基于算子分裂的未知发射时间声源分布式到达时间定位
Distributed ToA Localization of Acoustic Sources with Unknown Time of Emission via Operator Splitting
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中文总结 AI 辅助
针对非合作声源被动定位中发射时间未知的问题，提出将定位重构为ToA问题，采用基于边的分布式ADMM求解，推导闭式更新并证明收敛性，仿真验证有效性。
中文摘要 AI 辅助
利用多个空间分布的接收器对非合作声源进行定位，对于海洋生物追踪、搜索救援行动以及水下环境中的海上安全等应用至关重要。在传统的到达时间（ToA）系统中，发射时间通常通过时钟同步或双向通信显式或隐式地已知，因此问题简化为基于距离的定位。然而，在被动设置中，仅有接收时间戳可用。因此，必须通过到达时间差（TDoA）预处理消除发射时间，或与源位置联合估计。针对单源情况，我们通过将被动定位直接重新表述为具有未知信号发射时间的ToA问题，解决了接收器网络上的分布式定位问题。这产生了一个分布式共识优化问题，我们使用算子分裂方法求解，即一种基于边的分布式交替方向乘子法（DADMM）方案，该方案将估计任务分解为通过一致性约束耦合的局部子问题。我们推导了局部DADMM子问题的闭式局部更新方程，并为测量模型的平滑近似建立了收敛性质。数值模拟验证了所提出方法的有效性。
英文摘要
Localization of non-cooperative acoustic sources using multiple spatially distributed receivers is critical for applications such as marine-life tracking, search-and-rescue operations, and maritime security in underwater environments. In conventional Time of Arrival (ToA) systems, the emission time is typically known explicitly or implicitly through clock synchronization or two-way communication, so the problem reduces to range-based localization. In passive settings, however, only reception timestamps are available. Thus, the emission time must be eliminated through Time Difference of Arrival (TDoA) preprocessing or estimated jointly with the source position. For the case of a single source, we address the distributed localization problem over a receiver network by reformulating passive localization directly as a ToA problem with unknown time of signal emission. This yields a distributed consensus optimization problem, which we solve using an operator-splitting method, namely an edge-based Distributed Alternating Direction Method of Multipliers (DADMM) scheme that decomposes the estimation task into local subproblems coupled through agreement constraints. We derive closed-form local update equations for the local DADMM subproblems and establish convergence properties for a smoothed approximation of the measurement model. Numerical simulations illustrate the efficacy of the proposed approach.
发表机构
- Institute for Systems and Robotics (ISR), LARSyS, Instituto Superior Técnico (IST), University of Lisbon, Portugal（里斯本大学高等技术学院系统机器人研究所（ISR），LARSyS）
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