STK12与Python联合仿真（三）：分析星座覆盖性能

2024-06-02 16:16| 来源: 网络整理| 查看: 265

分析星座覆盖性能打开STK，连接到工程创建种子星 (STK)创建种子星 (Python)生成星座Python 创建覆盖网格绑定卫星的传感器建立星座定义多重网格计算与绘图结语

打开STK，连接到工程

jupyter：

导入相关的包

from agi.stk12.stkdesktop import STKDesktop from agi.stk12.stkobjects import * from agi.stk12.stkutil import * from agi.stk12.vgt import * import os

链接STK

STK_PID = 5600 # 根据自己刚刚得到的PID stk = STKDesktop.AttachToApplication(pid=int(STK_PID)) # stk = STKDesktop.StartApplication(visible=True) #using optional visible argument root = stk.Root print(type(root)) scenario = root.CurrentScenario # 链接当前场景创建种子星 (STK)

这里我在STK手动建立了高度600km，倾角75° 的种子卫星，并携带了对地观测角80°的传感器然后建立Wakler星座在这里插入图片描述设置36个轨道面，每个轨道面10个星结果如下：

创建种子星 (Python) # 创建星座 —— 种子卫星 sat_seed = scenario.Children.New(AgESTKObjectType.eSatellite,'COL') # 种子卫星 # 种子卫星属性 sat_seed.SetPropagatorType(2) # J4 摄动 keplerian = sat_seed.Propagator.InitialState.Representation.ConvertTo(1) # eOrbitStateClassical, Use the Classical Element interface keplerian.SizeShapeType = 0 # eSizeShapeAltitude, Changes from Ecc/Inc to Perigee/Apogee Altitude keplerian.LocationType = 5 # eLocationTrueAnomaly, Makes sure True Anomaly is being used keplerian.Orientation.AscNodeType = 0 # eAscNodeLAN, Use LAN instead of RAAN for data entry# Assign the perigee and apogee altitude values: keplerian.SizeShape.PerigeeAltitude = 600 # km 近地点高度 keplerian.SizeShape.ApogeeAltitude = 600 # km 远地点高度# Assign the other desired orbital parameters: keplerian.Orientation.Inclination = 75 # deg 倾角 keplerian.Orientation.ArgOfPerigee = 0 # deg 近地点幅角度 keplerian.Orientation.AscNode.Value = 0 # deg keplerian.Location.Value = 0 # deg 平近点角# Apply the changes made to the satellite's state and propagate: sat_seed.Propagator.InitialState.Representation.Assign(keplerian) sat_seed.Propagator.Propagate() 添加传感器，命名Cam # 添加传感器 sensor = sat_seed.Children.New(AgESTKObjectType.eSensor,'Cam') # 传感器属性 sensor.CommonTasks.SetPatternSimpleConic(40,1) # 半张角40°，角分辨率1° LOS = sensor.AccessConstraints.AddConstraint(34) # Range 类型 # 对照 https://help.agi.com/stkdevkit/Content/DocX/STKObjects~Enumerations~AgEAccessConstraints_EN.html LOS = LOS.QueryInterface(STKObjects.IAgAccessCnstrMinMax) # 如果报错没有QueryInterface方法就把这一段注释 LOS.EnableMax = True LOS.Max = 1100

这里解释一下约束首先https://help.agi.com/stkdevkit/Content/DocX/STKObjectsEnumerationsAgEAccessConstraints_EN.html 这里解释sensor.AccessConstraints.AddConstraint(34)是IAgAccessCnstrMinMax的Range 类型因此要接入STKObjects.IAgAccessCnstrMinMax，然后LOS.EnableMax对应的是图中的可选框，是否激活 LOS.Max = 1100表示设定的值比如LOS.EnableMin = True LOS.Min = 10

在这里插入图片描述

生成星座

生成星座只需要一个命令，

root.ExecuteCommand(‘Walker */Satellite/COL Type Delta NumPlanes 16 NumSatsPerPlane 10 InterPlanePhaseIncrement 1 ColorByPlane Yes’);

在‘Walker */Satellite/COL Type Delta NumPlanes 16 NumSatsPerPlane 10 InterPlanePhaseIncrement 1 ColorByPlane Yes’ 这里面中，COL是种子卫星的平面，往后的参数依次是轨道平面数、每轨道卫星数、轨道相位因子，对应STK如下在这里插入图片描述（这里为了演示能快一点就减少了卫星数量）

