Fast Algorithm for Joint Azimuth and Elevation Angles, and Frequency Estimation via Hierarchical Space-Time Decomposition

Lin, Chun-Yu; Fang, Wen‐Hsien; Wu, Kuo-Hsiung; Lin, Jen-Der

doi:10.1109/icassp.2007.366422

Cited by 11 publications

(7 citation statements)

References 9 publications

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“…With the help of the temporal filtering and beamforming processes, the new constraint becomes L ≤ (M R − 1) · (N − 1) [33], which is much easier to meet. (3) To further reduce the computational overhead, along the same line as above, the twodimensional (2-D) MUSIC employed in the F-MUSIC can be further decomposed into three stages of one-dimensional (1-D) MUSICs, see, e.g., [31]. Also, the MUSIC may be replaced by the computationally more efficient ESPRIT algorithm [34] to get rid of the cumbersome search process.…”

Section: Remarksmentioning

confidence: 99%

“…On the other hand, we should also separate signals of similar frequency offsets/delays but different DOAs before we estimate the frequency offsets/delays. To put together these ideas, we construct an FAF tree structure which succinctly combines the parameter estimation and filtering processes [28][29][30][31]. The FAF contains two frequency offset/delay estimation and one angle selectivity estimation, as shown in Fig.…”

Section: Joint Estimation Of Frequency Offsets/delays and Angle Selecmentioning

confidence: 99%

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Antenna-Array-Assisted Frequency Offset Estimation and Data Detection in an Uplink Multiuser MIMO-OFDM Interference Network

Fang

Chen

2009

Wireless Pers Commun

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Due to the popularity of IEEE 802.11a/g/n wireless local area networks, the high-density deployment of access points and their serious mutual interference have become a pressing concern, and made both frequency acquisition and data detection even more difficult. In addition, improving the network coverage and scalability in the mesh mode of IEEE 802.16 wireless metropolitan area network, space and frequency information can provide abundant scheduling information under the configuration. In light of this, this paper presents an antenna-array-assisted algorithm to solve these two problems in a multiuser multipleinput-multiple-output orthogonal frequency division multiplexing interference network. The algorithm begins with the estimation of three channel parameters: frequency offsets, delays and angle selectivity. To make a good use of the array signal characteristics, these three parameters are estimated in a frequency/delay-angle-frequency/delay (FAF) tree structure, in which two frequency/delay estimations and one angle estimation are employed alternatively. One special feature in the FAF tree structure is that temporal filtering or spatial beamforming is invoked between the parameter estimations to decompose signals so as to enhance the estimation accuracy. Thereafter, based on these parameter estimates, a data detection procedure is developed to mitigate both multiple access interference (MAI) and co-channel interference (CCI). Simulations show that the proposed algorithm can provide satisfactory performance even in networks with MAIs and CCIs sharing the same frequency band.

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Section: Remarksmentioning

confidence: 99%

Section: Joint Estimation Of Frequency Offsets/delays and Angle Selecmentioning

confidence: 99%

Antenna-Array-Assisted Frequency Offset Estimation and Data Detection in an Uplink Multiuser MIMO-OFDM Interference Network

Fang

Chen

2009

Wireless Pers Commun

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“…It has also played important roles in many applications in areas such as radio astronomy, speech analysis and biomedical systems. On the other hand, the problem of estimating the time delay in the signals at two spatially distant sensors has been an actively researched area due to its paramount applications in a wide number of areas such as radar, sonar, biomedical, and geophysics [3][4][5][6][7]. The problem of joint estimation of time delay and frequency of multiple sinusoidal sources has been studied by Wu et al [8,9] which is based on the measured data from two spatially separated sensors.…”

Section: Introductionmentioning

confidence: 99%

Joint Frequency and Time Estimation Algorithms

et al. 2016

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In this paper, we present six subspace decomposition based methods for joint time of arrival (TOA) and frequency of arrival (FOA) estimation of multiple incident sources. These are LU-TLS, QR-TLS, direct TSQR-TLS, direct TSLU-TLS, parallel TSQR-TLS, and parallel TSLU-TLS. The direct and parallel TSQR/TSLU-TLS are recently developed methods in subspace decomposition and are employed in this work for time and frequency estimation. The proposed methods employ a pair of spatially separated sensors to receive multiple incident source signals. A data matrix is constructed in the form of a Hankel matrix from multiple snapshots of the received signal. The information of both TOA and FOA of multiple incident sources is extracted from the data matrix by applying LU/QR techniques (in the first set of the methods) and a tall skinny TSLU/TSQR factorization in the second set. The estimates of the TOA and FOA are obtained from the signal subspace by applying the total least squares (TLS) method. Simulation results are presented to assess the performance of the proposed methods. B Nizar TayemThe effect of parametric variations on the performance has also been analyzed for all the proposed methods. Further, the computational times and complexities of the proposed methods are also computed and compared with each other.Keywords Time of arrival (TOA) · Frequency of arrival (FOA) estimation · QR and LU decomposition · Direct/parallel tall skinny QR/LU decomposition

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“…Assuming the estimated direction vector isâ(θ i ) (the ith column of the estimated direction matrixÂ). The normalization forâ(θ i ) is processed, and then normalized sequence is processed to attainû according to (29). Finally we use lease squares principle to estimate sin θ i .…”

Section: Angle Estimationmentioning

confidence: 99%

“…ESPRIT method is used for frequency and angle estimation under uniform circular array in [25,26]. The others joint angle and frequency estimation method is proposed in [28,29]. ESPRIT method algorithm requires eigen-value decomposition (EVD) to the cross spectral matrix or singular value decomposition (SVD) to the received data.…”

Section: Introductionmentioning

confidence: 99%

Novel Blind Joint Direction of Arrival and Frequency Estimation for Uniform Linear Array

Zhang¹,

Wang²,

Xu³

2008

PIER

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Fast Algorithm for Joint Azimuth and Elevation Angles, and Frequency Estimation via Hierarchical Space-Time Decomposition

Cited by 11 publications

References 9 publications

Antenna-Array-Assisted Frequency Offset Estimation and Data Detection in an Uplink Multiuser MIMO-OFDM Interference Network

Antenna-Array-Assisted Frequency Offset Estimation and Data Detection in an Uplink Multiuser MIMO-OFDM Interference Network

Joint Frequency and Time Estimation Algorithms

Novel Blind Joint Direction of Arrival and Frequency Estimation for Uniform Linear Array

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