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chan_test.py
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chan_test.py
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# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import matplotlib.pyplot as plt
from scipy import constants as C
import numpy as np
from scipy.optimize import fsolve
from scipy.optimize import minimize
from numpy import linalg as la
TB2AC_dis=np.array([5.35,4.51,3.10,4.52])
TAG2AC_dis=np.array([4.16,4.44,3.63,3.62])
TB2AC_T=TB2AC_dis/C.c
def f(x):
x0,x1,x2,x3,x4,x5 = x.tolist()
return [x0**2 + x1**2 - 3.12**2 ,
x2**2 + x3**2 - 7.79**2 ,
x4**2 + x5**2 - 7.03**2 ,
(x2-x0)**2 + x3**2 - 7.57**2 ,
(x4-x0)**2 + x5**2 - 8.05**2 ,
(x4-x2)**2 + (x5-x3)**2 - 3.07**2 ]
def chan_algorithm(arrivetime):
acnum=4
acpos=np.array([[0,0],
[10,0],
[0,10],
[10,10]])
idx=np.argsort(arrivetime)
arrivetime=np.take(arrivetime,idx)
base_pos=np.zeros((acnum,2))
for i in range(acnum):
base_pos[i,:]=acpos[idx[i],:]
evVal=np.concatenate((np.mat(arrivetime).T,base_pos),axis=1)
row, column = evVal.shape # 行，列
baseX = evVal[:, 1] # 列向量
baseY = evVal[:, 2]
ri1 = C.c*(evVal[:, 0] - evVal[0, 0])[1:] # 第i个基站和第一个基站之间的距离gui
xi1 = (baseX - baseX[0])[1:]
yi1 = (baseY - baseY[0])[1:]
k = np.zeros(row)
for i in range(0, row):
k[i] = baseX[i] ** 2 + baseY[i] ** 2
k = np.mat(k).T
# chan算法 ，计算标签坐标
def chan(evVal):
row, column = evVal.shape # 行，列
baseX = evVal[:, 1] # 列向量
baseY = evVal[:, 2]
ri1 = C.c * (evVal[:, 0] - evVal[0, 0])[:-1] # 第i个基站和第一个基站之间的距离gui
xi1 = (baseX - baseX[0])[1:]
yi1 = (baseY - baseY[0])[1:]
Standaraddeviation = 3.5e-2
k = np.zeros(row)
for i in range(0, row):
k[i] = baseX[i] ** 2 + baseY[i] ** 2
k = np.mat(k).T
h = np.zeros((3, 1))
for i in range(0, 3):
h[i, 0] = 0.5 * ((ri1[i]) ** 2 - k[i + 1] + k[0])
h = np.mat(h)
Ga = -np.bmat("xi1 yi1 ri1")
Q = np.zeros((row - 1, row - 1))
Q = np.mat(Q)
for i in range(0, row - 1):
Q[i, i] = (Standaraddeviation) ** 2
Za = (Ga.T * Q.I * Ga).I * Ga.T * Q.I * h
B1 = np.zeros((row - 1, row - 1))
for i in range(0, row - 1):
B1[i, i] = np.sqrt((baseX[i + 1] - Za[0]) ** 2 + (baseY[i + 1] - Za[1]) ** 2)
B1 = np.mat(B1)
P1 = C.c ** 2 * B1 * Q * B1
Za1 = (Ga.T * P1.I * Ga).I * Ga.T * P1.I * h
C0 = (Ga.T * P1.I * Ga).I
h1 = np.zeros((3, 1))
h1[0] = (Za1[0] - baseX[0]) ** 2
h1[1] = (Za1[1] - baseY[0]) ** 2
h1[2] = (Za1[2]) ** 2
h1 = np.mat(h1)
Ga1 = np.mat([[1, 0], [0, 1], [1, 1]])
r1 = np.sqrt((baseX[0] - Za1[0]) ** 2 + (baseY[0] - Za1[1]) ** 2)
B2 = np.zeros((3, 3))
