Deterministic Blind Beamforming for Electromagnetic Vector Sensor Array

Zhang, Xiaofei; Xu, Dazhuan

doi:10.2528/pier08080402

Cited by 23 publications

(22 citation statements)

References 31 publications

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“…Although in our model, beamforming and whitening have similar structure and goals [26], this paper assumes array manifold is unknown. This implies that the proposed method is robust since it is efficient when array manifold calibration exists.…”

Section: Cyclostationarity Based-whiteningmentioning

confidence: 99%

Improved Blind Source Extraction for Time Delay Estimate in Passive Coherent Location System

Zhang

Wang²,

Li³

et al. 2012

PIER B

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Abstract-Target localization is one challenge in passive coherent location (PCL) radar system, and the time delay is the most important parameter in the location. The difficulty of time delay estimate (TDE) in PCL system is that the target signal is completely buried in direct path signal, multipath and clutter (DMC). The conventional clutter cancellation algorithms in matched filter (MF) are time consuming. In this paper, we propose a time delay estimate method based on blind source extraction (BSE) which directly extract the target signal from the mixed signals, thus a new passive coherent location system is built. In this model, the reference antenna is not needed any more. For low signal to interference plus noise ratio (SINR) and frequency overlapped signals in passive coherent location, we introduce the cyclostationarity to enhance the target signal. The experiments on FM broadcast signals show that the computational burden of the proposed algorithm is extremely small and the improved SNR satisfies the location requirements of PCL system.

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Section: Cyclostationarity Based-whiteningmentioning

confidence: 99%

Improved Blind Source Extraction for Time Delay Estimate in Passive Coherent Location System

Zhang

Wang²,

Li³

et al. 2012

PIER B

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“…We can use trilinear decomposition [22][23][24][25][26] for blind signal detection for polarization sensitive uniform square array. Trilinear decomposition algorithm is the common data detection method for trilinear model [18], and it has been discussed in detailed in [22].…”

Section: Let the Received Noisy Signalrmentioning

confidence: 99%

“…Trilinear/multilinear decomposition is thus naturally related to linear algebra for multi-way arrays [18]. Trilinear/multilinear decomposition is used widely in blind receiver detection for Directsequence code-division multiple access (CDMA) system [19], array signal processing [20][21][22][23][24][25][26], blind estimation of Multi-Input-MultiOutput (MIMO) system [27], blind Speech Separation [28], downlink receiver for space-time block-boded CDMA System [29] and multiuser detection for Single-Input-Multi-Output (SIMO) CDMA System [30].…”

Section: Introductionmentioning

confidence: 99%

Quadrilinear Decomposition-Based Blind Signal Detection for Polarization Sensitive Uniform Square Array

Shi¹,

Zhang²

2008

PIER

Self Cite

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Abstract-This paper links the polarization-sensitive-array signal detection problem to quadrilinear decomposition model. Exploiting this link, it generates a deterministic blind quadrilinear decompositionbased signal detection algorithm, which doesn't require DOA (direction of arrival) and polarization information and has blind and robust characteristics. The proposed algorithm fully utilizes the polarization, spatial and temporal diversity. The simulation results reveal that the performance of blind quadrilinear decomposition-based signal detection algorithm for polarization sensitive uniform square array is close to nonblind MMSE method, and even works better than trilinear decomposition algorithm.

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“…Therefore, we should resort the other approaches to estimate the unwrapped phase. In this paper, we exploit the robust beamforming approach [17][18][19][20][21] to estimate the unwrapped phase.…”

Section: Signal Model and Problem Statementmentioning

confidence: 99%

Application of Array Processing Techniques in Multibaseline Insar for High-Resolution Dem Reconstruction

Mao¹,

Liao²

2008

PIER M

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Abstract-This work is concerned with generating the digital elevation models (DEMs) from the SAR images of the region of interest using multiple sensor arrays that are fixed on the distributed satellites. We present an exact estimate of the unwrapped phase and the relationship between the unwrapped phase and the terrain height. The optimum scheme that jointly processes the signals from all sensors is based on the model of the multibaseline joint block vector. The method can simultaneously coregister the SAR images, phase unwrapping and DEM generation in the presence of the large coregistration errors. The performance of our approach is verified by a series of simulation experiments based on the distributed sensor arrays.

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Deterministic Blind Beamforming for Electromagnetic Vector Sensor Array

Cited by 23 publications

References 31 publications

Improved Blind Source Extraction for Time Delay Estimate in Passive Coherent Location System

Improved Blind Source Extraction for Time Delay Estimate in Passive Coherent Location System

Quadrilinear Decomposition-Based Blind Signal Detection for Polarization Sensitive Uniform Square Array

Application of Array Processing Techniques in Multibaseline Insar for High-Resolution Dem Reconstruction

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