Verde Paula, Díez-González Javier, Ferrero-Guillén Rubén, Martínez-Gutiérrez Alberto, Perez Hilde
Department of Mechanical, Computer and Aerospace Engineering, Universidad de León, 24071 León, Spain.
Sensors (Basel). 2021 Apr 2;21(7):2458. doi: 10.3390/s21072458.
Local Positioning Systems (LPS) have become an active field of research in the last few years. Their application in harsh environments for high-demanded accuracy applications is allowing the development of technological activities such as autonomous navigation, indoor localization, or low-level flights in restricted environments. LPS consists of ad-hoc deployments of sensors which meets the design requirements of each activity. Among LPS, those based on temporal measurements are attracting higher interest due to their trade-off among accuracy, robustness, availability, and costs. The Time Difference of Arrival (TDOA) is extended in the literature for LPS applications and consequently we perform, in this paper, an analysis of the optimal sensor deployment of this architecture for achieving practical results. This is known as the Node Location Problem (NLP) and has been categorized as NP-Hard. Therefore, heuristic solutions such as Genetic Algorithms (GA) or Memetic Algorithms (MA) have been applied in the literature for the NLP. In this paper, we introduce an adaptation of the so-called MA-Solis Wets-Chains (MA-SW-Chains) for its application in the large-scale discrete discontinuous optimization of the NLP in urban scenarios. Our proposed algorithm MA-Variable Neighborhood Descent-Chains (MA-VND-Chains) outperforms the GA and the MA of previous proposals for the NLP, improving the accuracy achieved by 17% and by 10% respectively for the TDOA architecture in the urban scenario introduced.
在过去几年中，本地定位系统（LPS）已成为一个活跃的研究领域。它们在恶劣环境中用于高要求精度应用的情况，使得诸如自主导航、室内定位或在受限环境中的低空飞行等技术活动得以开展。LPS由满足各项活动设计要求的传感器临时部署组成。在LPS中，基于时间测量的系统因其在精度、鲁棒性、可用性和成本之间的权衡而受到更高关注。文献中对到达时间差（TDOA）进行了扩展以用于LPS应用，因此在本文中，我们对这种架构的最优传感器部署进行分析以获得实际结果。这就是所谓的节点定位问题（NLP），并且已被归类为NP难问题。因此，诸如遗传算法（GA）或混合算法（MA）等启发式解决方案已在文献中应用于NLP。在本文中，我们引入了所谓的MA - Solis Wets链（MA - SW - 链）的一种变体，用于其在城市场景中NLP的大规模离散不连续优化中的应用。我们提出的算法MA - 可变邻域下降链（MA - VND - 链）在NLP方面优于先前提议中的GA和MA，在引入的城市场景中，对于TDOA架构，分别将所达到的精度提高了17%和10%。