B2[0, 0] = Za1[0] - baseX[0]
B2[1, 1] = Za1[1] - baseY[0]
B2[2, 2] = r1
B2 = np.mat(B2)
P2 = 4 * B2 * C0 * B2
Za2 = (Ga1.T * P2.I * Ga1).I * Ga1.T * P2.I * h1
ms0 = np.sqrt(np.abs(Za2))
ms0[0] = ms0[0] + baseX[0]
ms0[1] = ms0[1] + baseY[0]
return ms0
def main():
# 所有的都从0开始
MAXTIME = 17207356974694.4 * 1e-12
tmpi = 1
locbuff = []
Timestamp_lasttime = np.zeros(4)
Tref = np.zeros(4)
Tref_lasttime = np.zeros(4)
k = 0.6
syncpreiod = 0.233
tagdatacnt = 0
tagdatal = 0
discardcnt = 0
TBDATA = {}
TAGDATA = {}
# 求解实际标签、基站、同步基站坐标
result = fsolve(f, [0, 0, 0, 0, 0, 0])
coordi = np.array([[0, 0],
[3.12, 0],
[result[2], result[3]],
[result[4], result[5]]])
func_tb = lambda x: (x[0] ** 2 + x[1] ** 2 - 5.35 ** 2) ** 2 + ((x[0] - 3.12) ** 2 + x[1] ** 2 - 4.51 ** 2) ** 2 + \
((x[0] - result[2]) ** 2 + (x[1] - result[3]) ** 2 - 3.10 ** 2) ** 2 + \
((x[0] - result[4]) ** 2 + (x[1] - result[5]) ** 2 - 4.52 ** 2) ** 2
res_tb = minimize(func_tb, [0, 0])
tbcoordi =np.mat(res_tb.x)
func_tag = lambda x: (x[0] ** 2 + x[1] ** 2 - 4.16 ** 2) ** 2 + ((x[0] - 3.12) ** 2 + x[1] ** 2 - 4.44 ** 2) ** 2 + \
((x[0] - result[2]) ** 2 + (x[1] - result[3]) ** 2 - 3.63 ** 2) ** 2 + \
((x[0] - result[4]) ** 2 + (x[1] - result[5]) ** 2 - 3.62 ** 2) ** 2
res_tag = minimize(func_tag, [0, 0])
tagcoordi = np.mat(abs(res_tag.x))
# coordi = np.array([[0,0],
# [3.12,0],
# [2.1096,7.5007],
# [-0.9133,6.9687]])
# tbcoordi = np.mat([1.2102,3.8821])
# tagcoordi = np.mat([2.8804,4.5038])
# 读写数据
fin = open(r'233ms.dat', 'rb')
while tagdatacnt is not None:
#判断是否读完
tag = fin.read(2)
if tag == b'':
break
tagdatacnt = int.from_bytes(tag, byteorder='little', signed=False)
# 同步基站时间戳数据
for i in range(0, 4): # 读数据类型
fin.read(2)
TBDATA[i] = {}
TBDATA[i]['anchor'] = fin.read(2) # 第几号基站的数据
TBDATA[i]['tagID'] = fin.read(2) # 第几号标签的数据
TBDATA[i]['Idx'] = fin.read(2)
TBDATA[i]['Timestamp'] = int.from_bytes(fin.read(8), byteorder='little', signed=False)
TBDATA[i]['Timestamp'] = TBDATA[i]['Timestamp'] * 15.65e-12
# 标签数据
if tagdatacnt != 0:
tagdatal = tagdatal + 1
for i in range(0, tagdatacnt):
fin.read(2)
TAGDATA[i] = {}
TAGDATA[i]['tagID'] = fin.read(2)
TAGDATA[i]['Idx'] = fin.read(2)
TAGDATA[i]['Validmask'] = fin.read(2)
TAGDATA[i]['ACTimestamp']= {}
for j in range(0,4): # 基站到达时间戳数据 总共4个基站
TAGDATA[i]['ACTimestamp'].update({j:int.from_bytes(fin.read(8), byteorder='little', signed=False)})
TAGDATA[i]['ACTimestamp'][j] = TAGDATA[i]['ACTimestamp'][j] * 15.65e-12
# 运动传感器
TAGDATA[i]['MPUDATA'] = {}
for j in range(0,4):
TAGDATA[i]['MPUDATA'][j] = {}
