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

测绘学报 ›› 2024, Vol. 53 ›› Issue (9): 1761-1776.doi: 10.11947/j.AGCS.2024.20230371

附加自适应短时高程变化率约束的PPP/INS紧组合增强模型

程建华(), 陈思成, 臧楠(), 程思翔, 赵国晶, 马子凡

哈尔滨工程大学智能科学与工程学院，黑龙江　哈尔滨　150000

收稿日期:2023-09-07 出版日期:2024-10-16 发布日期:2024-10-16
通讯作者: 臧楠 E-mail:ins_cheng@163.com;zang6050@163.com
作者简介:程建华（1977—），男，博士，教授，研究方向为惯性及组合导航技术。E-mail：ins_cheng@163.com
基金资助:
国家自然科学基金(42204035);黑龙江省省重点研发计划(JD2023SJ09);国家重点研发计划(2021YFB3901300)

PPP/INS tightly integrated enhancement model considering adaptive short-term height variation rate constraint

Jianhua CHENG(), Sicheng CHEN, Nan ZANG(), Sixiang CHENG, Guojing ZHAO, Zifan MA

College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150000, China

Received:2023-09-07 Online:2024-10-16 Published:2024-10-16
Contact: Nan ZANG E-mail:ins_cheng@163.com;zang6050@163.com
About author:CHENG Jianhua (1977—), male, PhD, professor, majors in SINS and integration technology. E-mail: ins_cheng@163.com
Supported by:
The National Natural Science Foundation of China(42204035);Heilongjiang Provincial Key Research and Development Program(JD2023SJ09);The National Key Research and Development Program(2021YFB3901300)

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.

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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

附加自适应短时高程变化率约束的PPP/INS紧组合增强模型

PPP/INS tightly integrated enhancement model considering adaptive short-term height variation rate constraint

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图/表 23

参考文献 31

相关文章 15

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模型	路段1		路段2		路段3		路段4		路段5		路段6
模型	均方根	最大值	均方根	最大值	均方根	最大值	均方根	最大值	均方根	最大值	均方根	最大值
1	0.45	0.66	0.21	0.24	0.93	1.18	0.72	0.89	0.47	0.59	0.10	0.18
2	1.60	2.33	2.56	2.73	2.85	3.20	3.35	3.56	3.14	3.62	2.94	3.38
3	1.23	1.88	2.13	2.26	2.33	2.62	2.62	2.82	2.46	2.90	2.40	2.67
4	0.44	0.64	0.01	0.02	0.42	0.48	0.20	0.29	0.06	0.10	0.06	0.09

数据集	模型	30 s		60 s		90 s		120 s		150 s		180 s
数据集	模型	高程	三维	高程	三维	高程	三维	高程	三维	高程	三维	高程	三维
数据集1	1	0.90	4.92	0.73	18.67	1.19	43.26	2.35	90.55	3.64	164.73	5.43	269.83
	2	1.60	5.93	2.56	71.93	3.34	169.15	4.28	314.10	5.01	468.13	5.59	660.06
	3	1.87	5.13	2.85	17.19	3.62	45.31	4.56	119.43	5.30	206.17	5.91	337.80
	4	0.82	4.88	0.71	18.06	0.59	41.84	0.53	71.47	0.63	105.24	0.95	163.40
数据集2	1	0.16	0.91	0.20	3.22	1.28	7.78	3.77	15.15	7.71	25.51	13.02	38.01
	2	0.20	0.91	0.27	3.11	0.57	11.71	0.91	31.19	0.82	68.10	0.81	104.98
	3	0.23	0.96	0.30	3.67	0.59	13.66	0.94	40.35	0.85	61.93	0.84	113.56
	4	0.10	0.32	0.12	1.60	0.57	6.77	1.07	17.19	1.44	36.39	1.88	60.42

设置	路段1	路段2	路段3
遮挡路段时间/s	691~699	739~741，744，758	848~876
坡度/(%)	－0.27	1.86	－1.85

模型	路段1		路段2						路段3
	691~699 s		739~741 s		744 s		758 s		848~876 s
	均方根	最大值	均方根	最大值	均方根	最大值	均方根	最大值	均方根	最大值
1	0.05	0.08	2.03	2.08	1.54	1.54	0.99	0.99	1.27	2.68
2	0.20	0.34	0.91	1.04	1.48	1.48	1.95	1.95	1.58	1.73
3	0.12	0.23	0.72	0.77	0.80	0.80	0.90	0.90	0.98	1.18
4	0.05	0.07	0.07	0.11	0.14	0.14	0.44	0.44	0.54	0.86

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