Target localization is an essential aspect of smart environmental monitoring, which has gained prominence thanks to the Internet of Things (IoT) paradigm. Localization is the process of determining the location of an unknown target within an area of interest (AoI) based on data gathered by IoT sensors. Existing target localization works assume full knowledge about the sensors without considering the cold start problem, which may arise when new sensors join the network. This is exacerbated by the problem of bootstrapping in IoT sensors, where the system needs to identify and register unknown sensors. Additionally, the variability in sensors characteristics is inevitable due to the numerous factors that may alter the sensors behavior, raising the problem of sensor bias. Existing works dealing with the aforementioned problems are inadequate, since they involve a pre-processing step and assume the bias to be static over time, resulting in inaccurate localization systems. Therefore, this work proposes a robust and dynamic, holistic localization system that can find the location of an unknown emitting target regardless of the sensors characteristics. The proposed system comprises of two phases: 1) characterization and localization phase; where a deep learning model is utilized to characterize the sensors based on their locations and readings only, whereas Bayesian algorithm is employed to determine the unknown target location by leveraging the predicted sensors characteristics, and 2) correction phase, where an update metric is devised to refine the predicted sensors characteristics based on the estimated target location. The validation of the proposed approach was conducted using real-life and synthetic datasets and compared against an existing benchmark, where it shows an improvement of ∼79%, in terms of both localization and prediction errors. Thus, showcasing the effectiveness and potential advantages of the proposed approach in localizing unknown target(s).

目标定位是智能环境监测的一个重要方面，由于物联网（IoT）范式而受到重视。定位是根据物联网传感器收集的数据确定感兴趣区域 (AoI) 内未知目标位置的过程。现有的目标定位工作假设对传感器有充分的了解，而不考虑冷启动问题，冷启动问题可能在新传感器加入网络时出现。物联网传感器的引导问题加剧了这种情况，系统需要识别和注册未知的传感器。此外，由于许多因素可能会改变传感器的行为，从而引发传感器偏差问题，因此传感器特性的变化是不可避免的。处理上述问题的现有工作是不够的，因为它们涉及预处理步骤并假设偏差随着时间的推移是静态的，从而导致定位系统不准确。因此，这项工作提出了一种稳健、动态、整体的定位系统，无论传感器特性如何，都可以找到未知发射目标的位置。所提出的系统包括两个阶段：1）表征和定位阶段；其中深度学习模型用于仅根据传感器的位置和读数来表征传感器，而贝叶斯算法用于通过利用预测的传感器特性来确定未知目标位置，以及 2) 校正阶段，其中设计了更新度量根据估计的目标位置细化预测的传感器特性。 所提出的方法的验证是使用现实生活和合成数据集进行的，并与现有基准进行比较，在定位和预测误差方面显示出约 79% 的改进。因此，展示了所提出的方法在定位未知目标方面的有效性和潜在优势。