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A novel wavelet packet division multiplexing based on maximum likelihood algorithm and optimum pilot symbol assisted modulation for Rayleigh fading channels

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2005年第3期24卷 287-302页

作者： Zhou, L Li, JD Liu, J Zhang, GH Xidian Univ State Key Lab Integrated Serv Networks Broadband Wireless Commun Lab Inst Sci Informat Xian 710071 Peoples R China

Wavelet packet division multiplexing (WPDM) is a high-capacity, flexible, and robust multiple-signal transmission technique. In this paper, a novel WPDM system based on optimum pilot symbol assisted modulation (OPSAM) and a maximum likelihood (NIL) algorithm is studied for Rayleigh fading channels. The NIL detecting algorithm and the new discrete wavelet packet transform structure, which is based on pilot symbol assisted modulation (PSAM) using a least mean squares algorithm, are two novel aspects of the presented system. An expression for the bit error rate of the WPDM scheme on quadrature phase-shift keying (QPSK) is derived in the presence of flat fading and Gaussian noise. It is demonstrated by simulation results that the OPSAM WPDM scheme can provide greater immunity to flat fading channels and Gaussian noise than the OPSAM orthogonal frequency division multiplexing scheme, the differential QPSK WPDM scheme, and the normal PSAM WPDM scheme.

关键词： wavelet packet division multiplexing pilot symbol assisted modulation maximum likelihood algorithm

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An indoor position estimation method by maximum likelihood algorithm using RSS

An indoor position estimation method by maximum likelihood a...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Yamada, Isao Ohtsuki, Tomoaki Hisanaga, Tetsuo Zheng, Li Yamtake Corp Kanagawa Japan Keio Univ Dept Informat & Comp Sci Kanagawa Japan

ISBN: (纸本)9784907764289

A lot of applications of sensor networks require the position of human and objects in indoor environment. There are some methods for this purpose. In this paper, we use received signal strength (RSS) to estimate the position. However, RSS varies substantially owing to fading, shadowing and multipath effects. Attenuation constant varies according to indoor environment. For this reason, the attenuation constant is determined before estimating the position of target node in the conventional method. We propose a position estimation method that estimates a target node position and attenuation constant simultaneously. Thus, the proposed method has no need to estimate the attenuation constant in the target environment preliminarily. To evaluate the position estimation accuracy of the proposed method, we developed the prototype system and experimented in our office. Multiple fixed target nodes were set in the target environment. The experiment presents that the proposed method has about the same accuracy as the conventional one without preliminary data collection for attenuation constant estimation.

关键词： IEEE802.15.4 position estimation maximum likelihood algorithm attenuation constant

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Direction Finding of maximum likelihood algorithm Using Quantum Bee Colony for Noncircular Signals

Direction Finding of Maximum Likelihood Algorithm Using Quan...

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IEEE 10th International Conference on Signal Processing

作者： Gao, Hongyuan Yu, Xuemei Cao, Jinlong Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

ISBN: (纸本)9781424458981

The noncircular maximum likelihood (NC-ML) algorithm proposed in this paper is an extension of the maximum likelihood (ML) algorithm to the case of noncircular signals which are used in communication systems. Due to the utilization of noncircular information of signals, the root mean square error (RMSE) performance of NC-ML is better than ML algorithm for noncircular signals. The proposed NC-ML algorithm utilized few virtual elements and expanded the number of effective aperture array, while significantly improving the performance of the original ML algorithms. In order to fit the proposed NC-ML algorithm, a quantum bee colony (QBC) algorithm is proposed and applied to objective function of NC-ML. The quantum bee colony is a novel intelligence algorithm for optimization problem, which can solve NC-ML with fast velocity. Monte-Carlo simulations have proved that the proposed NC-ML method based omit the QBC has good performance for non-coherent and coherent signals.

关键词： direction finding quantum computation maximum likelihood algorithm artificial bee colony noncircular signals

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A new simplified temporal-spatial maximum likelihood algorithm for DOA estimation

A new simplified temporal-spatial maximum likelihood algorit...

