DOA estimation and self-calibration algorithm for uniform circular array

2005 ◽  
Vol 41 (20) ◽  
pp. 1092 ◽  
Author(s):  
C. Qi ◽  
Y. Wang ◽  
Y. Zhang ◽  
H. Chen
Keyword(s):  
Doa Estimation ◽  
Circular Array ◽  
Self Calibration ◽  
Calibration Algorithm

scholarly journals Error Self-Calibration Algorithm for Acoustic Vector Sensor Array

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Peng Wang ◽  
Yujun Kong ◽  
Mingxing Zhang
Keyword(s):  
Sensor Array ◽  
Doa Estimation ◽  
Acoustic Vector Sensor ◽  
Estimation Performance ◽  
Self Calibration ◽  
Phase Errors ◽  
Vector Sensor ◽  
Calibration Algorithm ◽  
The Impact ◽  
Vector Sensor Array

In this paper, the errors of acoustic vector sensor array are classified, the impact factor of each error for the array signal model is derived, and the influence of each type of error on the direction-of-arrival (DOA) estimation performance of the array is compared by Monte Carlo experiments. Converting the directional error and location error to amplitude and phase errors, the optimization model and error self-calibration algorithm for acoustic vector sensor array are proposed. The simulation experiments and field experiment data processing of MEMS vector sensor array show that the proposed self-calibration algorithm has good parameter estimation performance and certain engineering practicability.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

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