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Free eBook Number DOA of Coherent Signals Determination by Eigenvector Matrix Operation: A New Method for Estimating the Direction-of-Arrival and Determining the Numbers of Coherent Impinging Signals download

by Yuh-Huu Chang Nelson Wu

Free eBook Number  DOA of Coherent Signals Determination by Eigenvector Matrix Operation: A New Method for Estimating the Direction-of-Arrival and Determining the Numbers of Coherent Impinging Signals download ISBN: 3639000323
Author: Yuh-Huu Chang Nelson Wu
Publisher: VDM Verlag (May 6, 2008)
Language: English
Pages: 68
Category: Engineering & Transportation
Subcategory: Engineering
Size MP3: 1830 mb
Size FLAC: 1531 mb
Rating: 4.6
Format: doc mobi rtf azw


The 2D-DOA of uncorrelated signals can be estimated by DOA-matrix .

The 2D-DOA of uncorrelated signals can be estimated by DOA-matrix method. The parameter pairing between azimuth and elevation is accomplished. DOA) estimation is an important problem in array signal processing. An effective spatial differencing method is addressed, for the DOA estimation problem of a number of uncorrelated and coherent narrowband signals simultaneously impinging on the far field of a uniform linear array (ULA). By using a new method of modified estimation of signal parameters via rotation invariance techniques (ESPRIT), the DOAs of uncorrelated signals are first estimated.

First, the reasons for the failure of decorrelation are analyzed from the lost of shift-invariant characteristics in the virtual arrays. Then, the principle of decorrelation and DOA estimation of coherent sources is deduced by using the focusing technology.

Modeling the Received Array Signals. Estimating the Direction of Arrival (DOA). The row weighting parameter determines the maximum weight. In most cases, it is chosen to be as large as possible. Estimating the Number of Signal Sources. Reducing Computational Complexity. Optimizing Performance. Estimating Coherent Sources in Multipath Environments. You can estimate the number of signal sources by specifying 'Auto' for the NumSignalsSource property and choosing either 'AIC' or 'MDL' for the NumSignalsMethod property. For AIC, the Akaike information criterion (AIC) is used, and for MDL, the minimum description length (MDL) criterion is used.

For the problem of signal Direction of Arrival (DOA) estimation of the weak signals in the presence of coherent strong signals, a new method is proposed in this paper. First, a toeplitz de-correlation method using the maximum eigenvector is adopted to remove the coherence between strong and weak signal. Second, an advanced subspace projection method is to estimate the DOAs of weak signals. Compared with the existing algorithms such as JJM and RELAX, this method does not need the knowledge of the DOAs of strong signals and iterative steps

Number and Doa of Coherent Signals Determination by Eigenvector Matrix Operation - a New Method for Estimating the and Determining. by Yuh-Huu Chang and Nelson Wu.

Number and Doa of Coherent Signals Determination by Eigenvector Matrix Operation - a New Method for Estimating the and Determining. In array processing, the estimation of DOA( Direction of Arrival ) or the determination of number of signals plays an important role in many applications such as in sonar, radar, and seismic measurement. However, the concept of most known algorithms is assuming the signals are uncorrelated.

Thus, a scalar multiplication of an eigenvector is again an eigenvector of the same eigenvalue. Part (b) is a special case of this fact.

The second approach applies multiple linear constraints determined from the DOAs of the coherent signals to develop a minimum variance beamformer, which can achieve efficient signal utilization. To cope with performance degradation due to sample covariance errors, DOA estimation errors and other array imperfections, the eigenstructure of the covariance matrix is exploited to improve the performance of the proposed approaches by constraining the weight vector in the signal subspace.

A direction of arrival (DOA) estimation algorithm for coherent signals in the presence of unknown mutual coupling is proposed. A group of auxiliary sensors in a uniform linear array are applied to eliminate the effects on the orthogonality of subspaces brought by mutual coupling. Then, a Toeplitz matrix, whose rank is independent of the coherency between impinging signals, is reconstructed to eliminate the rank loss of the spatial covariance matrix. Therefore, the signal and noise subspaces can be estimated properly

The set of all eigenvectors of a linear transformation, each paired with its corresponding eigenvalue, is called .

The set of all eigenvectors of a linear transformation, each paired with its corresponding eigenvalue, is called the eigensystem of that transformation. If the entries of the matrix A are all real numbers, then the coefficients of the characteristic polynomial will also be real numbers, but the eigenvalues may still have nonzero imaginary parts. The entries of the corresponding eigenvectors therefore may also have nonzero imaginary parts. Similarly, the eigenvalues may be irrational numbers even if all the entries of A are rational numbers or even if they are all integers.

Two approaches on signal (DOA) and . coherent or temporally correlated signals, for which CSDE works very well.

Two approaches on signal (DOA) and amplitude estimation are developed, the correlated signal direction estimate (CSDE) for three-row systems and the ESPRIT-based method. Observing that the estimated spatial covariance function has only a finite number of lags due to the finite number of sensors, Burg's method does the covariance extension according to Burg's Entropy criterion, which assumes a maximal random data model. For the deterministic signals or signal with deterministic components, MEM's performance becomes worse.

The research of signal parameter estimation using antenna array have been extensively attended for decades. In array processing, the estimation of DOA( Direction of Arrival ) or the determination of number of signals plays an important role in many applications such as in sonar, radar, and seismic measurement.However, the concept of most known algorithms is assuming the signals are uncorrelated. In practice, the transmitting signals are usually reflected or refracted by mountains, buildings or other impediments. It will cause multi-path propagation as highly correlated or coherent signals are received on the sensor.Therefore, rank deficiency of signal covariance matrix will be raised.It will cause the usual eigenvalue-detecting method going failed.In this book, we propose a new method for the rank deficient issue. For avoiding the confusion caused by the unknown length of subarray, we use some operations of the eigenvector matrix to form a new matrix. Besides, we can find out the number of signals by counting the non-zero eigenvalues of the covariance matrix of this new matrix. Furthermore, we also can categorize the coherent signals into different groups for their coherence.