TAGDATA[i]['MPUDATA'][j]['gyro'] = fin.read(6)
TAGDATA[i]['MPUDATA'][j]['accel'] = fin.read(6)
TAGDATA[i]['MPUDATA'][j]['quat'] = fin.read(16)
# 定位数据的计算 TAGTStamp记得从0开始
if tagdatacnt != 0:
for i in range(0, tagdatacnt):
TAGTStamp = np.zeros(4) # 从0开始
TStmpdiff = np.zeros(4)
TStmpArr = np.zeros(4)
tagVDcnt = 0
meanTS = 0
# 四个基站的时间戳处理
for j in range(0, 4):
if TAGDATA[i]['ACTimestamp'] != 0:
TAGTStamp[j] = TAGDATA[i]['ACTimestamp'][j]
if TAGTStamp[j] - Timestamp_lasttime[j] < 0:
TStmpdiff[j] = MAXTIME + TAGTStamp[j] - Timestamp_lasttime[j]
else:
TStmpdiff[j] = TAGTStamp[j] - Timestamp_lasttime[j]
meanTS = meanTS + TStmpdiff[j]
tagVDcnt = tagVDcnt + 1
meanTS = meanTS / tagVDcnt
for j in range(0, 4):
if TStmpdiff[j] != 0: #判断数据有没有污染
if np.abs(TStmpdiff[j] - meanTS) > 0.1: #有 丢弃
TAGDATA[i]['ACTimestamp'][j] = 0
tagVDcnt = tagVDcnt - 1
else: #没有 修正
TStmpdiff[j] = TStmpdiff[j] * syncpreiod / Tref_lasttime[j]
TStmpArr[j] = TStmpdiff[j] + TB2AC_T[j]
# 判断有没有数量的基站
if tagVDcnt >= 4:
evVal = np.zeros((4,3))
evVal = np.mat(evVal)
for j in range(0, 4):
if TAGDATA[i]['ACTimestamp'][j] != 0:
coordi_mat =np.mat(coordi)
evVal[j,0] = TStmpArr[j]
evVal[j,1] = coordi_mat[j,0]
evVal[j,2] = coordi_mat[j,1]
loc = chan(evVal)
if (la.norm(loc.T-tagcoordi)<3) :
print(loc)
#loc_print = loc.A
#plt.scatter(loc_print[0], loc_print[1], c='b', marker='^', label='loc')
else:
discardcnt = discardcnt + 1
else:
discardcnt = discardcnt + 1
#基站状态更新
for i in range(0,4):
if TBDATA[i]['Timestamp'] != 0: #存在基站更新数据
if Timestamp_lasttime[i] == 0: #判断是不是初始数据
Timestamp_lasttime[i] = TBDATA[i]['Timestamp']
else:
if Tref[i] == 0: #判断是不是初始数据
if TBDATA[i]['Timestamp'] - Timestamp_lasttime[i] < 0:
Tref[i] = MAXTIME + TBDATA[i]['Timestamp'] - Timestamp_lasttime[i]
else:
Tref[i] = TBDATA[i]['Timestamp'] - Timestamp_lasttime[i]
Tref_lasttime[i] = Tref[i]
Timestamp_lasttime[i] = TBDATA[i]['Timestamp']
else:
if TBDATA[i]['Timestamp'] - Timestamp_lasttime[i]< 0 :
Tref[i] = MAXTIME + TBDATA[i]['Timestamp'] - Timestamp_lasttime[i]
else:
Tref[i] = TBDATA[i]['Timestamp']-Timestamp_lasttime[i]
if Tref[i] > syncpreiod + 0.02 or Tref[i] < syncpreiod - 0.02:
Tref_lasttime[i] = Tref_lasttime[i]
Timestamp_lasttime[i] = Timestamp_lasttime[i]+Tref_lasttime[i]
else:
Tref[i] = Tref_lasttime[i] * (1-k) + k * Tref[i]
Tref_lasttime[i] = Tref[i]
Timestamp_lasttime[i] = TBDATA[i]['Timestamp']
else:
Tref_lasttime[i] = Tref_lasttime[i]
Timestamp_lasttime[i] = Timestamp_lasttime[i] + Tref_lasttime[i]
if __name__ == '__main__':
main()
plt.show()