基于稀疏重構(gòu)的共形陣列穩(wěn)健自適應(yīng)波束形成算法

陳沛; 趙擁軍; 劉成城

doi:10.11999/JEIT160436

基于稀疏重構(gòu)的共形陣列穩(wěn)健自適應(yīng)波束形成算法

doi: 10.11999/JEIT160436 cstr: 32379.14.JEIT160436

基金項目:

國家自然科學(xué)基金(61401469)

計量
- 文章訪問數(shù): 1419
- HTML全文瀏覽量: 212
- PDF下載量: 447
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-04-29
- 修回日期: 2016-11-10
- 刊出日期: 2017-02-19

Robust Adaptive Beamforming Algorithm for Conformal Arrays Based on Sparse Reconstruction

Funds:

The National Natural Science Foundation of China (61401469)

摘要

摘要: 針對共形陣列天線自適應(yīng)波束形成中存在的通用性差、主瓣保形困難、計算復(fù)雜度高等問題，該文提出一種基于稀疏重構(gòu)的穩(wěn)健自適應(yīng)波束形成算法。該算法通過引入漸進(jìn)最小方差準(zhǔn)則，實現(xiàn)了干擾加噪聲協(xié)方差矩陣的稀疏重構(gòu)，并得到期望方向上的導(dǎo)向矢量估計，進(jìn)而求得波束形成器的最優(yōu)權(quán)矢量。該算法無需復(fù)雜的子陣分解或虛擬映射變換，適用于任意陣列形狀。仿真實驗驗證了該算法不僅保證了期望的主瓣響應(yīng)，同時對指向誤差有較好的穩(wěn)健性。與現(xiàn)有算法相比，該算法所需采樣快拍數(shù)少，計算復(fù)雜度低，收斂速度快，在較大的輸入信噪比范圍內(nèi)達(dá)到了較好的陣列輸出性能。
- 穩(wěn)健自適應(yīng)波束形成 /
- 共形陣列 /
- 漸進(jìn)最小方差準(zhǔn)則 /
- 稀疏重構(gòu)
Abstract: Adaptive beamforming techniques for conformal arrays suffer from poor universality, difficulty to maintain the main beam and high computational cost. A novel robust adaptive beamforming algorithm for conformal arrays based on sparse reconstruction is proposed to alleviate the existing problems. Firstly, by introducing the Asymptotic Minimum Variance (AMV) criterion, the Interference-Plus-Noise (IPN) covariance matrix reconstruction is realized in a sparse way. Secondly, the Steering Vector (SV) of the Signal Of Interest (SOI) is estimated. Finally, the optimal weight coefficients are achieved. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm and prove that this algorithm can achieve superior output performance over the existing adaptive beamforming methods for conformal arrays in a large range of Signal to Noise Ratio (SNR) of the SOI. Moreover, the proposed algorithm needs fewer snapshots with a lower computational cost and has a faster convergence rate.
- Robust adaptive beamforming /
- Conformal arrays /
- Asymptotic minimum variance /
- Sparse reconstruction

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