Accuracy and Stability of Numerical Algorithms
Accuracy and Stability of Numerical Algorithms
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Ultra-Wideband Wireless Communications
Ultra-Wideband Wireless Communications
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Signal Processing - Sparse approximations in signal and image processing
Algorithms for simultaneous sparse approximation: part II: Convex relaxation
Signal Processing - Sparse approximations in signal and image processing
Distributed sparse random projections for refinable approximation
Proceedings of the 6th international conference on Information processing in sensor networks
Near-optimal Bayesian localization via incoherence and sparsity
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
IEEE Transactions on Signal Processing
Modified-CS: modifying compressive sensing for problems with partially known support
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
RWS'10 Proceedings of the 2010 IEEE conference on Radio and wireless symposium
Compressed sensing performance bounds under Poisson noise
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Sparse solutions to linear inverse problems with multiple measurement vectors
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
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A key challenge to achieve very high positioning accuracy (such as sub-mm accuracy) in Ultra-Wideband (UWB) positioning systems is how to obtain ultra-high resolution UWB echo pulses, which requires ADCs with a prohibitively high sampling rate. The theory of Compressed Sensing (CS) has been applied to UWB systems to acquire UWB pulses below the Nyquist sampling rate. This paper proposes a front-end optimized scheme for the CS-based UWB positioning system. A Space-Time Bayesian Compressed Sensing (STBCS) algorithm is developed for joint signal reconstruction by transferring mutual a priori information, which can dramatically decrease ADC sampling rate and improve noise tolerance. Moreover, the STBCS and time difference of arrival (TDOA) algorithms are integrated in a pipelined mode for fast tracking of the target through an incremental optimization method. Simulation results show the proposed STBCS algorithm can significantly reduce the number of measurements and has better noise tolerance than the traditional BCS, OMP, and multi-task BCS (MBCS) algorithms. The sub-mm accurate CS-based UWB positioning system using the proposed STBCS-TDOA algorithm requires only 15% of the original sampling rate compared with the UWB positioning system using a sequential sampling method.