High-Resolution Bearing Estimation via UNItary Decomposition Artificial Neural Network (UNIDANN)

スポンサーリンク

概要

• 論文の詳細を見る

• This paper describes a new Artificial Neural Network (ANN), UNItary Decomposition ANN (UNIDANN), which can perform the unitary eigendecomposition of the synaptic weight matrix. It is shown both analytically and quantitatively that if the synaptic weight matrix is Hermitian positive definite, the neural output, based on the proposed dynamic equation, will converge to the principal eigenvectors of the synaptic weight matrix. Compared with previous works, the UNIDANN possesses several advantageous features such as low compuration time and no synchronization problem due to the underlying analog circuit structure, faster convergence speed, accurate final results, and numerical stability. Some simulations with a particular emphasis on the applications to high resolution bearing estimation problems are also furnished to justify the proposed ANN.

• 社団法人電子情報通信学会の論文
• 1998-11-25

著者

• Lee Tong-yao

  The Department Of Electronic Engineering National Taiwan University Of Science And Technology

• Fang Wen-hsien

  The Department Of Electrical Engineering National Taiwan Ocean University

• Fang Wen-hsien

  The Department Of Electonic Engineering National Taiwan University Of Science And Technology

• CHANG Shun-Hsyung

  the Department of Electrical Engineering, National Taiwan Ocean University

• Chang Shun-hsyung

  The Department Of Electrical Engineering National Taiwan Ocean University

関連論文

• Decision Aided Hybrid MMSE/SIC Multiuser Detection : Structure and AME Performance Analysis (Spread Spectrum Technologies and Applications)
• High-Resolution Bearing Estimation via UNItary Decomposition Artificial Neural Network (UNIDANN)
• Fast, Robust Block Motion Estimation Using Simulated Annealing
• Robust Two-Dimensional Frequency Estimation by Using Higher Order Statistics
• Reduced-Order Root-MUSIC for DOA Estimation(Digital Signal Processing)

スポンサーリンク