Azimuth Sampling Optimization Scheme for Sparse Microwave Imaging Based on Mutual Coherence Criterion
-
摘要: 稀疏微波成像將稀疏信號(hào)處理理論系統(tǒng)性地引入微波成像中，與傳統(tǒng)合成孔徑雷達(dá)成像相比，具有提高成像質(zhì)量、降低系統(tǒng)復(fù)雜度等優(yōu)點(diǎn)。稀疏采樣方式是影響稀疏微波成像重建質(zhì)量的重要因素。該文主要研究方位向稀疏采樣的優(yōu)化問題，分析了稀疏微波成像觀測矩陣的相關(guān)系數(shù)與重建能力的關(guān)系，在此基礎(chǔ)上提出一種基于相關(guān)系數(shù)的優(yōu)化準(zhǔn)則，并對方位向稀疏采樣參數(shù)進(jìn)行優(yōu)化。仿真結(jié)果驗(yàn)證了所提優(yōu)化方法的有效性。
-
關(guān)鍵詞:
- 合成孔徑雷達(dá) /
- 稀疏微波成像 /
- 方位向采樣 /
- 相關(guān)準(zhǔn)則
Abstract: Sparse microwave imaging is a novel theory that systematically introduces sparse signal processing to microwave imaging. Compared with conventional synthetic aperture radar imaging, sparse microwave imaging exhibits the advantage of better imagery quality and lower system complexity. Non-ambiguity reconstruction for sparse scene can be achieved on under-sampling raw data by means of sparse microwave imaging, which leads to total data amount reduction. The imagery quality of sparse microwave imaging depends on the recovery property of measurement matrix, which is affected by the sparse sampling strategy. This paper focuses on the problem of design the azimuth sparse sampling scheme. The connection between mutual coherence and recovery property of the measurement matrix is analyzed. A mutual coherence based criterion is then proposed and applied to optimize the existing azimuth sparse sampling scheme. Numerical results demonstrate the effectiveness of the proposed method and conclusions are discussed.-
Key words:
- SAR /
- Sparse microwave imaging /
- Azimuth sampling /
- Mutual coherence criterion
-
計(jì)量
- 文章訪問數(shù): 2197
- HTML全文瀏覽量: 199
- PDF下載量: 574
- 被引次數(shù): 0
下載:
