转弯畸变拖曳阵声呐鲁棒Capon波束形成方法

针对柔性拖曳阵转弯机动过程，在积累时间内阵形变化导致自适应波束形成性能下降的问题，该文提出一种基于时变阵形聚焦和降维处理的低复杂度鲁棒波束形成方法。首先，基于阵列Water-Pulley模型估计时变阵形，以基准阵形为参考采用预成导向方法对阵形进行聚焦，消除数据中阵形模型偏差；然后，以阵形聚焦后数据协方差矩阵的共轭梯度方向矢量构成降维矩阵，构造大孔径阵列降维鲁棒Capon波束形成器。仿真结果表明:所提方法能够提高转弯机动拖曳阵波束形成输出信号-干扰噪声比(SINR)。海试验证表明:该方法能够提高测向空间谱上目标输出信噪比，提高大孔径阵列机动状态下对弱目标检测能力，同时能区分目标左右舷。

Robust Capon Beamforming for Towed Array Sonar During Maneuvering

The robust beamformer suffers performance degradation due to the distortion of towed array shape caused by the maneuverings of tow platform. To address this problem, a low complexity robust Capon beamforming method is proposed based on time-varying array focusing and dimension reduction. First, the array shape is estimated sequentially using the array heading data based on Water-Pulley model. The Sample Covariance Matrix (SCM) at each recording time is focused to a reference array model via the STeered Covariance Matrix (STCM) technique to eliminate the array model error. Then, the reducing transform matrix is formed based on the conjugate gradient direction vectors of the focused SCM. The reduced-dimension Capon beamformer is finally derived to calculate the spatial spectrum. The results of the simulations show that, the proposed method can improve the Signal-to-Interference-plus-Noise Ratio (SINR) of the beamforming during the maneuvering of towed array. The results of sea-trial data processing show that the proposed method can improve the output Signal-to-Noise Ratio (SNR) of the target, as well as the performance of detecting weak targets and solving the left-right ambiguity during maneuvering.