| 1. |
- Chen, Ming, 1972, et al.
(författare)
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Adaptive Channel Prediction Based on Polynomial Phase Signals
- 2008
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Ingår i: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP; Las Vegas, NV; United States; 31 March 2008 through 4 April 2008. - 1520-6149. - 9781424414840 ; , s. 2881-2884
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Konferensbidrag (refereegranskat)abstract
- Motivated by recently published physics based scattering SISO and MIMO channel models, a new adaptivechannel prediction using Kalman filter based on non-stationary polynomial phase signals with time-varying amplitudes is proposed. To mitigate the influence of the time-varying amplitudes on parameter estimation, an iterative estimation using the Non-linear instantaneous LS criterion is proposed, where the number of signal components and model orders are known. The new predictoroutperforms the classical Linear Prediction and stationarysinusoidal modeling based prediction in Monte Carlo simulations.
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| 2. |
- 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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| 3. |
- 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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| 4. |
- 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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| 5. |
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| 6. |
- 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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| 7. |
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| 8. |
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| 9. |
- Feng, S, et al.
(författare)
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3D Face Recognition Using Affine Integral Invariants
- 2006
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Ingår i: IEEE international conf. on ASSP (ICASSP-06). ; II, s. 189-192
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Konferensbidrag (refereegranskat)abstract
- A new 3D face representation and recognition approach is presented in this paper. Two sets of facial curves are extracted from a face range image, and a novel facial feature representation, the affine integral invariant, is introduced to mitigate the effect of pose on the facial curves. A human face is shown to be representable by a smallsubset of those affine integral invariant curves. A recognition procedure based on the Discriminant Analysis and Jensen-Shannon Divergence analysis is proposed. Substantiating examples are provided with an achieved classification accuracy of 92.57%.
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| 10. |
- Ferreol, A., et al.
(författare)
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Modeling error sensitivity of the MUSIC algorithm conditioned on resolved sources
- 2008
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Ingår i: European Signal Processing Conference. - 2219-5491.
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Konferensbidrag (refereegranskat)abstract
- When the correlation matrix is known, the resolution power of subspace algorithms is infinite. In the presence of modelling errors, even if the correlation matrix is known, sources can no longer be resolved with certainty. Focusing on the MUSIC algorithm [1], the purpose of this work is to provide closed form expression of bias and variance versus the model mismatch (these errors can be different for each source). Un-like previous work, these performance measures are derived conditioned on the success of a certain source resolution test. Among the resolution definitions proposed in [2], we investigate which one is more suitable for our purposes. Numerical results support the theoretical investigations. Our findings are of a great interest for the determination of the necessary antenna calibration accuracy to achieve specifications on the estimator performance.
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