无人潜航器舷侧阵声呐匹配场被动定位方法研究

为了使无人潜航器在复杂的浅海环境中具有对远程声源被动定位的能力，提出了一种将舷侧阵声呐与匹配场处理算法相结合的被动定位方法.该方法可用于无人潜航器定深低速直线巡航时定点数据采集的过程中.首先用匹配场算法处理在每个采样位置上收集到的舷侧阵数据，实现对远程声源的初步定位，然后融合处理所有采样位置上的匹配场定位输出．这样可以克服舷侧阵声呐空间增益低的缺点，降低海洋环境参数失配和舷侧阵位置误差的影响，稳定参数估计过程并提高输出信噪比，随着时间的推移逐渐估算出远程声源的三维坐标.仿真测试结果表明，该定位方法在不同的输入信噪比下具有较高的定位概率和定位精度，而且使用该方法的系统需求较低，便于实际应用.

Matched-field Source Localization Using the Flank Array of Unmanned Underwater Vehicle

In order to equip unmanned underwater vehicles with the capacity to passively localize long-range sources in shallow water, a localization approach which combines the flank array of unmanned underwater vehicle and matched-field processing was proposed. The proposed approach can be used as an unmanned underwater vehicle cruising at a low speed along a straight line at a certain depth and gathering data at sampling positions. Matched-field processing data received by the flank array at each sampling position provide the primary localization capacity. Furthermore, combining and analyzing already generated matched-field localization outputs can improve the localization performance against the lack of spatial gain due to the limited physical size of the flank array. It enhances the robustness to ocean environmental mismatch and position errors of the flank array, the stability in the estimation process, and the output signal-to-noise ratio. The three-dimensional coordinates of the source of interest can be gradually estimated over time. The ability of the proposed approach is examined through the synthetic test cases at different signal-to-noise ratios, and satisfactory results can be obtained in all cases. Additionally, the localization approach is proper for practical situations because the system requirement is not high.