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MIMO Sonar DOA Estimation Based on Improved Transmitting Diversity Smoothing (TDS)

机译:基于改进的发射分集平滑(TDS)的MIMO声纳DOA估计

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A multiple-input multiple-output (MIMO) sonar has an effect of transmission diversity smoothing (TDS), which is able to “de-correlate” the echoes of multiple targets automatically. Accordingly, a series of high-resolution DOA estimation methods can be adopted directly without using conventional spatial smoothing, and hence avoiding the losses of array aperture and degree of freedom. However, the performance of TDS-based DOA estimation methods would degrade seriously in low signal-to-noise ratio (SNR) condition. In this paper, to solve the problem we proposed a new MIMO sonar DOA estimation method based on improved TDS. In order to improve the SNR of data used for DOA estimation, two steps are used. Firstly, we use the sum of all transmitting signals to matched filter echoes at the receiving hydrophone array. And secondly, we truncate outputs of matched filters around the zero-delay point. Synchronously, the number of truncated snapshots is carefully considered to retain the TDS effect. After the two-step processing, the TDS effect is still available and SNR of the data to be processed is remarkably improved. And thus, the performance of the TDS-based DOA estimation method is effectively improved. Numerical simulations show that the proposed method could significantly improve the performance of TDS-based DOA estimation methods in low SNR environment.
机译:多输入多输出(MIMO)声纳具有传输分集平滑(TDS)的效果,该功能能够自动“解相关”多个目标的回波。因此,可以直接采用一系列高分辨率的DOA估计方法,而无需使用常规的空间平滑处理,从而避免了阵列孔径和自由度的损失。但是,基于TDS的DOA估计方法的性能在低信噪比(SNR)条件下会严重降低。为了解决这一问题,我们提出了一种基于改进的TDS的MIMO声纳DOA估计方法。为了提高用于DOA估计的数据的SNR,使用了两个步骤。首先,我们使用所有发射信号的总和来匹配接收水听器阵列处的滤波器回波。其次,我们在零延迟点附近截断匹配滤波器的输出。同步地,仔细考虑截断快照的数量以保留TDS效果。经过两步处理后,TDS效果仍然可用,并且要处理的数据的SNR显着提高。因此,有效地提高了基于TDS的DOA估计方法的性能。数值仿真表明,该方法在低信噪比环境下可以显着提高基于TDS的DOA估计方法的性能。

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