Two-stage Reduced-dimension Adaptive Processing Method Based on the Spatial Data Decomposition
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摘要: 傳統(tǒng)的后多普勒自適應(yīng)處理方法，如因子法和擴(kuò)展因子法，雖然能大大降低自適應(yīng)處理時(shí)的運(yùn)算量和獨(dú)立同分布樣本的需求量，但在天線陣元數(shù)進(jìn)一步增大的情況下，還是不能有效抑制雜波。針對這一問題，該文提出一種空域數(shù)據(jù)分解的兩級降維自適應(yīng)處理方法。該方法將多普勒濾波后的空域數(shù)據(jù)進(jìn)行分解，使其變?yōu)閮蓚€(gè)向量的Kronecker乘積，得到一雙二次代價(jià)函數(shù)，利用循環(huán)迭代的思想求解最優(yōu)權(quán)。實(shí)驗(yàn)表明該方法具有快速收斂，所需訓(xùn)練樣本少的優(yōu)點(diǎn)，尤其在小樣本條件下該方法抑制雜波的性能明顯優(yōu)于因子法和擴(kuò)展因子法。
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關(guān)鍵詞:
- 雷達(dá)信號處理 /
- 空時(shí)自適應(yīng)處理(STAP) /
- 雜波抑制 /
- 降維
Abstract: The traditional post-Doppler adaptive processing approaches such as Factored Approach (FA) and Extended Factored Approach (EFA) can significantly reduce the computation-cost and training sample requirement in adaptive processing. However, their clutter suppression ability is considerably degraded with the increasing number of antenna elements. To solve this problem, a two-stage reduced-dimension adaptive processing method based on the decomposition of spatial data is proposed. This method decomposes the spatial data after Doppler filtering into a Kronecker product of two short vectors. Then a bi-quadratic cost function is obtained. The circular iteration is applied to solve the optimal weight. Experimental results show that the proposed method has the advantages of fast convergence and small training samples requirement. It has greater clutter suppression ability especially in small training samples support compared with FA and EFA. -
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