Guest Home Page

Home | Login | Logout | Access Information | Alerts | Purchase History |

Cart | Sitemap | Help

Top Documents Accessed


	BROWSE	SEARCH	IEEE XPLORE GUIDE	SUPPORT

Signal Processing, IEEE Transactions on

Top Documents Accessed: Dec 2009

Display Format:

Citation

Citation & Abstract

Article Information

1.	A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking Arulampalam, M.S.; Maskell, S.; Gordon, N.; Clapp, T. Signal Processing, IEEE Transactions on Volume 50, Issue 2, Date: Feb 2002, Pages: 174-188 Digital Object Identifier 10.1109/78.978374 Summary: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-...

	Abstract \| Full Text: PDF (355 KB)

2.	Bayesian Compressive Sensing Via Belief Propagation Baron, D.; Sarvotham, S.; Baraniuk, R.G. Signal Processing, IEEE Transactions on Volume 58, Issue 1, Date: Jan. 2010, Pages: 269-280 Digital Object Identifier 10.1109/TSP.2009.2027773 Summary: Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the s...

	Abstract \| Full Text: PDF (764 KB)

3.	Algebraic Signal Processing Theory: Sampling for Infinite and Finite 1-D Space Kovacevic, J.; Puschel, M. Signal Processing, IEEE Transactions on Volume 58, Issue 1, Date: Jan. 2010, Pages: 242-257 Digital Object Identifier 10.1109/TSP.2009.2029718 Summary: We derive a signal processing framework, called space signal processing, that parallels time signal processing. As such, it comes in four versions (continuous/discrete, infinite/finite), each with its own notion of convolution and Fourier transform. A...

	Abstract \| Full Text: PDF (1933 KB)

4.	Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio Lunden, J.; Kassam, S.A.; Koivunen, V. Signal Processing, IEEE Transactions on Volume 58, Issue 1, Date: Jan. 2010, Pages: 38-52 Digital Object Identifier 10.1109/TSP.2009.2029790 Summary: Cognitive radios sense the radio spectrum in order to find underutilized spectrum and then exploit it in an agile manner. Spectrum sensing has to be performed reliably in challenging propagation environments characterized by shadowing and fading effec...

	Abstract \| Full Text: PDF (1552 KB)

5.	Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks Zhi Quan; Shuguang Cui; Sayed, A.H.; Poor, H.V. Signal Processing, IEEE Transactions on Volume 57, Issue 3, Date: March 2009, Pages: 1128-1140 Digital Object Identifier 10.1109/TSP.2008.2008540 Summary: Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to legacy (primary) networks. In this paper, a novel...

	Abstract \| Full Text: PDF (884 KB)

6.	High-Resolution ARMA Estimation of Mixed Spectra Khanshan, A.H.; Amindavar, H.; Bakhshi, H. Signal Processing, IEEE Transactions on Volume 58, Issue 1, Date: Jan. 2010, Pages: 97-107 Digital Object Identifier 10.1109/TSP.2009.2028939 Summary: In this paper, we propose a high-resolution autoregressive moving average (ARMA) modeling technique for signals which are a sum of sinusoids embedded in colored noise. The approach is based on a special ARMA model. We show that an approximation to thi...

	Abstract \| Full Text: PDF (447 KB)

7.	A SURE Approach for Digital Signal/Image Deconvolution Problems Pesquet, J.-C.; Benazza-Benyahia, A.; Chaux, C. Signal Processing, IEEE Transactions on Volume 57, Issue 12, Date: Dec. 2009, Pages: 4616-4632 Digital Object Identifier 10.1109/TSP.2009.2026077 Summary: In this paper, we are interested in the classical problem of restoring data degraded by a convolution and the addition of a white Gaussian noise. The originality of the proposed approach is twofold. First, we formulate the restoration problem as a non...

	Abstract \| Full Text: PDF (1911 KB)

8.	Alleviating Sensor Position Error in Source Localization Using Calibration Emitters at Inaccurate Locations Le Yang; Ho, K.C. Signal Processing, IEEE Transactions on Volume 58, Issue 1, Date: Jan. 2010, Pages: 67-83 Digital Object Identifier 10.1109/TSP.2009.2028947 Summary: A previous study shows that the use of a calibration emitter whose position is known exactly can significantly reduce the loss in time differences of arrival (TDOA) based source localization accuracy when the available sensor positions have random err...

	Abstract \| Full Text: PDF (589 KB)

9.	Bayesian Multi-Object Filtering With Amplitude Feature Likelihood for Unknown Object SNR Clark, D.; Ristic, B.; Ba-Ngu Vo; Vo, B.T. Signal Processing, IEEE Transactions on Volume 58, Issue 1, Date: Jan. 2010, Pages: 26-37 Digital Object Identifier 10.1109/TSP.2009.2030640 Summary: In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multiple-target state estimation by obtaining more accurate target and false-alarm likelihoods. T...

	Abstract \| Full Text: PDF (1928 KB)

10.	Modeling and Identification of Nonlinear Systems in the Short-Time Fourier Transform Domain Avargel, Y.; Cohen, I. Signal Processing, IEEE Transactions on Volume 58, Issue 1, Date: Jan. 2010, Pages: 291-304 Digital Object Identifier 10.1109/TSP.2009.2028978 Summary: In this paper, we introduce a novel approach for improved nonlinear system identification in the short-time Fourier transform (STFT) domain. We first derive explicit representations of discrete-time Volterra filters in the STFT domain. Based on these ...

	Abstract \| Full Text: PDF (646 KB)

Help Contact Us Privacy & Security IEEE.org

© Copyright 2010 IEEE – All Rights Reserved