| 11. |
- Chen, Ming, 1972, et al.
(författare)
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Long Range Channel Prediction Based on Non-Stationary Parametric Modeling
- 2009
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Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 57:2, s. 622-634
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Tidskriftsartikel (refereegranskat)abstract
- Motivated by the analysis of measured radio channels and recently published physics-based scattering SISO and MIMO channel models, a new approach of long-range channel prediction based on nonstationary multicomponent polynomial phase signals (MC-PPS) is proposed. An iterative and recursive method for detecting the number of signals and the orders of the polynomial phases is proposed. The performance of these detectors and estimators is evaluated by Monte Carlo simulations. The performance of the new channel predictors is evaluated using both synthetic signals and examples of real world channels measured in urban and suburban areas. High-order polynomial phase parameters are detected in most of the measured data sets, and the new methods outperform the classical LP in given examples for long-range prediction for the cases where the estimated model parameters are stable. For the more difficult data sets, the performance of these methods are similar, which provides alternatives for system design when other issues are concerned.
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| 12. |
- Chen, Ming, 1972, et al.
(författare)
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Models and Predictions of Scattered Radio Waves on Rough Surfaces
- 2007
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Ingår i: IEEE ICASSP 2007 (Honolulu, Hawaii, USA, 2007). ; 3, s. 785-788
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Konferensbidrag (refereegranskat)abstract
- Scattering of radio waves on rough surfaces is investigated using ray tracing techniques, which results in a sinusoidal model with time varying amplitudes. An AR(d) model with nonzero mean is proposed to characterize and predict the time variation of the amplitudes. A covariance sequence based method is proposed to estimate the autoregressive coefficients from the channel observations. An adaptive channel predictor using a Kalman filter is proposed to predict the complex amplitudes of the scattering signal. The proposed method outperforms other sinusoidal modeling based channel predictors and Linear Predictors with single scattering scenarios.
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| 13. |
- Chen, Ming, 1972, et al.
(författare)
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New Approaches for Channel Prediction Based on Sinusoidal Modeling
- 2007
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Ingår i: Eurasip Journal on Applied Signal Processing. - : Springer Science and Business Media LLC. - 1110-8657 .- 1687-0433. ; 2007
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Tidskriftsartikel (refereegranskat)abstract
- Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP) in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS) prediction model and the associated joint least-squares (LS) predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.
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| 14. |
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| 15. |
- Chung, P. J., et al.
(författare)
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Broadband ML estimation under model order uncertainty
- 2010
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684. ; 90:5, s. 1350-1356
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Tidskriftsartikel (refereegranskat)abstract
- The number of signals hidden in data plays a crucial role in array processing. When this information is not available, conventional approaches apply information theoretic criteria or multiple hypothesis tests to simultaneously estimate model order and parameter. These methods are usually computationally intensive, since ML estimates are required for a hierarchy of nested models. In this contribution, we propose a computationally efficient solution to avoid this full search procedure and address issues unique to the broadband case. Our max-search approach computes ML estimates only for the maximally hypothesized number of signals, and selects relevant components through hypothesis testing. Furthermore, we introduce a criterion based on the rank of the steering matrix to reduce indistinguishable components caused by overparameterization. Numerical experiments show that despite model order uncertainty, the proposed method achieves comparable estimation and detection accuracy as standard methods, but at much lower computational expense. (C) 2009 Elsevier B.V. All rights reserved.
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| 16. |
- Chung, Pei-Jung, et al.
(författare)
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Broadband ML estimation under model order uncertainty
- 2009
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Ingår i: Proceedings of ICASSP 2009. ; , s. 2121 - 2124
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Konferensbidrag (refereegranskat)abstract
- The number of signals plays a crucial role in array processing. The performance of most direction finding algorithms relies strongly on a correctly specified number of signals. When this information is not available, conventional approaches apply information theoretic criteria or multiple hypothesis tests to simultaneously estimate model order and parameter. These methods are usually computationally intensive, since ML estimates are required for a hierarchy of nested models. In the previous work, we proposed a computationally efficient solution to avoid this full search procedure and demonstrated its feasibility by extensive simulations. Here we extend to broadband data, and address issues unique to the broadband case. Our max-search approach computes ML estimates only for the maximally hypothesized number of signals, and selects relevant components through hypothesis testing. Another novelty of this work is the reduction of indistinguishable components caused by overparameterization. Our approach is based on the rank of the estimated steering matrix. Numerical experiments show that despite an unknown number of signals, the proposed method achieves comparable estimation and detection accuracy as standard methods, but at much lower computational expense.
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| 17. |
- Chung, Pei-Jung, et al.
(författare)
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DOA Estimation Methods and Algorithms
- 2014
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Ingår i: Academic Press Library in Signal Processing: Volume 3 Array and Statistical Signal Processing. - 9780124115972 ; , s. 599-650
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Estimation of direction of arrival (DOA) from data collected by sensor arrays is of fundamental importance to a variety of applications such as radar, sonar, wireless communications, geophysics and biomedical engineering. Significant progress in the development of algorithms has been made over the last three decades. This article provides an overview of DOA estimation methods that are relevant in theory and practice. We will present estimators based on beamforming, subspace and parametric approaches and compare their performance in terms of estimation accuracy, resolution capability and computational complexity. Methods for processing broadband data and signal detection will be discussed as well. Finally, a brief discussion will be given to application specific algorithms.
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| 18. |
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