Ejemplo n.º 1
0
        /// <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;
        }
Ejemplo n.º 2
0
        /// <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;
        }
Ejemplo n.º 3
0
        /// <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;
        }
Ejemplo n.º 4
0
 public static Complex transferToComplex(PointD p)
 {
     return new Complex(p.X, p.Y);
 }
Ejemplo n.º 5
0
        /**
         * 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;
        }