GNSS/UWB/INS indoor and outdoor seamless positioning algorithm based on federal filtering

全球导航卫星系统应用 计算机科学 加权 多径传播 惯性导航系统 实时计算 精密点定位 稳健性(进化) 全球定位系统 航位推算 算法 电信 方向(向量空间) 数学 基因 医学 放射科 频道(广播) 生物化学 化学 几何学
作者
Linghan Yao,Mi Li,Tianhe Xu,Xiaoji Dai,Tianyou Jiang,Peipei Dai,Sen Wang,Jianping Xing
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:35 (1): 015135-015135 被引量:5
标识
DOI:10.1088/1361-6501/ad03ba
摘要

Abstract An integrated navigation system plays an important role in the application of indoor and outdoor seamless positioning. Essentially, it is a flexible fusion positioning of multiple sensors with different combinations. The Global Navigation Satellite System (GNSS) can provide high-precision positioning and navigation in an outdoor environment with good observation conditions. Ultra-wide Band (UWB) has the characteristics of great bandwidth and anti-multipath, which can be applied to provide high-precision positioning and navigation indoors. Inertial Navigation System (INS) has independent navigation capabilities that can be tightly combined with GNSS and UWB to enhance positioning robustness. In response to the problem of a single sensor struggling to provide reliable location information for indoor and outdoor seamless positioning, this study applies federal filtering to integrate and construct the GNSS/UWB/INS integrated positioning algorithm based on Horizontal Dilation of Precision (HDOP) of UWB and carrier-to-noise ratio of GNSS weighting. The gross errors are first detected during the data preprocess, and an adaptive weighting approach to reduce the influence of the anomalous observations is constructed. The tight combination of GNSS/INS and UWB/INS is implemented in the subfilter of the federal filtering. In the main filter, the weights of the two subfilters are adjusted based on HDOP and carrier-to-noise ratio weighting, and the information is allocated and fused to reduce the impact of potential poor UWB geometric configuration on the positioning solutions. It is proven that the error in the horizontal direction in the indoor and outdoor seam area is less than 10 cm. Compared with conventional federal filtering and Huber-based Kalman filtering, the algorithm in this study improves the accuracy of the reference point in indoor and outdoor seam area by 46.1% and 15.4%, respectively, which is beneficial for applications in indoor and outdoor seamless activities.
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