Python 创建覆盖网格 covdef = scenario.Children.New(AgESTKObjectType.eCoverageDefinition,'testCov') # 创建Coverage definition

这里对应STK的在这里插入图片描述设置 Converage Defination的属性

covdef.Grid.BoundsType = 6 # VAR1 ''' 1 Global 2 Latitude Bounds 3 Latitude Line 4 Longitude Line 5 Custom Boundary 6 LatLon Region ''' covdef.Grid.Resolution.LatLon = 6 # VAR2 covdef.PointDefinition.Altitude = 10 # 10 km # VAR3# 如果选择eBoundsLatLonRegion可以定义网格的覆盖区域 # covdef.Grid.BoundsType = ‘eBoundsLatLonRegion’; # covdef.Grid.Bounds.MinLongitude = -120; # covdef.Grid.Bounds.MaxLongitude = 120; # covdef.Grid.Bounds.MinLatitude = -30; # covdef.Grid.Bounds.MaxLatitude = 30;

这里分别对应在这里插入图片描述

绑定卫星的传感器要读取所有可用的对象，放入all_list由于我们只需要卫星的传感器，即放入sensor_list 里面卫星传感器在列表中是交替列出的，因此只要间隔取样就可以了 all_list = covdef.AssetList.AvailableAssets sensor_list = [] for e in range(len(all_list)):if e%2 == 0:passelse:sensor_list.append(all_list[e])

将所有传感器塞入

for j in sensor_list:covdef.AssetList.Add(j) 建立星座 sate_constellation = scenario.Children.New(AgESTKObjectType.eConstellation,'COL') for obj in tqdm(all_list):sate_constellation.Objects.Add(obj) 定义多重网格

有时候我们需要分析不同高度的覆盖性能，但是手动添加太过繁琐，一下例程演示0-300km，采样间隔10km的网格创建。并把不同网格放在一个列表里

covdef_lits = [] for i in range(0,310,10):_string = 'CovDef' + str(i)covdef = scenario.Children.New(AgESTKObjectType.eCoverageDefinition, _string)covdef.Grid.BoundsType = 6covdef.Grid.Resolution.LatLon = 6covdef.PointDefinition.Altitude = i for j in sensor_list:covdef.AssetList.Add(j)covdef_lits.append(covdef) 计算与绘图方法变量值描述eFmAccessConstraint0Access Constraint Figure of Merit.eFmAccessDuration1Access Duration Figure of Merit.eFmAccessSeparation2Access Separation Figure of Merit.eFmCoverageTime3Coverage Time Figure of Merit.eFmDilutionOfPrecision4Dilution of Precision Figure of Merit.eFmNAssetCoverage5N Asset Coverage Figure of Merit.eFmNavigationAccuracy6Navigation Accuracy Figure of Merit.eFmNumberOfAccesses7Number of Accesses Figure of Merit.eFmNumberOfGaps8Number of Gaps Figure of Merit.eFmResponseTime9Response Time Figure of Merit.eFmRevisitTime10Revisit Time Figure of Merit.eFmSimpleCoverage11Simple Coverage Figure of Merit.eFmTimeAverageGap12Time Average Gap Figure of Merit.eFmSystemResponseTime13System Response Time Figure of Merit.eFmAgeOfData14Age of Data Figure of Merit.eFmScalarCalculation15Scalar Calculation Figure of Merit.eFmSystemAgeOfData16System Age Of Data Figure of Merit. figmerit1.SetDefinitionType(1) # eFmAccessDuration covdef_tmp.ComputeAccesses(); pov = covdef_tmp.DataProviders.Item('Coverage by Latitude').Exec() # Coverage By Latitude

这里对照Reoprt Style 的属性在这里插入图片描述

data_array = pov.DataSets.ToArray() # 转换为数组

在这里插入图片描述对比STK数据完成绘图

import numpy as np from matplotlib import pyplot as plt data_array = np.array(data_array) x = [] y = [] for ele in data_array:x.append(ele[0])y.append(ele[1])passplt.plot(x,y)

在这里插入图片描述对比STK生成的图

结语

Python 有很多接口都是整形变量，不像MATLAB可以直接用字符串那么方便，需要自己找对应的变量。我一般是对照着MATLAB的例程找到一些范式，可以在网站中慢慢找 STK Help

本文所有代码我将上传至我的Github

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