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Estimation of the number of sources embedded in noise is a fundamental problem in statistical signal and array processing. This paper focuses on a non-parametric tool to estimate the number of sources without any information about the signature matrix. We exploit the behavior of eigenvalues of the sample covariance matrix and propose a new estimator based on a sequence of hypothesis test. A series...
The problem of blind despreading of the long-code direct sequence spread signal is addressed in this paper. A novel algorithm applicable in blind despreading algorithm of the long-code direct sequence spread spectrum signal is presented in this paper. The algorithm exploits the structure of the signal and a monotone missing-data model subject to low rank is constructed. Based on the maximum likelihood...
Eigenvalue computation is essential in many fields of science and engineering. For high performance and real-time applications, it needs to be carried out in hardware. This paper focuses on the exploration of an efficient hardware architecture which computes eigenvalues of symmetric matrices based on Jacobi method. The proposed architecture is more efficient than previous architectures reported in...
This paper proposes a computationally efficient spreading-waveform-estimation method for the non-symbol-periodic Direct Sequence Spread Spectrum (DS-SS) signals. The method is a low SNR unconditional maximum likelihood (UML) estimation algorithm. It works under the assumption of uniformly distributed transmission delay, which may cause mismatch in the real-world model. Equivalence exists between the...
A new method for the detection of number of signals in array signal processing is proposed. Unlike the traditional eigenvalue-based techniques, this method is based on the eigenvector and exploits the eigenvectors of the estimated correlation matrix. A detector is formulated and simulation results are presented, showing performance at various SNRs and the number of snapshots. Computer simulations...
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