PDF(Pubmed)
Abstract：
Non-line-of-sight (NLOS) errors significantly impact the accuracy of ultra-wideband (UWB) indoor positioning, posing a major barrier to its advancement. This study addresses the challenge of effectively distinguishing line-of-sight (LOS) from NLOS signals to enhance UWB positioning accuracy. Unlike existing research that focuses on optimizing deep learning network structures, our approach emphasizes the optimization of model parameters. We introduce a chaotic map for the initialization of the population and integrate a subtraction-average-based optimizer with a dynamic exploration probability to enhance the Snake Search Algorithm (SSA). This improved SSA optimizes the initial weights and thresholds of backpropagation (BP) neural networks for signal classification. Comparative evaluations with BP, Particle Swarm Optimizer-BP (PSO-BP), and Snake Optimizer-PB (SO-BP) models-performed using three performance metrics-demonstrate that our LTSSO-BP model achieves superior stability and accuracy, with classification accuracy, recall, and F1 score values of 90%, 91.41%, and 90.25%, respectively.
摘要：
非视距(NLOS)误差显著影响超宽带(UWB)室内定位精度,构成其进步的主要障碍。这项研究解决了有效区分视线（LOS）和NLOS信号以提高UWB定位精度的挑战。与专注于优化深度学习网络结构的现有研究不同，我们的方法强调模型参数的优化。我们引入了混沌映射来初始化种群，并将基于减法-平均值的优化器与动态探索概率集成在一起，以增强Snake搜索算法（SSA）。这种改进的SSA优化了反向传播（BP）神经网络的初始权重和阈值，以进行信号分类。与BP的比较评估，粒子群优化算法-BP(PSO-BP),和SnakeOptimizer-PB（SO-BP）模型-使用三个性能指标执行-证明我们的LTSSO-BP模型具有出色的稳定性和准确性，具有分类准确性，召回，F1得分值为90%，91.41%,90.25%,分别。
