954 lines
32 KiB
Plaintext
954 lines
32 KiB
Plaintext
|
||
|
||
#include <iostream>
|
||
#include <memory>
|
||
#include <cmath>
|
||
#include <complex>
|
||
#include <device_launch_parameters.h>
|
||
#include <cuda_runtime.h>
|
||
#include <cublas_v2.h>
|
||
#include <cuComplex.h>
|
||
|
||
#include "BaseConstVariable.h"
|
||
#include "GPUTool.cuh"
|
||
|
||
#ifdef __CUDANVCC___
|
||
|
||
#define CUDAMEMORY Memory1MB*100
|
||
|
||
#define LAMP_CUDA_PI 3.141592653589793238462643383279
|
||
|
||
|
||
// 定义参数
|
||
__device__ cuComplex cuCexpf(cuComplex x)
|
||
{
|
||
float factor = exp(x.x);
|
||
return make_cuComplex(factor * cos(x.y), factor * sin(x.y));
|
||
}
|
||
|
||
// 定义仿真所需参数
|
||
|
||
__device__ float GPU_getSigma0dB(CUDASigmaParam param, float theta) {
|
||
float sigma= param.p1 + param.p2 * exp(-param.p3 * theta) + param.p4 * cos(param.p5 * theta + param.p6);
|
||
return sigma;
|
||
}
|
||
|
||
__device__ CUDAVector GPU_VectorAB(CUDAVector A, CUDAVector B) {
|
||
CUDAVector C;
|
||
C.x = B.x - A.x;
|
||
C.y = B.y - A.y;
|
||
C.z = B.z - A.z;
|
||
return C;
|
||
}
|
||
|
||
__device__ float GPU_VectorNorm2(CUDAVector A) {
|
||
return sqrtf(A.x * A.x + A.y * A.y + A.z * A.z);
|
||
}
|
||
|
||
__device__ float GPU_dotVector(CUDAVector A, CUDAVector B) {
|
||
return A.x * B.x + A.y * B.y + A.z * B.z;
|
||
}
|
||
|
||
__device__ float GPU_CosAngle_VectorA_VectorB(CUDAVector A, CUDAVector B) {
|
||
return GPU_dotVector(A, B) / (GPU_VectorNorm2(A) * GPU_VectorNorm2(B));
|
||
}
|
||
|
||
__device__ CUDAVectorEllipsoidal GPU_SatelliteAntDirectNormal(float RstX, float RstY, float RstZ,
|
||
float antXaxisX, float antXaxisY, float antXaxisZ,
|
||
float antYaxisX, float antYaxisY, float antYaxisZ,
|
||
float antZaxisX, float antZaxisY, float antZaxisZ,
|
||
float antDirectX, float antDirectY, float antDirectZ
|
||
) {
|
||
CUDAVectorEllipsoidal result{ 0,0,-1 };
|
||
float Xst = -1 * RstX; // 卫星 --> 地面
|
||
float Yst = -1 * RstY;
|
||
float Zst = -1 * RstZ;
|
||
float AntXaxisX = antXaxisX;
|
||
float AntXaxisY = antXaxisY;
|
||
float AntXaxisZ = antXaxisZ;
|
||
float AntYaxisX = antYaxisX;
|
||
float AntYaxisY = antYaxisY;
|
||
float AntYaxisZ = antYaxisZ;
|
||
float AntZaxisX = antZaxisX;
|
||
float AntZaxisY = antZaxisY;
|
||
float AntZaxisZ = antZaxisZ;
|
||
// 天线指向在天线坐标系下的值
|
||
float Xant = (Xst * (AntYaxisY * AntZaxisZ - AntYaxisZ * AntZaxisY) + Xst * (AntXaxisZ * AntZaxisY - AntXaxisY * AntZaxisZ) + Xst * (AntXaxisY * AntYaxisZ - AntXaxisZ * AntYaxisY)) / (AntXaxisX * (AntYaxisY * AntZaxisZ - AntZaxisY * AntYaxisZ) - AntYaxisX * (AntXaxisY * AntZaxisZ - AntXaxisZ * AntZaxisY) + AntZaxisX * (AntXaxisY * AntYaxisZ - AntXaxisZ * AntYaxisY));
|
||
