Abstract-In this letter, we addressed the problem of estimating the time delay and the frequencies of noisy sinusoidal signals received at two spatially separated sensors. We employ the Propagator Method (PM) in conjunction with the well-known MUSIC/ root-MUSIC algorithm; the proposed method would generate estimates of the unknown parameters. Such estimates are based on the observation and/or covariance matrices. Moreover, the PM does not require the eigenvalue decomposition (EVD) or singular value decomposition (SVD) of the cross-spectral matrix (CSM) of received signals; therefore, a significant improvement in computational load is achieved. Computer simulations are also included to demonstrate the effectiveness of the proposed method.Index Terms-Delay and frequency estimation, MUSIC, propagator method, root-MUSIC.
In this paper, we proposed a closed form solution for blind Orthogonal Frequency Division Multiplexing (OFDM) Carrier Frequency Offset (CFO) estimation employing the RankRevealing QR triangular factorization Method (RRQR). The advantage of using the RRQR it gives precious information about numerical rank and efficiently separates the signal space from the noise space. Furthermore, the RRQR does not involve the eigenvalue decomposition (EVD) or singular value decomposition (SVD) of co-variance matrix of the received signals and is a valuable tool in linear algebra to meet compatibility requirement of real time environment. Computer simulations shows the superior performance and much less processing time of RRQR compared with the method employing ESPRIT Algorithm
The Rank-Revealing QR factorization (RRQR) is a valuable tool in numerical linear algebra because it provides accurate information about rank and numerical null-space. In this paper, we addressed the problem of estimating the time delay and the frequencies of noisy sinusoidal signals received at two spatially separated sensors using the well known RRQR, subspace decomposition technique. Although eigenvalue decomposition (EVD) of cross spectral matrix or Singular value decomposition SVD for the data matrix based techniques provide accurate estimation, they are hard to meet real time constraints due to computational load and cost. To explore compatibility with real time applications, we proposed a RRQR method in association with the well-known MUSIC/root-MUSIC algorithm to estimate unknown parameters without using any EVD or SVD. The simulation results verify that the proposed method provide better performance than the well known EVD or SVD based methods with less computational complexity.
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