A Novel PARAFAC Model for Processing the Nested Vector-Sensor Array

In this paper, a novel parallel factor (PARAFAC) model for processing the nested vector-sensor array is proposed. It is first shown that a nested vector-sensor array can be divided into multiple nested scalar-sensor subarrays. By means of the autocorrelation matrices of the measurements of these sub...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 18; H. 11; S. 3708
Hauptverfasser: Rao, Wei, Li, Dan, Zhang, Jian Qiu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI 31.10.2018
MDPI AG
Schlagworte:
direction of arrival estimation
nested array
parallel factor (PARAFAC) decomposition
vector sensor
parallel factor (PARAFAC) decomposition
direction of arrival estimation
vector sensor
nested array
ISSN:1424-8220, 1424-8220
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Zusammenfassung:In this paper, a novel parallel factor (PARAFAC) model for processing the nested vector-sensor array is proposed. It is first shown that a nested vector-sensor array can be divided into multiple nested scalar-sensor subarrays. By means of the autocorrelation matrices of the measurements of these subarrays and the cross-correlation matrices among them, it is then demonstrated that these subarrays can be transformed into virtual scalar-sensor uniform linear arrays (ULAs). When the measurement matrices of these scalar-sensor ULAs are combined to form a third-order tensor, a novel PARAFAC model is obtained, which corresponds to a longer vector-sensor ULA and includes all of the measurements of the difference co-array constructed from the original nested vector-sensor array. Analyses show that the proposed PARAFAC model can fully use all of the measurements of the difference co-array, instead of its partial measurements as the reported models do in literature. It implies that all of the measurements of the difference co-array can be fully exploited to do the 2-D direction of arrival (DOA) and polarization parameter estimation effectively by a PARAFAC decomposition method so that both the better estimation performance and slightly improved identifiability are achieved. Simulation results confirm the efficiency of the proposed model.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s18113708