forked from mfkiwl/GNSS_Programming
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathfor_report_0129.py
215 lines (183 loc) · 8.31 KB
/
for_report_0129.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
# -*- coding: utf-8 -*-
"""
@title: for_report
@author: iDeal0103
@status: Active
@type: Process
@created: 29-Jan-2022
@post-History: 29-Jan-2022
comment：
进展汇报
1.单点定位
2.差分定位
1）载波相位双差双历元
2）载波相位+伪距双差单历元
3.单
选取 Curting University的观测站CUAA CUBB CUCC CUT0 的观测数据
"""
# import module
import datetime
import numpy as np
import SinglePointPosition as SPP
import RTK
import RTK2
import utils.DoFile as DoFile
from attitude_determination.WOPP import *
from attitude_determination.TRIAD import *
import matplotlib.pyplot as plt
import utils.ResultAnalyse as ResultAnalyse
# 设置文件
broadcast_file = r"edata\attitude_project\rinex2\brdc0030.21n"
CUAA_observation_file = r"edata\attitude_project\rinex2\cuaa0030.21o"
CUBB_observation_file = r"edata\attitude_project\rinex2\cubb0030.21o"
CUCC_observation_file = r"edata\attitude_project\rinex2\cucc0030.21o"
CUT0_observation_file = r"edata\attitude_project\rinex2\cut00030.21o"
# 读取文件
br_records = DoFile.read_GPS_nFile(broadcast_file)
CUAA_ob_records = DoFile.read_Rinex2_oFile(CUAA_observation_file)
CUBB_ob_records = DoFile.read_Rinex2_oFile(CUBB_observation_file)
# CUCC_ob_records = DoFile.read_Rinex2_oFile(CUCC_observation_file)
# CUT0_ob_records = DoFile.read_Rinex2_oFile(CUT0_observation_file)
# print('文件读取完毕!')
# 设置观测站参考坐标
CUAA_coordinate = [-2364336.1554, 4870280.8223, -3360815.9725]
CUBB_coordinate = [-2364334.0912, 4870285.4649, -3360807.7523]
CUCC_coordinate = [-2364332.0167, 4870284.0337, -3360814.1380]
CUT0_coordinate = [-2364338.2011, 4870284.7857, -3360809.1387]
# 设置起始时间
Tr_strat = datetime.datetime(2021, 1, 3, 0, 1, 0)
Tr_end = datetime.datetime(2021, 1, 3, 22, 0, 0)
# 单点定位
true_coors = []
cal_coors = []
vs = []
Tr = Tr_strat
print("开始解算各历元单点定位!")
while Tr < Tr_end:
Xk, Yk, Zk, Q, v = SPP.SPP_on_broadcastrecords(CUAA_ob_records, br_records, Tr, cutoff=0,
init_coor=CUAA_coordinate, recalP=True, doTDC=True, doIDC=True)
print(Xk, Yk, Zk)
cal_coors.append([Xk, Yk, Zk])
true_coors.append(CUAA_coordinate)
vs.append(v)
Tr += datetime.timedelta(seconds=30)
SPP.cal_NEUerrors(true_coors, cal_coors)
SPP.cal_XYZerrors(true_coors, cal_coors)
SPP.cal_Coorerrors(true_coors, cal_coors)
print("neu各方向RMSE:", ResultAnalyse.get_NEU_rmse(true_coors, cal_coors))
print("坐标RMSE:", ResultAnalyse.get_coor_rmse(true_coors, cal_coors))