float Yant = (Yst * (AntYaxisZ * AntZaxisX - AntYaxisX * AntZaxisZ) + Yst * (AntXaxisX * AntZaxisZ - AntXaxisZ * AntZaxisX) + Yst * (AntYaxisX * AntXaxisZ - AntXaxisX * AntYaxisZ)) / (AntXaxisX * (AntYaxisY * AntZaxisZ - AntZaxisY * AntYaxisZ) - AntYaxisX * (AntXaxisY * AntZaxisZ - AntXaxisZ * AntZaxisY) + AntZaxisX * (AntXaxisY * AntYaxisZ - AntXaxisZ * AntYaxisY));
|
||
float Zant = (Zst * (AntYaxisX * AntZaxisY - AntYaxisY * AntZaxisX) + Zst * (AntXaxisY * AntZaxisX - AntXaxisX * AntZaxisY) + Zst * (AntXaxisX * AntYaxisY - AntYaxisX * AntXaxisY)) / (AntXaxisX * (AntYaxisY * AntZaxisZ - AntZaxisY * AntYaxisZ) - AntYaxisX * (AntXaxisY * AntZaxisZ - AntXaxisZ * AntZaxisY) + AntZaxisX * (AntXaxisY * AntYaxisZ - AntXaxisZ * AntYaxisY));
|
||
// 计算theta 与 phi
|
||
float Norm = sqrtf(Xant * Xant + Yant * Yant + Zant * Zant); // 计算 pho
|
||
float ThetaAnt = acosf(Zant / Norm); // theta 与 Z轴的夹角
|
||
float YsinTheta = Yant / sinf(ThetaAnt);
|
||
float PhiAnt = (YsinTheta / abs(YsinTheta)) * acosf(Xant / (Norm * sinf(ThetaAnt)));
|
||
result.theta = ThetaAnt;
|
||
result.phi = PhiAnt;
|
||
result.pho = Norm;
|
||
return result;
|
||
}
|
||
|
||
/**
|
||
天线方向图插值方法，以双线性插值算法为基础，由theta与phi组合得到的矩阵图为基础数据，通过插值计算的方法获取目标点的数据。
|
||
其中行是theta、列是phi
|
||
*/
|
||
__device__ float GPU_BillerInterpAntPattern(float* antpattern,
|
||
float starttheta, float startphi, float dtheta, float dphi,
|
||
long thetapoints, long phipoints,
|
||
float searththeta, float searchphi) {
|
||
float stheta = searththeta;
|
||
float sphi = searchphi;
|
||
if (stheta > 90) {
|
||
return 0;
|
||
}
|
||
else {}
|
||
|
||
|
||
float pthetaid = (stheta - starttheta) / dtheta;//
|
||
float pphiid = (sphi - startphi) / dphi;
|
||
|
||
long lasttheta = floorf(pthetaid);
|
||
long nextTheta = lasttheta + 1;
|
||
long lastphi = floorf(pphiid);
|
||
long nextPhi = lastphi + 1;
|
||
|
||
|
||
if (lasttheta < 0 || nextTheta < 0 || lastphi < 0 || nextPhi < 0 ||
|
||
lasttheta >= thetapoints || nextTheta >= thetapoints || lastphi >= phipoints || nextPhi >= phipoints)
|
||
{
|
||
return 0;
|
||
}
|
||
else {
|
||
float x = stheta;
|
||
float y = sphi;
|
||
|
||
float x1 = lasttheta * dtheta + starttheta;
|
||
float x2 = nextTheta * dtheta + starttheta;
|
||
float y1 = lastphi * dphi + startphi;
|
||
float y2 = nextPhi * dphi + startphi;
|
||
|
||
float z11 = antpattern[lasttheta * phipoints + lastphi];
|
||
float z12 = antpattern[lasttheta * phipoints + nextPhi];
|
||
float z21 = antpattern[nextTheta * phipoints + lastphi];
|
||
float z22 = antpattern[nextTheta * phipoints + nextPhi];
|
||
|
||
|
||
//z11 = powf(10, z11 / 10); // dB-> 线性
|
||
//z12 = powf(10, z12 / 10);
|
||
//z21 = powf(10, z21 / 10);
|
||
//z22 = powf(10, z22 / 10);
|
||
|
||
float GainValue = (z11 * (x2 - x) * (y2 - y)
|
||
+ z21 * (x - x1) * (y2 - y)
|
||
+ z12 * (x2 - x) * (y - y1)
|
||
+ z22 * (x - x1) * (y - y1));
|
||
GainValue = GainValue / ((x2 - x1) * (y2 - y1));
