Costa Russell, Wettergren Thomas A
Naval Undersea Warfare Center, Newport, RI 02841, USA.
Sensors (Basel). 2025 Sep 1;25(17):5381. doi: 10.3390/s25175381.
Optimizing sensor placement for the accurate localization of an uncertain source is crucial for a variety of distributed sensor network applications. To handle the uncertainty of the source locations, objective functions are typically written as an aggregation over a variety of plausible source locations. While prior research has explored how the resulting optimized sensor configurations correspond to the optimization over different objectives and various aggregations, the combined impact of both a complex noise environment and the choice of aggregation function for handling source uncertainty remains largely unexplored. This paper investigates this critical interplay. We demonstrate that incorporating distance-dependent environmental noise models reveals a strong dependence of the optimal sensor configuration on the aggregation method. This dependence affects diverse sensor types in differing ways, and this distinction is illustrated by examining both bearings-only and range-only sensors. We develop computational strategies for finding optimal configurations for each of these sensor types, and illustrate their distinctive features through canonical example problems for each type. The results underscore the importance of carefully considering both the environmental complexity and the aggregation approach when employing robust and reliable localization systems in practical applications.
对于各种分布式传感器网络应用而言，优化传感器布局以准确确定不确定源的位置至关重要。为了处理源位置的不确定性，目标函数通常被写成对各种合理源位置的汇总。虽然先前的研究已经探讨了由此产生的优化传感器配置如何对应于不同目标和各种汇总上的优化，但复杂噪声环境和用于处理源不确定性的汇总函数选择的综合影响在很大程度上仍未得到探索。本文研究了这种关键的相互作用。我们证明，纳入距离相关的环境噪声模型揭示了最优传感器配置对汇总方法的强烈依赖性。这种依赖性以不同方式影响不同类型的传感器，通过研究仅方位传感器和仅距离传感器来说明这种区别。我们为每种传感器类型开发了寻找最优配置的计算策略，并通过每种类型的典型示例问题说明了它们的独特特征。结果强调了在实际应用中采用强大可靠的定位系统时，仔细考虑环境复杂性和汇总方法的重要性。