# # 差分定位
# true_coors = []
# cal_coors = []
# Tr = Tr_strat
# print("开始解算各历元差分定位!")
# while Tr < Tr_end:
# # Tr2 = Tr + datetime.timedelta(seconds=30*60)
# print(Tr.hour, Tr.minute, Tr.second)
# CoorXYZ, Q = RTK2.DD_onCarrierPhase_and_Pseudorange_1known(CUAA_ob_records, CUBB_ob_records, br_records, Tr,
# CUAA_coordinate, CUBB_coordinate, cutoff=5, ambi_fix=True)
# Xk, Yk, Zk = CoorXYZ
# cal_coors.append([Xk, Yk, Zk])
# true_coors.append(CUBB_coordinate)
# Tr += datetime.timedelta(seconds=30)
# SPP.cal_NEUerrors(true_coors, cal_coors)
# SPP.cal_XYZerrors(true_coors, cal_coors)
# SPP.cal_Coorerrors(true_coors, cal_coors)
# print("neu各方向RMSE:", ResultAnalyse.get_NEU_rmse(true_coors, cal_coors))
# print("坐标RMSE:", ResultAnalyse.get_coor_rmse(true_coors, cal_coors))
# true_coors = []
# cal_coors = []
# Tr = Tr_strat
# print("开始解算各历元差分定位!")
# while Tr < Tr_end:
# Tr2 = Tr + datetime.timedelta(seconds=30*60)
# print(Tr.hour, Tr.minute, Tr.second)
# CoorXYZ, Q = RTK.DD_onCarrierPhase_1known(CUT0_ob_records, CUBB_ob_records, br_records, Tr, Tr2,
# CUT0_coordinate, CUBB_coordinate, cutoff=15, ambi_fix=False)
# Xk, Yk, Zk = CoorXYZ
# cal_coors.append([Xk, Yk, Zk])
# true_coors.append(CUBB_coordinate)
# Tr += datetime.timedelta(seconds=30)
# SPP.cal_NEUerrors(true_coors, cal_coors)
# SPP.cal_XYZerrors(true_coors, cal_coors)
# # OPP和WOPP定姿
# # 在参考框架坐标系下
# f1 = np.array([-2.2775, 2.4688, 5.1465]) # CUCC -> CUBB
# f2 = np.array([3.9066, 1.7174, 0.1474]) # CUT0 -> CUBB
# f3 = np.array([-2.0202, 4.1602, 7.0336]) # CUAA -> CUT0
# f4 = np.array([-6.1844, 0.7520, 4.9993]) # CUCC -> CUT0
# F = get_matrix_from_vectors([f1.tolist(), f2.tolist(), f3.tolist(), f4.tolist()])
# # F = get_matrix_from_vectors([f1.tolist(), f2.tolist()])
# #
# r = rota.from_euler('zyx', [5, 7, 10], degrees=True)
#
# WOPP_residual = []
# OPP_residual = []
# TRIAD_residual = []
# dr = 0.05
# for i in range(1000):
# b1 = add_perturbation_to_vector(r.apply(f1), [dr, dr, dr]) # CUCC -> CUBB
# b2 = add_perturbation_to_vector(r.apply(f2), [dr, dr, dr]) # CUT0 -> CUBB
# b3 = add_perturbation_to_vector(r.apply(f3), [dr, dr, dr]) # CUAA -> CUT0
# b4 = add_perturbation_to_vector(r.apply(f4), [dr, dr, dr]) # CUCC -> CUT0
# B = get_matrix_from_vectors([b1.tolist(), b2.tolist(), b3.tolist(), b4.tolist()])
# # B = get_matrix_from_vectors([b1.tolist(), b2.tolist()])
# #
# # # OPP
# r_check_SVD = solve_OPP_withSVD(F, B, [1, 1, 1, 1])
# OPP_residual.append((np.array(rota.from_matrix(r_check_SVD).as_euler('zyx', degrees=True)) - np.array([5, 7, 10])).tolist())
# #
# # WOPP
# # Qbb = np.array([[0.02, 0.01, 0.01], [0.01, 0.02, 0.01], [0.01, 0.01, 0.02]])
# Qbb = np.array([[0.02, 0.01, 0.01], [0.01, 0.01, 0.02], [0.01, 0.02, 0.01]])
# QBB = Qbb
# for i in range(F.shape[1] - 