一種改進(jìn)的基于低秩逼近的空時自適應(yīng)處理算法
doi: 10.11999/JEIT140832 cstr: 32379.14.JEIT140832
基金項目:
國家自然科學(xué)基金(61271293)和陜西省自然科學(xué)基金(2013JM8008)資助課題
An Improved Space-time Adaptive Processing Algorithm Based on Low Rank Approximation
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摘要: 針對非均勻雜波情況下的空時自適應(yīng)處理的小樣本問題，該文提出一種基于權(quán)矩陣低秩逼近的空時自適應(yīng)處理方法。與傳統(tǒng)的低秩逼近算法不同，利用空時導(dǎo)向矢量特殊的克羅累計性，該文重新構(gòu)造新的權(quán)矩陣，使得該權(quán)矩陣的行數(shù)與列數(shù)盡可能地相近或相同，以減少算法所需的樣本個數(shù)和計算量。采用低秩逼近方法對新構(gòu)造的權(quán)矩陣進(jìn)行表示，則原二次優(yōu)化問題轉(zhuǎn)化為求解一個雙二次代價函數(shù)問題。實驗表明，改進(jìn)的空時權(quán)矩陣低秩逼近方法能有效地提高空時自適應(yīng)處理的收斂速度和降低算法復(fù)雜度。
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關(guān)鍵詞:
- 機(jī)載雷達(dá) /
- 空時自適應(yīng) /
- 低秩逼近 /
- 雜波抑制 /
- 雙迭代
Abstract: To handle the small sample support problem under the heterogeneous clutter environment, a fast convergence Space-Time Adaptive Processing (STAP) algorithm based on low-rank approximation of the weight matrix is proposed. Unlike the traditional Low-Rank Approximation (LRA) algorithm for STAP, the weight matrix is reconstructed so that the numbers of its columns and rows are the same or close to each other by utilizing the special Kronecker property of the space time steering vector, which to reduce the requirement of samples and computational load. By using the low-rank approximation method to approximate the adaptive weight matrix, the original quadratic optimal problem transforms into a bi-quadratic optimal problem. Experimental results verify that the Improved LRA (ILRA) method can improve the convergence rate and reduce the computational load. -
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