|
||
return GainValue;
|
||
}
|
||
}
|
||
|
||
__device__ cuComplex GPU_calculationEcho(float sigma0, float TransAnt, float ReciveAnt,
|
||
float localangle, float R, float slopeangle, float Pt, float lamda) {
|
||
float r = R;
|
||
float amp = Pt * TransAnt * ReciveAnt;
|
||
amp = amp * sigma0;
|
||
amp = amp / (powf(4 * LAMP_CUDA_PI, 2) * powf(r, 4)); // 反射强度
|
||
float phi = (-4 * LAMP_CUDA_PI / lamda) * r;
|
||
cuComplex echophi = make_cuComplex(0, phi);
|
||
cuComplex echophiexp = cuCexpf(echophi);
|
||
cuComplex echo;
|
||
echo.x = echophiexp.x * amp;
|
||
echo.y = echophiexp.y * amp;
|
||
return echo;
|
||
}
|
||
|
||
__global__ void CUDA_DistanceAB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz, float* R, long len) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (idx < len) {
|
||
R[idx] = sqrtf(powf(Ax[idx] - Bx[idx], 2) + powf(Ay[idx] - By[idx], 2) + powf(Az[idx] - Bz[idx], 2));
|
||
}
|
||
}
|
||
|
||
__global__ void CUDA_B_DistanceA(float* Ax, float* Ay, float* Az, float Bx, float By, float Bz, float* R, long len) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (idx < len) {
|
||
R[idx] = sqrtf(powf(Ax[idx] - Bx, 2) + powf(Ay[idx] - By, 2) + powf(Az[idx] - Bz, 2));
|
||
}
|
||
}
|
||
|
||
__global__ void CUDA_make_VectorA_B(float sX, float sY, float sZ, float* tX, float* tY, float* tZ, float* RstX, float* RstY, float* RstZ, long len) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (idx < len) {
|
||
RstX[idx] = sX - tX[idx]; // 地面->天
|
||
RstY[idx] = sY - tY[idx];
|
||
RstZ[idx] = sZ - tZ[idx];
|
||
}
|
||
}
|
||
|
||
__global__ void CUDA_Norm_Vector(float* Vx, float* Vy, float* Vz, float* R, long len) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (idx < len) {
|
||
R[idx] = sqrtf(powf(Vx[idx], 2) + powf(Vy[idx], 2) + powf(Vz[idx], 2));
|
||
}
|
||
}
|
||
|
||
__global__ void CUDA_cosAngle_VA_AB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz, float* anglecos, long len) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (idx < len) {
|
||
float tAx = Ax[idx];
|
||
float tAy = Ay[idx];
|
||
float tAz = Az[idx];
|
||
float tBx = Bx[idx];
|
||
float tBy = By[idx];
|
||
float tBz = Bz[idx];
|
||
float AR = sqrtf(powf(tAx, 2) + powf(tAy, 2) + powf(tAz, 2));
|
||
float BR = sqrtf(powf(tBx, 2) + powf(tBy, 2) + powf(tBz, 2));
|
||
float dotAB = tAx * tBx + tAy * tBy + tAz * tBz;
|
||
float result = acosf(dotAB / (AR * BR));
|
||
anglecos[idx] = result;
|
||
}
|
||
}
|
||
|
||
__global__ void CUDA_SatelliteAntDirectNormal(float* RstX, float* RstY, float* RstZ,
|
||
float antXaxisX, float antXaxisY, float antXaxisZ,
|
||
float antYaxisX, float antYaxisY, float antYaxisZ,
|
||
float antZaxisX, float antZaxisY, float antZaxisZ,
|
||
float antDirectX, float antDirectY, float antDirectZ,
|
||
float* thetaAnt, float* phiAnt
|
||
