附加自适应短时高程变化率约束的PPP/INS紧组合增强模型

doi:10.11947/j.AGCS.2024.20230371

摘要/Abstract

摘要：

卫星信号在城市高遮挡环境下受复杂干扰引起的质量下降甚至中断问题，常引发精密单点定位/惯性导航系统（PPP/INS）紧组合导航误差发散。基于常值高程假设提出的传统高程约束模型虽可有效抑制平缓路面下惯性导航系统的误差累积，但因其无法合理地适应路面高程变化而难以增强高遮挡环境下的PPP/INS紧组合模型。本文顾及载体运动中短时高程变化率相近的特性，提出一种自适应短时高程变化率的高程约束PPP/INS紧组合模型。采用模拟的遮挡环境和真实的城市环境下的车载试验验证本文模型有效性。在真实城市环境试验中，相比于无约束、顾及高程变化定权的高程常值约束、历元间高程常值约束3种PPP/INS紧组合模型，本文模型在高程方向上定位精度分别提升52.2%、49.2%、70.9%。

关键词: PPP/INS, 顾及短时高程变化率约束, 高程约束随机模型, 卫星信号中断

Abstract:

The GNSS signal suffers from quality degradation or outages due to blockages in the urban environment, leading to the error divergence of precise point positioning (PPP)/inertial navigation system (INS) tightly coupled system. Although the traditional height constraint models based on the constant height hypothesis can effectively suppress INS error accumulation under flat surface, it is difficult to enhance the PPP/INS tightly coupled model because of unadapting the change of surface under the high occlusion environment. In this paper, considering the similar characteristic of short-term height variation rate during vehicle experiment, an adaptive short-term height variation rate constraint (ASTHVRC) model of PPP/INS tightly coupled system is proposed. The effectiveness of the proposed model is verified by both simulated occlusive environment and real urban environment. In the real urban environment, comparing with the height constraint-free (HCF), the height rate-weighted constant height constraint (HRWHC) and the inter-epoch constant height constraint (ICHC), the results show that ASTHVRC model improves the accuracy of PPP/INS tightly coupled height solution by 52.2%, 49.2%, 70.9%, respectively.

Key words: PPP/INS, adaptive short-term height variation rate constraint, stochastic model of height constraint, satellite outage

中图分类号:

P228

程建华, 陈思成, 臧楠, 程思翔, 赵国晶, 马子凡. 附加自适应短时高程变化率约束的PPP/INS紧组合增强模型[J]. 测绘学报, 2024, 53(9): 1761-1776.

Jianhua CHENG, Sicheng CHEN, Nan ZANG, Sixiang CHENG, Guojing ZHAO, Zifan MA. PPP/INS tightly integrated enhancement model considering adaptive short-term height variation rate constraint[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(9): 1761-1776.

