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基于矢量基线融合的解相位模糊算法

Phase ambiguity resolution algorithm based on vector baseline fusion

  • 摘要: 为解决均匀圆阵超短基线系统使用窄带信号定位时出现的相位模糊问题, 基于平行基线算法提出一种矢量基线融合算法。首先引入伪模糊数概念, 建立均匀圆阵基线相位差的矢量约束条件, 将邻边短基线与对应长基线配对形成多组基线组合, 基于接收信号相位计算每组的伪模糊数集合; 其次采用分步搜索法, 根据阵元数量确定初筛保留的候选组合数量, 筛选得到各基线的候选伪模糊数; 然后构建融合相位差误差与时延符号约束的综合损失函数, 设置合适权值后对所有候选组合进行全局优化, 最终求解得到最优伪模糊数组合。仿真结果表明, 在−10 dB信噪比下, 该算法解模糊成功概率较平行基线算法提高44.7%; 湖试结果表明, 在300 m和700 m作用距离下, 算法解模糊成功率分别达到98.7%和92.6%, 在真实水下环境中展现出良好的鲁棒性与工程实用性。

     

    Abstract: To address the phase ambiguity difference problem encountered when narrowband signals are used for localization in uniform circular array based ultra-short baseline systems, this paper proposes a vector baseline fusion algorithm based on the parallel baseline algorithm. First, the concept of pseudo-ambiguity number is introduced, and the vector constraint condition for the baseline phase difference of uniform circular arrays is established. Adjacent short baselines are paired with corresponding long baselines to form multiple groups of baseline combinations, and the pseudo-ambiguity number set for each group is calculated based on the phase of the received signal. Second, a step-by-step search method is adopted: the number of candidate combinations retained after preliminary screening is determined according to the number of array elements, and candidate pseudo-ambiguity numbers for each baseline are obtained through screening. Then, a comprehensive loss function integrating phase difference error and time delay sign constraint is constructed. After setting appropriate weight values, global optimization is performed on all candidate combinations, and the optimal pseudo-ambiguity number combination is finally obtained through solution. Simulation results show that at a signal-to-noise ratio of −10 dB, the success probability of ambiguity resolution of the proposed algorithm is 44.7% higher than that of the parallel baseline algorithm. Lake test results indicate that at operating distances of 300 m and 700 m, the ambiguity resolution success rate of the algorithm reaches 98.7% and 92.6% respectively, demonstrating favorable robustness and engineering practicability in real underwater environments.

     

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