西安电子科技大学学报（自然科学版）2026，Vol.53Issue(3)：77-91,15.DOI:10.19665/j.issn1001-2400.20260303
信号像素化下的多模态定位方法
Multi-modal localization method under signal pixelization
摘要
Abstract
This paper proposes an image-pixel-guided signal space and vision fusion localization algorithm(CDV),which innovatively transforms the simplistic numerical descriptions of individual signals into expres-sions for inter-signal numerical differences.By introducing the Signal Feature Analogy Index(FFAI)to construct a Signal Feature Pixel Map(SFPM)with multi-view representations,the algorithm achieves a profound transition from one-dimensional spatial signal intensity characteristics to two-dimensional pixelated descriptions.At the model construction level,the algorithm leverages the DPSE network to incorporate a contrastive learning mechanism so as to establish a localization model rich in multimodal feature information through the deep mining of similarity metrics and relative signal strength differences,thereby significantly enhancing the system's perception of complex environmental features.To further ensure the reliability of localization results,this paper employs a pair of monocular vision sensors to simulate a"quasi-binocular"visual effect,utilizes geometric constraints to perform visual space calibration on the signal-based results and achieve effective synergy between signal representation and visual observation.Validated by real-world tests with a 1-m sampling interval,the multimodal CDV method demonstrates a superior performance,achieving a precise Root Mean Square Error(RMSE)of only 1.100m within the Euclidean distance scale,with the localization error rate strictly controlled below 10%.关键词
信号特征像素图/指纹定位/对比学习/视觉定位/多传感器融合Key words
signal feature pixel map/wifi fingerprint localization/contrastive learning/visual localization/multi-sensor fusion分类
信息技术与安全科学引用本文复制引用
谢楚帆,秦宁宁,张欣,王艳..信号像素化下的多模态定位方法[J].西安电子科技大学学报（自然科学版）,2026,53(3):77-91,15.基金项目
长三角科技创新共同体联合研究(2023CSJGG1700) （2023CSJGG1700）