图/表 23

图1

表1

表2

图2

图3

表3

图4

表4

图5

图6

图7

图8

表5

图9

表6

表7

表8

图10

图11

表9

图12

表10

表11

参考文献 31

[1]	WANG Minghua, WANG Jiexian, DONG Danan, et al. Performance of BDS-3: satellite visibility and dilution of precision[J]. GPS Solutions, 2019, 23(2):56.
[2]	SHI Junbo, OUYANG Chenhao, HUANG Yongshuai, et al. Assessment of BDS-3 global positioning service: ephemeris, SPP, PPP, RTK, and new signal[J]. GPS Solutions, 2020, 24(3):81.
[3]	杨元喜. 弹性PNT基本框架[J]. 测绘学报, 2018, 47(7):893-898. DOI: 10.11947/j.AGCS.2018.20180149.
	YANG Yuanxi. Resilient PNT concept frame[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(7):893-898. DOI: 10.11947/j.AGCS.2018.20180149.
[4]	张小红, 李星星, 李盼. GNSS精密单点定位技术及应用进展[J]. 测绘学报, 2017, 46(10):1399-1407. DOI: 10.11947/j.AGCS.2017.20170327.
	ZHANG Xiaohong, LI Xingxing, LI Pan. Review of GNSS PPP and its application[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1399-1407. DOI: 10.11947/j.AGCS.2017.20170327.
[5]	HEGARTY C J, CHATRE E. Evolution of the global navigation satellite system (GNSS)[J]. Proceedings of the IEEE, 2008, 96(12):1902-1917.
[6]	KING A D. Inertial navigation-forty years of evolution[J]. GEC review, 1998, 13(3):140-149.
[7]	LI Bofeng, ZANG Nan, GE Haibo, et al. Single-frequency PPP models: analytical and numerical comparison[J]. Journal of Geodesy, 2019, 93(12):2499-2514.
[8]	LI Xingxing, WANG Huidan, LI Shengyu, et al. GIL: a tightly coupled GNSS PPP/INS/LiDAR method for precise vehicle navigation[J]. Satellite Navigation, 2021, 2(1):26.
[9]	CHANG Le, NIU Xiaoji, LIU Tianyi, et al. GNSS/INS/LiDAR-SLAM integrated navigation system based on graph optimization[J]. Remote Sensing, 2019, 11(9):1009.
[10]	LIU Fei, HAN Houzeng, CHENG Xin, et al. Performance of tightly coupled integration of GPS/BDS/MEMS-INS/odometer for real-time high-precision vehicle positioning in urban degraded and denied environment[J]. Journal of Sensors, 2020:8670262.
[11]	CHIANG Kaiwei, CHANG H W, LI Yuhua, et al. Assessment for INS/GNSS/odometer/barometer integration in loosely-coupled and tightly-coupled scheme in a GNSS-degraded environment[J]. IEEE Sensors Journal, 2020, 20(6):3057-3069.
[12]	VANA S, NACIRI N, BISNATH S. Benefits of motion constraining for robust, low-cost, dual-frequency gnss ppp+mems imu navigation[C]//Proceedings of 2020 IEEE/ION Position, Location and Navigation Symposium. Portland: IEEE, 2020: 1093-1103.
[13]	SUN Rui, YANG Yuanxi, CHIANG Kaiwei, et al. Robust IMU/GPS/VO integration for vehicle navigation in GNSS degraded urban areas[J]. IEEE Sensors Journal, 2020, 20(17):10110-10122.
[14]	柴艳菊, 欧吉坤, 袁运斌, 等. 附加方位约束的GPS/INS组合系统选权自适应卡尔曼滤波[J]. 测绘学报, 2011, 40(4):421-428.
	CHAI Yanju, OU Jikun, YUAN Yunbin, et al. The adaptive Kalman filtering for single antenna GPS/INS integrated system with heading angle constraint by selecting the parameter weights[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4):421-428.
[15]	DISSANAYAKE G, SUKKARIEH S, NEBOT E, et al. The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications[J]. IEEE Transactions on Robotics and Automation, 2001, 17(5):731-747.
[16]	CHENG Sixiang, CHENG Jianhua, ZANG Nan, et al. Adaptive non-holonomic constraint aiding multi-GNSS PPP/INS tightly coupled navigation in the urban environment[J]. GPS Solutions, 2023, 27(3):152.
[17]	KLEIN I, FILIN S, TOLEDO T. Vehicle constraints enhancement for supporting INS navigation in urban environments[J]. Navigation, 2011, 58(1):7-15.
[18]	NIU Xiaoji, ZHANG Hongping, CHIANG K W, et al. Using land-vehicle steering constraint to improve the heading estimation of MEMS GPS/INS georeferencing systems[C]//Proceedings of 2010 ISPRS-International Archives of the Photogrammetry. Kyoto: Remote Sensing Spatial Information Sciences, 2010: 1-5.
[19]	GODHA S, CANNON M E. GPS/MEMS INS integrated system for navigation in urban areas[J]. GPS Solutions, 2007, 11(3):193-203.
[20]	柴艳菊, 阳仁贵, 王海涛, 等. 附加运动约束提高GPS/INS导航参数估计精度[J]. 中国惯性技术学报, 2011, 19(1):28-32.
	CHAI Yanju, YANG Rengui, WANG Haitao, et al. Improving the navigation accuracy of GPS/INS by adding motion information constraints[J]. Journal of Chinese Inertial Technology, 2011, 19(1):28-32.
[21]	李彦杰, 杨元喜, 何海波. 附加约束条件对GNSS/INS组合导航结果的影响分析[J]. 武汉大学学报(信息科学版), 2017, 42(9):1249-1255.
	LI Yanjie, YANG Yuanxi, HE Haibo. Effects analysis of constraints on GNSS/INS integrated navigation[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9):1249-1255.
[22]	尹潇, 柴洪洲, 向民志, 等. 附加运动学约束的BDS抗差UKF导航算法[J]. 测绘学报, 2020, 49(11):1399-1406. DOI: 10.11947/j.AGCS.2020.20200149
	YIN Xiao, CHAI Hongzhou, XIANG Minzhi, et al. Robust UKF algorithm with motion constraint in BDS navigation[J]. Acta Geodaetica et Cartographica Sinica 2020, 49(11):1399-1406. DOI: 10.11947/j.AGCS.2020.2020149
[23]	YANG Jun, MA Jinfeng, LIU Xinning, et al. A height constrained adaptive Kalman filtering based on climbing motion model for GNSS positioning[J]. IEEE Sensors Journal, 2017, 17(21):7105-7113.
[24]	ZHANG Zhetao, LI Yuan, HE Xiufeng, et al. Resilient GNSS real-time kinematic precise positioning with inequality and equality constraints[J]. GPS Solutions, 2023, 27(3):116.
[25]	CHENG Sixiang, CHENG Jianhua, ZANG Nan, et al. A sequential student's t-based robust Kalman filter for multi-GNSS PPP/INS tightly coupled model in the urban environment[J]. Remote Sensing, 2022, 14(22):5878.
[26]	王利, 张勤, 黄观文, 等. 基于指数平滑法的GPS卫星钟差预报[J]. 武汉大学学报(信息科学版), 2017, 42(7):995-1001.
	WANG Li, ZHANG Qin, HUANG Guanwen, et al. GPS satellite clock bias prediction based on exponential smoothing method[J]. Geomatics and Information Science of Wuhan University, 2017, 42(7):995-1001.
[27]	MONTGOMERY D C, PECK E A, VINING G G. Introduction to linear regression analysis[M].[S.l.]: John Wiley & Sons, 2021.
[28]	PAN Cheng, QIAN Nijia, LI Zengke, et al. A robust adaptive cubature Kalman filter based on SVD for dual-antenna GNSS/MIMU tightly coupled integration[J]. Remote Sensing, 2021, 13(10):1943.
[29]	CHEN Kai, CHANG Guobin, CHEN Chao. GINav: a Matlab-based software for the data processing and analysis of a GNSS/INS integrated navigation system[J]. GPS Solutions, 2021, 25(3):108.
[30]	王甫红, 栾梦杰, 程雨欣, 等. 城市环境下智能手机车载GNSS/MEMS IMU紧组合定位算法[J]. 武汉大学学报(信息科学版), 2023, 48(7):1106-1116.
	WANG Fuhong, LUAN Mengjie, CHENG Yuxin, et al. Smartphone GNSS/MEMS IMU tightly-coupled integration positioning method for vehicular navigation in urban conditions[J]. Geomatics and Information Science of Wuhan University, 2023, 48(7):1106-1116.
[31]	李正帅, 缪玲娟, 周志强, 等. 神经网络修正的速度约束辅助车载SINS定位算法[J]. 宇航学报, 2022, 43(9):1236-1245.
	LI Zhengshuai, MIAO Lingjuan, ZHOU Zhiqiang, et al. Vehicle SINS positioning algorithm assisted by velocity constraint based on neural network modification[J]. Journal of Astronautics, 2022, 43(9):1236-1245.

