed method calculates the likelihood of the antenna attitude by taking into account both the large-scale fading statistics, i.e. path loss, shadowing, and the small-scale fading attributed to multi-path propagation. The performance of the method is evaluated by simulations in both urban and rural scenarios, where a random-propagation-graph method is applied to generating realistic channel impulse responses by using directional antenna patterns. Results show that by using the proposed method, accurate estimates of the antenna attitude can be obtained when the observations from more UEs are available and the line-of-sight (LoS) condition dominates the channels between the BS and the UEs.
maximum-likelihood (ML) and its relaxation algorithm are discussed which are used to identify the mathematics model of an Underwater Vehicle (UV). With trial data of zigzag tests, the hydrodynamic derivatives of the U...
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ISBN:
(纸本)9783037859223
maximum-likelihood (ML) and its relaxation algorithm are discussed which are used to identify the mathematics model of an Underwater Vehicle (UV). With trial data of zigzag tests, the hydrodynamic derivatives of the UV were estimated, and the better astringency of the relaxation algorithm can be acquired from the contrast between the two methods. A simulation environment based on these parameters is established to verify the validity and effect of these methods. The result shows the model is credible and the methods are very useful for the research of maneuverability and intelligent control of underwater vehicles.
maximum-likelihood(ML)and its relaxation algorithm are discussed which are used to identify the mathematics model of an Underwater Vehicle(UV).With trial data of zigzag tests,the hydrodynamic derivatives of the UV wer...
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maximum-likelihood(ML)and its relaxation algorithm are discussed which are used to identify the mathematics model of an Underwater Vehicle(UV).With trial data of zigzag tests,the hydrodynamic derivatives of the UV were estimated,and the better astringency of the relaxation algorithm can be acquired from the contrast between the two methods.A simulation environment based on these parameters is established to verify the validity and effect of these *** result shows the model is credible and the methods are very useful for the research of maneuverability and intelligent control of underwater vehicles.
In distributed satellites interferometric SAR(InSAR) system,short baselines usually make higher height ambiguity but the interferometric phase can be unwrapped much more easily,while the long baselines usually cause m...
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In distributed satellites interferometric SAR(InSAR) system,short baselines usually make higher height ambiguity but the interferometric phase can be unwrapped much more easily,while the long baselines usually cause more complicated phase unwrapping problems but have less height ***'s of obvious significance if we can combine the data from baselines of different *** this paper,a multi-baseline InSAR data fusion method based on iterative and maximum-likelihood methods is proposed to combine the information from different baselines and obtain more accurate digital elevation model(DEM)compared with single *** simulation shows that the proposed algorithm is more effective and accurate than iterative or maximum-likelihood method in multi-baseline InSAR system,and it is especially appropriate for the rugged terrain height retrieving or processing highly ambiguous data,for which commonly used phase unwrapping algorithms may fail.
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