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6th International Conference on Signal Processing

作者： Zhao, YB Zhang, SH Xidian Univ Key Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)0780374886

For the purpose of improving the DOA estimation performance, we propose a new simplified temporal-spatial maximum likelihood algorithm. Instead of using the discrete Fourier transform(DFT) filters, the proposed algorithm uses the special Doppler filters to process the received data according to signal's Doppler frequency. The processed data are then used by the maximum likelihood algorithm to estimate DOA. Because the special Doppler filters can greatly reduce the influences between signals, the proposed algorithm works significantly better than the simplified temporal-spatial maximum likelihood algorithm. Computer simulation results are presented and demonstrate the merits of the proposed algorithm.

关键词： DOA estimation maximum likelihood algorithm Doppler processing

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An Indoor Position Estimation Method by maximum likelihood algorithm Using Received Signal Strength

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SICE Journal of Control, Measurement, and System Integration 2008年第3期1卷 251-256页

作者： Isao Yamada Tomoaki Ohtsuki Tetsuo Hisanaga Li Zheng Yamatake Corporation Department of Information and Computer Science Keio University

A lot of applications of sensor networks require the positions of human and objects in indoor environments. There are some methods for this purpose. In this paper, we use received signal strength (RSS) to estimate the position for its simplicity. However, RSS varies substantially owing to fading, shadowing, multipath effects and so on. Attenuation coefficient varies according to indoor environment. For this reason, the attenuation coefficient is determined before estimating the position of a target node in the conventional method. We propose a position estimation method that estimates a target node position and an attenuation coefficient simultaneously. Thus, the proposed method has no need to estimate the attenuation coefficient in the target environment preliminarily. To evaluate the position estimation accuracy of the proposed method, we developed the prototype system and experimented in our office. Multiple fixed target nodes were set in the target environments. The experiments show that the proposed method has about the same accuracy as the conventional one without preliminary data collection for attenuation coefficient estimation.

关键词： IEEE802.15.4 position estimation maximum likelihood algorithm attenuation coefficient

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Classification of PolSAR data with the Complex Riesz distribution

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2025年

作者： Kammoun, Rayhan Kessentini, Sameh Zine, Raoudha Univ Sfax Fac Sci Sfax Lab Probabil & Stat BP 1171 Sfax 3000 Tunisia

This article deals with unsupervised classification strategies applied to polarimetric synthetic aperture radar (PolSAR) images. We discuss the performance of the Complex Riesz distribution, which is used for the first time to classify PolSAR images. Hence, we extend the maximum likelihood (ML) and expectation-maximization (EM) algorithms to the Complex Riesz distribution. Furthermore, we derive the analytic expression of five stochastic distances (Kullback-Leibler, Bhattacharyya, R & eacute;nyi, Hellinger, and Chi-square) between Complex Riesz distributions. We assess the accuracy of the Complex Riesz EM algorithm on synthetic data generated by an extension of the Bartlett decomposition. Then, comparing the Complex Wishart and the Complex Riesz distributions on PolSAR data reveals that the latter performs better than the former. Finally, the EM algorithm for the Complex Riesz distribution serves to classify the actual data, and the discrimination potential of the five stochastic distances is discussed. These results in both experimental, ML, and EM algorithms suggest that most stochastic distances are significant, and mainly the 0. 7-order R & eacute;nyi.

关键词： Complex Riesz distribution expectation-maximization algorithm Hermitian random matrix maximum likelihood algorithm PolSAR classification stochastic distances

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Spatio-temporal dynamics of land use and land cover types within the Belabo-Diang communal forest in East Cameroon

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2025年第4期11卷 1-18页

作者： Ngoukwa, Guylene Zekeng, Jules Christian Tchonang, Bienvenu Leonnel Djoumbi Chimi, Cedric Djomo Kabelong, Louis-Paul-Roger Banoho Ntonmen, Amandine Yonkeu Zapfack, Louis Univ Yaounde I Fac Sci Dept Plant Biol BP 812 Yaounde Cameroon Conservat & Sustainable Nat Resources Management N POB 8554 Yaounde Cameroon Univ Douala Adv Teachers Training Sch Tech Educ Dept Forest Engn POB 1872 Douala Cameroon Copperbelt Univ Oliver R Tambo Africa Res Chair Initiat ORTARChl D Sch Nat Resources POB 21692 Kitwe Zambia Inst Rech Agr Dev IRAD POB 136 Yokadouma Cameroon

