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Comprehensive Analysis of Beidou-3 PPP-B2b Performance Based on Adaptive Robust Extend Kalman Filter

基于自适应鲁棒扩展卡尔曼滤波器的北斗三号PPP-B2b性能综合分析

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Abstract

Beidou-3 navigation satellite system (BDS-3) initiated a real-time service for precise point positioning (PPP) using the B2b signal, mainly for users in China and surrounding areas. In this paper, the performance of PPP-B2b service is experimentally analyzed first. Then, the ionosphere-free model is established. In order to solve the problem of slow convergence for traditional PPP, an adaptive robust extend Kalman filter (AREKF) algorithm is developed. Unlike the error compensation models, it reflects the noise information in real time by adjusting the covariance matrix of the measurements and the weight matrix of the state vector. The experimental results are analyzed last. Evaluation results indicate that the corrections provided by PPP-B2b can significantly reduce the discontinuous error of the orbits and clock offsets caused by broadcast ephemeris updating. Positioning results confirm that AREKF outperforms EKF both in static and kinematic modes. Around 20% improvement in accuracy and 25% improvement in convergence speed are achieved, making it valuable for PPP processing.

摘要

北斗三号导航系统使用B2b信号为**及周边地区的用户提供实时精确点定位服务(PPP)。本文首先对PPP-B2b服务的性能进行了实验分析, 然后建立无电离层模型。为了解决PPP收敛慢的传统问题, 开发了一种自适应鲁棒扩展卡尔曼滤波(AREKF)算法。与误差补偿模型不同, 它通过调整测量值的协方差矩阵和状态向量的权重矩阵实时反映噪声信息。最后, 对实验结果进行了分析。实验评估结果表明, PPP-B2b校**可以显着降低广播星历更新引起的轨道和时钟的不连续性误差。定位结果表明, AREKF在静态和仿动态条件下均优于EKF。精确度提高了约20%, 收敛速度提升了约25%, 这对PPP的研究具有重要价值。

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Acknowledgment

The authors would like to thank SinoGNSS for their board, and Tang Haibo for his useful suggestions and support.

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  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yuan Wan  (万缘) & Xuchu Mao  (茅旭初)

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  1. Yuan Wan  (万缘)
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  2. Xuchu Mao  (茅旭初)
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Correspondence to Xuchu Mao  (茅旭初).

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Wan, Y., Mao, X. Comprehensive Analysis of Beidou-3 PPP-B2b Performance Based on Adaptive Robust Extend Kalman Filter. J. Shanghai Jiaotong Univ. (Sci.) (2023). https://doi.org/10.1007/s12204-023-2664-1

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