Parameter estimation of the near-field source using the PCA-BPalgorithm with the array error

被引：0
|
作者：
Wang L. [1 ]
Zhao P. [2 ]
Wang L. [1 ]
Wang G. [3 ]
机构：
[1] School of Physics and Optoelectronic Engineering, Xidian University, Xi'an
[2] School of Telecommunications Engineering, Xidian University, Xi'an
[3] School of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2022年 / 49卷 / 01期
关键词：
Covariance matrix; Direction of arrival; Near field source; Principal component analysis; The back propagation neural network;
D O I：
10.19665/j.issn1001-2400.2022.01.018
中图分类号：
学科分类号：
摘要：
The steering vector of the array will be biased when there is an error in the signal receiving array, which will affect the performance of the parameter estimation algorithm. In order to reduce the influence of the array error on the parameter estimation results and reduce the computational complexity, a combination of intelligent algorithms and principal component analysis is used.First, in order to avoid the tedious process of error modeling, the back propagation neural network method is used to include errors and other factors in the network model.Second, it takes too long and is quite complicated for the back propagation neural network to train the near-field source parameter estimation model. In order to shorten the training time and reduce the amount of calculation, the principal component analysis method is introduced in the back propagation neural network model to reduce the dimension of the signal feature matrix. Then the reduced-dimensional signal feature matrix is used as the input feature of the back propagation neural network, and the near-field source parameters are used as the expected output for training, so as to simplify the network structure and shorten the training time.Finally, the received data containing signal information to be estimated is input into the trained network model to obtain the estimated value of the signal incident direction. This algorithm can accurately estimate the parameters of the near-field source in the presence of errors in the receiving array, and improve the estimation performance of the near-field source signal parameters under a low signal-to-noise ratio.Simulation experimental results show the effectiveness of the algorithm in this paper. © 2022, Editorial Department of Journal of Xidian University. All right reserved.
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页码：181 / 187
页数：6
相关论文
共 18 条
  • [1] WANG L M, HUI Z, WANG S Z, Et al., Underdetermined DOA Estimation Algorithm Based on an Improved Nested Array[J], Wireless Personal Communications, 112, pp. 2423-2437, (2020)
  • [2] ZHANG M, LYU T., Multi-parameter Estimation Based on Improved MUSIC Algorithm for Polarization Sensitive Array
  • [3] ROY R, PAULRAJ A, KAILATH T., ESPRIT-A Subspace Rotation Approach to Estimation of Parameters of Cisoids in Noise[J], IEEE Transactions on Acoustics Speech & Signal Processing, 34, 5, pp. 1340-1342, (2003)
  • [4] HU Jianwei, ZHAO Wei, CUI Yanpeng, Et al., PHP Code Vulnerability Mining Technology Based on Theimproved ASTNN, Journal of Xidian University, 47, 6, pp. 164-173, (2020)
  • [5] KONG Xin, CHEN Gang, GONG Guoliang, Et al., High Performance Multiply-Accumulator for the Convolutional Neural Networks Accelerator, Journal of Xidian University, 47, 4, pp. 55-63, (2020)
  • [6] CHEN M, GONG Y, MAO X P., Deep Neural Network for Estimation of Direction of Arrival with Antenna Array[J], IEEE Access, 8, pp. 140688-140698, (2020)
  • [7] pp. 17-29, (2019)
  • [8] XIONG T, BAO Y, HU Z., Multiple-Output Support Vector Regression with a Firefly Algorithm for Interval-Valued Stock Price Index Forecasting[J], Knowledge-Based Systems, 55, pp. 87-100, (2014)
  • [9] WANG R K, WEN B Y, HUAN W M., A Support Vector Regression-Based Method for Target Direction of Arrival Estimation from HF Radar Data[J], IEEE Geoscience and Remote Sensing Letters, 15, 5, pp. 674-678, (2018)
  • [10] AN Yali, ZHOU Shuisheng, CHEN Li, Et al., Robust Support Vector Machines and Their Sparse Algorithms, Journal of Xidian University, 46, 1, pp. 64-72, (2019)
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