粒子滤波 演示与opencv代码

粒子滤波的理论实在是太美妙了，用一组不同权重的随机状态来逼近复杂的概率密度函数。其再非线性、非高斯系统中具有优良的特性。opencv给出了一个实现，但是没有给出范例，学习过程中发现网络上也找不到。learning opencv一书中有介绍，但距离直接使用还是有些距离。在经过一番坎坷后，终于可以用了，希望对你有帮助。

本文中给出的例子跟 我的另一篇博文是同一个应用例子，都是对二维坐标进行平滑、预测

使用方法：

1.创建并初始化

const int stateNum=4;//状态数
const int measureNum=2;//测量变量数
const int sampleNum=2000;//粒子数

CvConDensation* condens = cvCreateConDensation(stateNum,measureNum,sampleNum);

在不影响性能的情况下，粒子数量越大，系统表现的越稳定

其他初始化内容请参考learning opencv

2.预测
3.更新例子可信度，也就是权重。本例中更新方法与learning opencv中有所不同，想看代码
4.更新CvConDensation

代码：

#include <cv.h>
#include <cxcore.h>
#include <highgui.h>
#include <cvaux.h>

#include <cmath>
#include <vector>
#include <iostream>
using namespace std;

const int winHeight=600;
const int winWidth=800;

CvPoint mousePosition=cvPoint(winWidth>>1,winHeight>>1);

//mouse event callback
void mouseEvent(int event,int x,int y,int flags,void *param )
{
if (event==CV_EVENT_MOUSEMOVE) {
mousePosition=cvPoint(x,y);
}
}

int main (void)
{
//1.condensation setup
const int stateNum=4;
const int measureNum=2;
const int sampleNum=2000;

CvConDensation* condens = cvCreateConDensation(stateNum,measureNum,sampleNum);
CvMat* lowerBound;
CvMat* upperBound;
lowerBound = cvCreateMat(stateNum, 1, CV_32F);
upperBound = cvCreateMat(stateNum, 1, CV_32F);
cvmSet(lowerBound,0,0,0.0 );
cvmSet(upperBound,0,0,winWidth );
cvmSet(lowerBound,1,0,0.0 );
cvmSet(upperBound,1,0,winHeight );
cvmSet(lowerBound,2,0,0.0 );
cvmSet(upperBound,2,0,0.0 );
cvmSet(lowerBound,3,0,0.0 );
cvmSet(upperBound,3,0,0.0 );
float A[stateNum][stateNum] ={
1,0,1,0,
0,1,0,1,
0,0,1,0,
0,0,0,1
};
memcpy(condens->DynamMatr,A,sizeof(A));
cvConDensInitSampleSet(condens, lowerBound, upperBound);

CvRNG rng_state = cvRNG(0xffffffff);
for(int i=0; i < sampleNum; i++){
condens->flSamples[i][0] = float(cvRandInt( &rng_state ) % winWidth); //width
condens->flSamples[i][1] = float(cvRandInt( &rng_state ) % winHeight);//height
}

CvFont font;
cvInitFont(&font,CV_FONT_HERSHEY_SCRIPT_COMPLEX,1,1);

char* winName="condensation";
cvNamedWindow(winName);
cvSetMouseCallback(winName,mouseEvent);
IplImage* img=cvCreateImage(cvSize(winWidth,winHeight),8,3);
bool isPredictOnly=false;//trigger for prediction only,press SPACEBAR
while (1){
//2.condensation prediction
CvPoint predict_pt=cvPoint((int)condens->State[0],(int)condens->State[1]);

float variance[measureNum]={0};
//get variance/standard deviation of each state
for (int i=0;i<measureNum;i++) {
//sum
float sumState=0;
for (int j=0;j<condens->SamplesNum;j++) {
sumState+=condens->flSamples[i][j];
}
//average
sumState/=sampleNum;
//variance
for (int j=0;j<condens->SamplesNum;j++) {
variance[i]+=(condens->flSamples[i][j]-sumState)*
(condens->flSamples[i][j]-sumState);
}
variance[i]/=sampleNum-1;
}
//3.update particals confidence
CvPoint pt;
if (isPredictOnly) {
pt=predict_pt;
}else{
pt=mousePosition;
}
for (int i=0;i<condens->SamplesNum;i++) {
float probX=(float)exp(-1*(pt.x-condens->flSamples[i][0])
*(pt.x-condens->flSamples[i][0])/(2*variance[0]));
float probY=(float)exp(-1*(pt.y-condens->flSamples[i][1])
*(pt.y-condens->flSamples[i][1])/(2*variance[1]));
condens->flConfidence[i]=probX*probY;
}
//4.update condensation
cvConDensUpdateByTime(condens);

//draw
cvSet(img,cvScalar(255,255,255,0));
cvCircle(img,predict_pt,5,CV_RGB(0,255,0),3);//predicted point with green
char buf[256];
sprintf_s(buf,256,"predicted position:(%3d,%3d)",predict_pt.x,predict_pt.y);
cvPutText(img,buf,cvPoint(10,30),&font,CV_RGB(0,0,0));
if (!isPredictOnly) {
cvCircle(img,mousePosition,5,CV_RGB(255,0,0),3);//current position with red
sprintf_s(buf,256,"real position :(%3d,%3d)",mousePosition.x,mousePosition.y);
cvPutText(img,buf,cvPoint(10,60),&font,CV_RGB(0,0,0));
}

cvShowImage(winName, img);
int key=cvWaitKey(30);
if (key==27){//esc
break;
}else if (key==' ') {//trigger for prediction
//isPredict=!isPredict;
if (isPredictOnly) {
isPredictOnly=false;
}else{
isPredictOnly=true;
}
}
}

cvReleaseImage(&img);
cvReleaseConDensation(&condens);
return 0;
}

kalman filter 视频演示：

演示中粒子数分别为100,200,2000

请仔细观测效果

http://v.youku.com/v_show/id_XMjU4MzE0ODgw.html

demo snapshot：