/// <summary>
/// 给轨迹点添加泊松噪音
/// </summary>
/// <param name="points">轨迹点</param>
/// <param name="lambda">泊松分布中的lambda</param>
/// <param name="XMax">X坐标轴的最大值</param>
/// <param name="YMax">y坐标轴的最大值</param>
/// <returns>添加噪音后的轨迹点</returns>
public static PointD[] addPoissonNoise(PointD[] points,int XMax,int YMax)
{
PointD[] noisePoint = new PointD[points.Length];
double lambdaX = 0;
double lambdaY = 0;
foreach (PointD point in points)
{
lambdaX += point.X;
lambdaY += point.Y;
}
lambdaX /= points.Length;
lambdaY /= points.Length;
for (int i = 0; i < points.Length;i++ )
{
// MessageBox.Show(""+XMax);
noisePoint[i] = new PointD();
//防止坐标超过雷达的范围
noisePoint[i].X = ((points[i].X + GetPoisson(lambdaX)) > XMax) ? XMax : points[i].X + GetPoisson(lambdaX);
noisePoint[i].Y = ((points[i].Y + GetPoisson(lambdaY)) > YMax) ? YMax : points[i].Y + GetPoisson(lambdaY);
}
return noisePoint;
}
/// <summary>
/// 给轨迹添加高斯噪声
/// </summary>
/// <param name="points">轨迹中的点</param>
/// <param name="mean">均值</param>
/// <param name="variance">方差</param>
/// <returns>添加高斯噪音后的轨迹点</returns>
public static PointD[] addGuassianNoise(PointD[] points,double XMean,double XVariance,double YMean,double YVariance)
{
PointD[] noisePoint = new PointD[points.Length];
//添加高斯噪音，x 和 y
for (int i = 0; i < points.Length;i++ )
{
noisePoint[i] = new PointD();
double factor1 = rand.NextDouble();
double factor2 = rand.NextDouble();
//rand normal(0,1)
double randStdNormal = Math.Sqrt(-2.0*Math.Log(factor1))*Math.Sin(2.0*Math.PI*factor2);
//rand normal(mean,variance^2)
double randNormalX = XMean+XVariance*randStdNormal;
double randNormalY = YMean + YVariance * randStdNormal;
noisePoint[i].X = points[i].X + randNormalX;
noisePoint[i].Y = points[i].Y + randNormalY;
}
return noisePoint;
}
/// <summary>
/// 给轨迹点添加均匀噪音
/// </summary>
/// <param name="points">原始轨迹点</param>
/// <returns>添加噪音后的轨迹点</returns>
public static PointD[] addUniformNoise(PointD[] points,double XA,double XB,double YA,double YB)
{
PointD[] noisePoint = new PointD[points.Length];
Random r;
for (int i = 0; i < points.Length; i++)
{
r = new Random();
noisePoint[i] = new PointD();
noisePoint[i].X = points[i].X + rand.Next((int)XA,(int)XB);
noisePoint[i].Y = points[i].Y + rand.Next((int)YA,(int)YB);
}
return noisePoint;
}
public static Complex transferToComplex(PointD p)
{
return new Complex(p.X, p.Y);
}
/**
* 特性分析处理类，这里面添加特性处理的代码
* param: list 是运行轨迹的一些点； count 是特性的个数
* */
public Dictionary<String, double> getTimeAndSpaceFeature(List<PointD> list,int count)
{
Dictionary<String, double> featDic = new Dictionary<String, double>();
//计算时域空域特征分析
double[] features = new double[count];
PointD[] p1 = new PointD[list.Count];
for (int i = 0; i < list.Count; i++)
{
p1[i] = list[i];
}
double[] pX = new double[list.Count];
for (int i = 0; i < list.Count; i++)
{
pX[i] = p1[i].X;
}
//算术平均值,arithmetic mean value
for (int i = 0; i < list.Count; i++)
{
features[0] += p1[i].X;
}
features[0] /= list.Count;
featDic.Add("算术平均值", features[0]);
//几何平均值,geometric mean
features[1] = 1.0;
for (int i = 0; i < list.Count; i++)
{
if (p1[i].X != 0)
features[1] *= p1[i].X;
}
features[1] = Math.Pow(features[1], 1 / list.Count);
featDic.Add("几何平均值", features[1]);
//均方根值,root mean square value
for (int i = 0; i < list.Count; i++)
{
features[2] += Math.Pow(p1[i].X, 2);
}
features[2] /= list.Count;
features[2] = Math.Pow(features[2], 1 / 2);
featDic.Add("均方根值", features[2]);
//方差, variance
for (int i = 0; i < list.Count; i++)
{
features[3] += Math.Pow(p1[i].X - features[0], 2);
}
features[3] /= list.Count - 1;
featDic.Add("方差", features[3]);
//标准差,standard deviation
for (int i = 0; i < list.Count; i++)
{
features[4] += Math.Pow(p1[i].X - features[0], 2);
}
features[4] /= list.Count;
features[4] = Math.Pow(features[4], 1 / 2);
featDic.Add("标准差", features[4]);
//波形指标,waveform indicators
features[5] = featDic["均方根值"] / Math.Abs(featDic["算术平均值"]);
featDic.Add("波形指标", features[5]);
//峰值指标,peak index
features[6] = pX.Max() / featDic["均方根值"];
featDic["峰值指标"] = features[6];
//脉冲指标,pulse factor
features[7] = pX.Max() / Math.Abs(featDic["算术平均值"]);
featDic["脉冲指标"] = features[7];
//方根幅值,root amplitude
for (int i = 0; i < list.Count; i++)
{
features[8] += Math.Pow(Math.Abs(p1[i].X), 1 / 2);
}
features[8] = Math.Pow(features[8] / list.Count, 2);
featDic["方根幅值"] = features[8];
//裕度指标,margin indicator
features[9] = pX.Max() / featDic["方根幅值"];
featDic["裕度指标"] = features[9];
//峭度指标,Kurosis amplitude
for (int i = 0; i < list.Count; i++)
{
features[10] += Math.Pow(p1[i].X, 4);
}
features[10] /= list.Count;
features[10] = features[10] / Math.Pow(featDic["均方根值"], 4);
featDic["峭度指标"] = features[10];
//自相关函数,m=2,autocorrelation
for (int i = 0; i < (list.Count - 2); i++)
{
features[11] += p1[i].X * p1[i + 2].X;
}
featDic["自相关函数"] = features[11];
//互相关函数,cross-correlation
for (int i = 0; i < (list.Count - 2); i++)
{
features[12] += p1[i].X * p1[i + 2].Y;
}
featDic["互相关函数"] = features[12];
return featDic;
}