基于空域稀疏性的方位依賴陣列誤差校正算法

李存勖; 陳伯孝

doi:10.11999/JEIT161318

基于空域稀疏性的方位依賴陣列誤差校正算法

doi: 10.11999/JEIT161318

李存勖¹,
陳伯孝¹

基金項(xiàng)目:

國家自然科學(xué)基金(61571344)

計(jì)量
- 文章訪問數(shù): 1189
- HTML全文瀏覽量: 150
- PDF下載量: 252
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-12-08
- 修回日期: 2017-03-30
- 刊出日期: 2017-09-19

Spatial Sparsity Based Method on Calibration of Direction-dependent Array Errors

LI Cunxu¹,
CHEN Baixiao¹

Funds:

The National Natural Science Foundation of China (61571344)

摘要

摘要: 針對(duì)方位依賴陣列誤差的校正問題，通過引入少量精確校正的輔助陣元，該文給出一種基于空域稀疏性的方位依賴陣列誤差校正算法。將受方位依賴陣列誤差擾動(dòng)的陣列流型表示為理想情況下的陣列流型與幅相誤差系數(shù)矩陣的乘積形式。同時(shí)利用接收信號(hào)的空域稀疏性，對(duì)接收信號(hào)進(jìn)行稀疏表示，將陣列誤差自校正問題轉(zhuǎn)化為一個(gè)二元最優(yōu)化問題，再通過交替迭代的優(yōu)化方式求得兩個(gè)優(yōu)化變量的最優(yōu)解，從而實(shí)現(xiàn)了信號(hào)方位與方位依賴陣列誤差的聯(lián)合估計(jì)。該文所提算法相比于已有算法性能提升明顯，參數(shù)估計(jì)性能優(yōu)于傳統(tǒng)算法且接近參數(shù)估計(jì)的Cramer-Rao下界，仿真實(shí)驗(yàn)也驗(yàn)證了算法的有效性和優(yōu)越性。
- 陣列誤差校正 /
- 波達(dá)方向估計(jì) /
- 空域稀疏性
Abstract: For calibration of direction-dependent gain-phase errors, with a few precisely calibrated instrumental sensors, a method that jointly estimates the direction-dependent gain-phase errors and the target azimuth by spatial sparsity of the signal is proposed. The array manifold that perturbed by direction-dependent gain-phase errors is denoted by the multiplication form of ideally array manifold and a gain-phase errors coefficient matrix, then the received signal is represented by sparse form. The calibration for gain-phase error problem is formulated as a dual optimization problem, through alternating iterative optimization method to acquire the optimal solution of the two optimization variables, so as to realize the signal incident angle and azimuth dependent amplitude and phase errors of the optimized calculation. In this paper, the proposed algorithm has better performance than the existing algorithm, performance of the proposed algorithm is approximate to the Cramer-Rao low bound. The simulation experiments verify the effectiveness and superiority of the proposed algorithm.
- Array calibration /
- Direction of arrive estimation /
- Spatial sparsity

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