, long len) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (idx < len) {
|
||
float Xst = -1 * RstX[idx]; // 卫星 --> 地面
|
||
float Yst = -1 * RstY[idx];
|
||
float Zst = -1 * RstZ[idx];
|
||
float AntXaxisX = antXaxisX;
|
||
float AntXaxisY = antXaxisY;
|
||
float AntXaxisZ = antXaxisZ;
|
||
float AntYaxisX = antYaxisX;
|
||
float AntYaxisY = antYaxisY;
|
||
float AntYaxisZ = antYaxisZ;
|
||
float AntZaxisX = antZaxisX;
|
||
float AntZaxisY = antZaxisY;
|
||
float AntZaxisZ = antZaxisZ;
|
||
|
||
// 归一化
|
||
float RstNorm = sqrtf(Xst * Xst + Yst * Yst + Zst * Zst);
|
||
float AntXaxisNorm = sqrtf(AntXaxisX * AntXaxisX + AntXaxisY * AntXaxisY + AntXaxisZ * AntXaxisZ);
|
||
float AntYaxisNorm = sqrtf(AntYaxisX * AntYaxisX + AntYaxisY * AntYaxisY + AntYaxisZ * AntYaxisZ);
|
||
float AntZaxisNorm = sqrtf(AntZaxisX * AntZaxisX + AntZaxisY * AntZaxisY + AntZaxisZ * AntZaxisZ);
|
||
|
||
|
||
float Rx = Xst / RstNorm;
|
||
float Ry = Yst / RstNorm;
|
||
float Rz = Zst / RstNorm;
|
||
float Xx = AntXaxisX / AntXaxisNorm;
|
||
float Xy = AntXaxisY / AntXaxisNorm;
|
||
float Xz = AntXaxisZ / AntXaxisNorm;
|
||
float Yx = AntYaxisX / AntYaxisNorm;
|
||
float Yy = AntYaxisY / AntYaxisNorm;
|
||
float Yz = AntYaxisZ / AntYaxisNorm;
|
||
float Zx = AntZaxisX / AntZaxisNorm;
|
||
float Zy = AntZaxisY / AntZaxisNorm;
|
||
float Zz = AntZaxisZ / AntZaxisNorm;
|
||
|
||
float Xant = (Rx * Yy * Zz - Rx * Yz * Zy - Ry * Yx * Zz + Ry * Yz * Zx + Rz * Yx * Zy - Rz * Yy * Zx) / (Xx * Yy * Zz - Xx * Yz * Zy - Xy * Yx * Zz + Xy * Yz * Zx + Xz * Yx * Zy - Xz * Yy * Zx);
|
||
float Yant = -(Rx * Xy * Zz - Rx * Xz * Zy - Ry * Xx * Zz + Ry * Xz * Zx + Rz * Xx * Zy - Rz * Xy * Zx) / (Xx * Yy * Zz - Xx * Yz * Zy - Xy * Yx * Zz + Xy * Yz * Zx + Xz * Yx * Zy - Xz * Yy * Zx);
|
||
float Zant = (Rx * Xy * Yz - Rx * Xz * Yy - Ry * Xx * Yz + Ry * Xz * Yx + Rz * Xx * Yy - Rz * Xy * Yx) / (Xx * Yy * Zz - Xx * Yz * Zy - Xy * Yx * Zz + Xy * Yz * Zx + Xz * Yx * Zy - Xz * Yy * Zx);
|
||
|
||
|
||
// 计算theta 与 phi
|
||
float Norm = sqrtf(Xant * Xant + Yant * Yant + Zant * Zant); // 计算 pho
|
||
float ThetaAnt = acosf(Zant / Norm); // theta 与 Z轴的夹角
|
||
float PhiAnt = atanf(Yant / Xant); // -pi/2 ~pi/2
|
||
|
||
|
||
if (abs(Yant) < PRECISIONTOLERANCE) { // X轴上
|
||
PhiAnt = 0;
|
||
}
|
||
else if (abs(Xant) < PRECISIONTOLERANCE) { // Y轴上，原点
|
||
if (Yant > 0) {
|
||
PhiAnt = PI / 2;
|
||
}
|
||
else {
|
||
PhiAnt = -PI / 2;
|
||
}
|
||
}
|
||
else if (Xant < 0) {
|
||
if (Yant > 0) {
|
||
PhiAnt = PI + PhiAnt;
|
||
}
|
||
else {
|
||
PhiAnt = -PI+PhiAnt ;
|
||
}
|
||
}
|
||
else { // Xant>0 X 正轴
|
||
|
||
}
|
||
|
||
if (isnan(PhiAnt)) {
|
||
printf("V=[%f,%f,%f];norm=%f;thetaAnt=%f;phiAnt=%f;\n", Xant, Yant, Zant,Norm, ThetaAnt, PhiAnt);
|
||
}
|
||
|
||
//if (abs(ThetaAnt - 0) < PRECISIONTOLERANCE) {
|
