Spatial Distribution Characteristics of Positioning Accuracy for Angle-of-Arrival Localization

• Published in: IEEE sensors journal Vol. 24; no. 19; pp. 30680 - 30689
• Main Authors: Cui, Jianfeng, Liang, Hong, Zhang, Huiyun, Li, Xingsheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithm shadow zone; Algorithms; angle-of-arrival (AOA); Angles (geometry); Apogees; Distribution functions; Errors; Exact solutions; Graphical models; Intersections; Localization; Mathematical models; Measurement errors; positioning accuracy (PA); Sensor arrays; Sensor phenomena and characterization; Sensors; Shadows; Spatial distribution; spatial distribution characteristics
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3446031

This study focused on the positioning performance of 3-D angle-of-arrival (AOA) localization. Although the positioning performance is known to be influenced by the sensor-target geometry, the spatial distribution characteristics have not been clearly defined. In this article, the generalized analytical solution for positioning accuracy (PA) with complex input errors was derived. Additionally, the tangency condition for geometric intersection was proposed to analyze the inherent features of PA. The findings suggest that PA exhibits a gradually changing distribution characteristic in space, with the low-accuracy zone near the apogee and the algorithm shadow zone. The algorithm shadow zone is dependent on the sensor deployment, which can be described by the tangency condition for geometric intersection. The spatial distribution characteristics of PA are influenced by both the sensor deployment and input parameter errors. When considering a fixed sensor array, PA is directly proportional to the standard deviation of input errors. The spatial relative distribution characteristics of PA are dependent on the variance ratio of input errors. The research findings were verified through Monte Carlo (MC) simulations. The spatial distribution characteristics of PA can serve as a theoretical foundation for sensor deployment and anti-detection strategies.