参数	陀螺仪			加速度计
参数	SPAN CPT	KVH1750	μIMU	SPAN CPT	KVH1750	μIMU
初始零偏	20°/h	2°/h	—	0.5 m/s²	2×10^－2 m/s²	—
零偏不稳定性	1°/h	0.07°/h	—	7.5×10^－2 m/s²	7.5×10^－2 m/s²	7.5×10^－2 m/s²
零偏稳定性	—	—	6°/h	—	—	—
比例因子	1500×10^－6	≤50×10^－6	≤1400×10^－6	4000×10^－6	≤100×10^－6	≤1500×10^－6
随机游走	0.067°/sqrt(h)	0.012°/sqrt(h)	0.3°/sqrt(h)	5.5×10^－4/sqrt(Hz)	1.17×10^－3/sqrt(Hz)	2.5×10^－4/sqrt(Hz)

待估参数	估计策略
对流层延迟τ	天顶干延迟(Saastamoinen模型改正) 天顶湿延迟(估计为随机游走)
GPS接收机钟差dt_r，IF，k	估计为白噪声
系统间偏差ISB_k	估计为随机游走
无电离层组合浮点模糊度	估计为常值浮点解
加速度计偏差	估计为随机游走
陀螺仪偏差(μ_ε)	估计为随机游走

路段	数据集1			数据集2
路段	遮挡路段时间/s	坡度/(%)	高度截止角/(°)	遮挡路段时间/s	坡度/(%)	高度截止角/(°)
路段1	139~168	1.44	90	408~427	0.16	78，70
路段2	507~526	－0.80	78	498~612	0.45	78
路段3	747~776	1.14	78	653~672	7.69~－4.09	70
路段4	1142~1161	0.57	70	723~742	9.52~－0.53	80
路段5	1352~1381	－0.64	70，90	793~807	0.55	70
路段6	1637~1656	1.74	70	873~892	0.53~－2.24	78

模型	约束	函数模型	随机模型
1	无约束(HCF)	无	无
2	顾及高程变化定权的高程常值约束模型(HRWHC)		R_hc，k=(dh)²
3	历元间高程常值约束模型(ICHC)		R_hc，k=P_k－1
4	自适应短时高程变化率的高程约束模型(ASTHVRC)

模型	东方向		北方向		高程
模型	数据集1	数据集2	数据集1	数据集2	数据集1	数据集2
1	0.45	0.13	0.56	0.14	0.46	0.84
2	0.62	0.09	0.71	0.12	2.87	0.94
3	0.59	0.09	0.67	0.12	2.29	0.73
4	0.45	0.09	0.55	0.13	0.21	0.30