改进了海底声学传感器网络的位置优化方法，大幅降低计算成本。
Improved Approximation of Sensor Network Performance for Seabed Acoustic Sensors
- 基于泊松目标模型，用更优的近似方法计算空隙概率。
- 在弗吉尼亚汉普顿航道数据上，误差比之前方法显著降低。
- 适合需要高效部署海洋监视传感器的工程应用。
为检测泊松分布的目标（如海底传感器探测航运交通），可通过优化传感器位置来最大化空隙概率（即检测到所有目标的概率）。由于空隙概率的计算成本高，本文提出一种新近似方法，显著降低网络位置选择的计算开销。该方法在前人使用詹森不等式近似空隙概率的基础上，更好处理泊松目标模型中的不确定性，得到更紧的误差界。通过弗吉尼亚州汉普顿航道的历史船舶交通数据验证，新方法相比先前方法显著降低了近似误差，证实其在海上监视应用中的有效性。
原文摘要 · Abstract (English)
Sensor locations to detect Poisson-distributed targets, such as seabed sensors that detect shipping traffic, can be selected to maximize the so-called void probability, which is the probability of detecting all targets. Because evaluation of void probability is computationally expensive, we propose a new approximation of void probability that can greatly reduce the computational cost of selecting locations for a network of sensors. We build upon prior work that approximates void probability using Jensen's inequality. Our new approach better accommodates uncertainty in the (Poisson) target model and yields a sharper error bound. The proposed method is evaluated using historical ship traffic data from the Hampton Roads Channel, Virginia, demonstrating a reduction in the approximation error compared to the previous approach. The results validate the effectiveness of the improved approximation for maritime surveillance applications.
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