Understanding spatiotemporal land-use and vegetation dynamics is vital for sustainable management;however, translating this knowledge into effective planning remains challenging. This study addresses the gap by assessing Land Use and Land Cover (LULC) dynamics in the Belabo-Diang Communal Forest (BDCF) in eastern Cameroon, employing remote sensing and Geographic Information System (GIS) techniques. Landsat images from December 1984, December 2002, and November 2020 were utilised for supervised classification using the maximum likelihood algorithm, resulting in thematic maps of land use/land cover (LULC). Post-classification analysis, conducted at the pixel scale and LULC process insights, assessed forest loss and change trajectories in the BDCF. Results show the BDCF covers 53,213.80 hectares, with seven LULC types: Mature Secondary Forest (MSF), Young Secondary Forest (YSF), Savannah (SA), Cropland and Fallow (CF), Bare Soil (BS), permanently flooded swamp (PFS), and periodically flooded swamp (TFS). MSF declined 37.78% (1984-2002) but increased 36.10% (2002-2020). The annual change per hectare shows that MSF decreased by 319.42 ha/year, while SA, CF, and BS increased by 401.12, 351.7, and 1281.4 ha/year, respectively, resulting in a loss of 19,625.93 ha in forest formations. These findings support BDCF managers in landscape restoration and the Reducing Emissions from Deforestation and Forest Degradation (REDD +) initiative, providing insights for sustainable land management, effective conservation strategies, and mitigating climate impact.

关键词： Change trajectories Communal forest Eastern Cameroon Land use and land cover maximum likelihood algorithm Spatio-temporal dynamics

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Hierarchical Identification of Hammerstein-Wiener time-delay systems with maximum likelihood and gradient descent algorithm 41

Hierarchical Identification of Hammerstein-Wiener time-delay...

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第41届中国控制会议

作者： Yizhe Jiang Weicheng Song Jie Chu Junhong Li School of Electrical Engineering Nantong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the parameter estimation for Hammerstein-Wiener nonlinear systems with unknown delay is *** on the hierarchical identification principle and two-step identification,the maximum likelihood recursive algorithm is used to identify the parameters of the system,and the gradient descent method is used to identify the time ***,the algorithm is verified by a numerical example,and the simulation results show that the algorithm has the characteristics of fast convergence speed and high identification accuracy.

关键词： system identification Hammerstein-Wiener systems maximum likelihood algorithm gradient descent algorithm hierarchical identification

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maximum-likelihood direction-of-arrival estimation in the presence of unknown nonuniform noise

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2001年第7期49卷 1310-1324页

作者： Pesavento, M Gershman, AB McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

We consider the problem of estimating directions of arrival (DOAs) of multiple sources observed on the background of nonuniform white noise with an arbitrary diagonal covariance matrix. A new deterministic maximum likelihood (ML) DOA estimator is derived. Its implementation is based on an iterative procedure which includes a stepwise concentration of the log-likelihood (LL) function with respect to the signal and noise nuisance parameters and requires only a few iterations to converge. New closed-form expressions for the deterministic and stochastic direction estimation Cramer-Rao bounds (CRBs) are derived for the considered nonuniform model. Our expressions can be viewed as an extension of the well-known results by Stoica and Nehorai and Weiss and Friedlander to a more general noise model than the commonly used uniform one. In addition, these expressions extend the recent results by Matveyev et al. to the multiple source case. Comparisons with the above-mentioned earlier results help to discover several interesting properties of DOA estimation in the nonuniform noise case, To compare the estimation performance of the proposed ML, technique with the results of our CRB analysis and with the performance of conventional "uniform" ME, simulation results are presented. Additionally, we test our technique using experimental seismic array data. Our simulations and experimental results both validate essential performance improvements achieved by means of the approach proposed.

关键词： direction-of-arrival estimation maximum likelihood algorithm nonuniform noise sensor array processing

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A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining

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BMC BIOINFORMATICS 2008年第s6期9卷 S4-S4页

作者： Guo, Mao-Zu Li, Jian-Fu Liu, Yang Harbin Inst Technol Dept Comp Sci & Technol Harbin 150001 Peoples R China

Background: Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (pECR). However, due to its high computation complexity, p-ECR has rarely been used in practice. Results: To make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining (NJ) to refine the unresolved nodes produced in p-ECR. Conclusion: Experiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time.

关键词： Evolutionary Tree Neighbor Join maximum likelihood algorithm Local Topological Topological Transformation

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