CAJ | 학술논문

采样矩阵求逆（SMI）算法是常用的自适应旁瓣对消算法。针对在低快拍下数据协方差矩阵的小特征值扩散,进而引起自适应波束副瓣升高的问题,提出了一种改进SMI算法的方法。该方法通过快拍数据的滑动平均获得协方差矩阵,使协方差矩阵小特征散布变小,能够很好地抑制副瓣电平,从而使算法适用于低快拍数据。
채양구진구역（SMI）산법시상용적자괄응방판대소산법。침대재저쾌박하수거협방차구진적소특정치확산,진이인기자괄응파속부판승고적문제,제출료일충개진SMI산법적방법。해방법통과쾌박수거적활동평균획득협방차구진,사협방차구진소특정산포변소,능구흔호지억제부판전평,종이사산법괄용우저쾌박수거。
SMI algorithm is commonly used in adaptive sidelobe cancellation.With low-snapshot data,the small eigenvalues of the covariance matrix spread,and thereby causing that the level of adaptive beam sidelobe increases.To solve this problem,an improved method of SMI algorithm is proposed.Through getting covariance matrix from sliding average of the snapshot data,this method makes the spread of the small eigenvalues decrease,and can obtain low sidelobe,so it makes the SMI algorithm suitable for low-snapshot data.