# sensors_serial[2] = j[i][0]
list[1] = j[i][1]
if j[i][0] == 13:
# sensors_serial[2] = j[i][0]
list[2] = j[i][1]
plane_tdoa = calcTDOAByMessage(list)
plane_tdoa_int = float2Int(plane_tdoa)
enc_plane_tdoa_int = vhe.encrypt_distance(M_TDOA, plane_tdoa_int)
top_k_index = calcEncTopKInex(enc_plane_tdoa_int, enc_tdoa, H)
print("top", top_k_index)
encSumCoor = calcEncLocationEstimationSum(top_k_index, enc_coor)
# 对密文位置和解密
decSumCoor = vhe.decrypt(S_COOR, encSumCoor)
# 解密后再缩小放大倍数 再取均值
predictCoor = decSumCoor / (5 * S2NS)
print("predictCoor", predictCoor)
# 然后转换为llh坐标
predictLLH = ecef2llh(predictCoor)
# 开始预测
legal_pre = encQuicklyIdentify(lat, lon, predictLLH[0], predictLLH[1])
t1 = time.time()
print("完成第%d条消息的快速认证，耗时=%.2f,进度为:%.2f%%" %
(index + 1, (t1 - t0), ((index + 1) / len(messages) * 100)))
# count+=1
if legal == 1 and legal_pre == 1:
TP += 1
if j[i][0] == 13:
plane_toa[2] = j[i][1]
plane_tdoa = calcTDOAByMessage(plane_toa)
# 将浮点型的飞机tdoa转换为整型
plane_tdoa_int = float2Int(plane_tdoa)
enc_plane_tdoa_int = vhe.encrypt_distance(EncTDOA_M, plane_tdoa_int)
# 加密的飞机的tdoa和网格的tdoa逐条计算密文欧式距离求top5的index
top_k_index = calcEncTopKInex(enc_plane_tdoa_int, EncTDOA, EncTDOA_H)
# 根据索引 再Coor中寻找对应的坐标数据累加
encSumCoor = calcEncLocationEstimationSum(top_k_index, EncCOOR)
# 对密文位置和解密
decSumCoor = vhe.decrypt(EncCOOR_S, encSumCoor)
# 解密后再缩小放大倍数 再取均值
predictCoor = decSumCoor / (5 * S2NS)
# 然后转换为llh坐标
predictLLH = ecef2llh(predictCoor)
error_llh = geodesic((lat, lon), (predictLLH[0], predictLLH[1])).m
print("预测的LLH位置和飞机的LLH位置的误差为%.2fm" % error_llh)
error.append(error_llh)
predictError = predictError.append([{
'id': id,
'latitude': lat,
"longitude": lon,
'height': height,
'predictLatitude': predictLLH[0],
'predictLongitude': predictLLH[1],