1):
# QBB = diagonalize_squarematrix(QBB, Qbb)
# R, Qrr, R_check = solve_WOPP_withLagrangianMultipliers(B, F, QBB)
# # R, Qrr, R_check= solve_WOPP_withLagrangianMultipliers(B, F, Qbb)
# WOPP_residual.append((np.array(rota.from_matrix(R).as_euler('zyx', degrees=True)) - np.array([5, 7, 10])).tolist())
#
# # # TRIAD
# # R_TRIAD = solve_TRIAD(F, B)
# # TRIAD_residual.append((np.array(rota.from_matrix(R_TRIAD).as_euler('zyx', degrees=True)) - np.array([5, 7, 10])).tolist())
#
# #
# # # 画图
# x = list(range(1000))
# plt.rcParams['font.sans-serif'] = ['SimHei']
# plt.rcParams['axes.unicode_minus'] = False
# # plt.plot(np.array(OPP_residual)[:, 0], color="r", label="delta z / 度")
# # plt.plot(np.array(OPP_residual)[:, 1], color="g", label="delta x / 度")
# # plt.plot(np.array(OPP_residual)[:, 2], color="b", label="delta y / 度")
# plt.scatter(x, np.array(OPP_residual)[:, 0], color="r", label="delta z / 度")
# plt.scatter(x, np.array(OPP_residual)[:, 1], color="g", label="delta x / 度")
# plt.scatter(x, np.array(OPP_residual)[:, 2], color="b", label="delta y / 度")
# plt.legend(loc='upper right')
# plt.title("OPP姿态角度解算模拟(dr=%sm)"%str(dr))
# plt.show()
# print("OPP:", np.std(np.array(OPP_residual)[:, 0]), np.std(np.array(OPP_residual)[:, 1]), np.std(np.array(OPP_residual)[:, 2]))
# #
# plt.rcParams['font.sans-serif'] = ['SimHei']
# plt.rcParams['axes.unicode_minus'] = False
# # plt.plot(np.array(WOPP_residual)[:, 0], color="r", label="delta z / 度")
# # plt.plot(np.array(WOPP_residual)[:, 1], color="g", label="delta x / 度")
# # plt.plot(np.array(WOPP_residual)[:, 2], color="b", label="delta y / 度")
# plt.scatter(x, np.array(WOPP_residual)[:, 0], color="r", label="delta z / 度")
# plt.scatter(x, np.array(WOPP_residual)[:, 1], color="g", label="delta x / 度")
# plt.scatter(x, np.array(WOPP_residual)[:, 2], color="b", label="delta y / 度")
# plt.legend(loc='upper right')
# plt.title("WOPP姿态角度解算模拟(dr=%sm)"%str(dr))
# plt.show()
# print("WOPP:", np.std(np.array(WOPP_residual)[:, 0]), np.std(np.array(WOPP_residual)[:, 1]), np.std(np.array(WOPP_residual)[:, 2]))
#
# # plt.rcParams['font.sans-serif'] = ['SimHei']
# # plt.rcParams['axes.unicode_minus'] = False
# # plt.plot(np.array(TRIAD_residual)[:, 0], color="r", label="delta z / 度")
# # plt.plot(np.array(TRIAD_residual)[:, 1], color="g", label="delta x / 度")
# # plt.plot(np.array(TRIAD_residual)[:, 2], color="b", label="delta y / 度")
# # plt.scatter(x, np.array(TRIAD_residual)[:, 0], color="r", label="delta z / 度")
# # plt.scatter(x, np.array(TRIAD_residual)[:, 1], color="g", label="delta x / 度")
# # plt.scatter(x, np.array(TRIAD_residual)[:, 2], color="b", label="delta y / 度")
# # plt.legend(loc='upper right')
# # plt.title("TRIAD姿态角度解算模拟(dr=%sm)"%str(dr))
# # plt.show()