||
// PhiAnt = 0;
|
||
//}
|
||
//else {}
|
||
|
||
|
||
thetaAnt[idx] = ThetaAnt*r2d;
|
||
phiAnt[idx] = PhiAnt*r2d;
|
||
//printf("Rst=[%f,%f,%f];AntXaxis = [%f, %f, %f];AntYaxis=[%f,%f,%f];AntZaxis=[%f,%f,%f];phiAnt=%f;thetaAnt=%f;\n", Xst, Yst, Zst
|
||
// , AntXaxisX, AntXaxisY, AntXaxisZ
|
||
// , AntYaxisX, AntYaxisY, AntYaxisZ
|
||
// , AntZaxisX, AntZaxisY, AntZaxisZ
|
||
// , phiAnt[idx]
|
||
// , thetaAnt[idx]
|
||
//);
|
||
}
|
||
}
|
||
|
||
__global__ void CUDA_BillerInterpAntPattern(float* antpattern,
|
||
float starttheta, float startphi, float dtheta, float dphi,
|
||
long thetapoints, long phipoints,
|
||
float* searththeta, float* searchphi, float* searchantpattern,
|
||
long len) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (idx < len) {
|
||
float stheta = searththeta[idx];
|
||
float sphi = searchphi[idx];
|
||
float pthetaid = (stheta - starttheta) / dtheta;//
|
||
float pphiid = (sphi - startphi) / dphi;
|
||
|
||
long lasttheta = floorf(pthetaid);
|
||
long nextTheta = lasttheta + 1;
|
||
long lastphi = floorf(pphiid);
|
||
long nextPhi = lastphi + 1;
|
||
|
||
if (lasttheta < 0 || nextTheta < 0 || lastphi < 0 || nextPhi < 0 ||
|
||
lasttheta >= thetapoints || nextTheta >= thetapoints || lastphi >= phipoints || nextPhi >= phipoints)
|
||
{
|
||
searchantpattern[idx] = 0;
|
||
}
|
||
else {
|
||
float x = stheta;
|
||
float y = sphi;
|
||
|
||
float x1 = lasttheta * dtheta + starttheta;
|
||
float x2 = nextTheta * dtheta + starttheta;
|
||
float y1 = lastphi * dphi + startphi;
|
||
float y2 = nextPhi * dphi + startphi;
|
||
|
||
float z11 = antpattern[lasttheta * phipoints + lastphi];
|
||
float z12 = antpattern[lasttheta * phipoints + nextPhi];
|
||
float z21 = antpattern[nextTheta * phipoints + lastphi];
|
||
float z22 = antpattern[nextTheta * phipoints + nextPhi];
|
||
|
||
|
||
z11 = powf(10, z11 / 10);
|
||
z12 = powf(10, z12 / 10);
|
||
z21 = powf(10, z21 / 10);
|
||
z22 = powf(10, z22 / 10);
|
||
|
||
float GainValue = (z11 * (x2 - x) * (y2 - y)
|
||
+ z21 * (x - x1) * (y2 - y)
|
||
+ z12 * (x2 - x) * (y - y1)
|
||
+ z22 * (x - x1) * (y - y1));
|
||
GainValue = GainValue / ((x2 - x1) * (y2 - y1));
|
||
searchantpattern[idx] = GainValue;
|
||
}
|
||
}
|
||
}
|
||
|
||
|
||
__global__ void CUDA_Test_HelloWorld(float a, long len) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
printf("\nidx:\t %d %d \n", idx, len);
|
||
}
|
||
|
||
|
||
__global__ void CUDA_RTPC(
|
||
float antPx, float antPy, float antPz,// 天线坐标
|
||
float antXaxisX, float antXaxisY, float antXaxisZ,
|
||
float antYaxisX, float antYaxisY, float antYaxisZ,
|
||
float antZaxisX, float antZaxisY, float antZaxisZ,
|
||
float antDirectX, float antDirectY, float antDirectZ,
|
||
float* demx, float* demy, float* demz, long* demcls,
|
||
float* demslopex, float* demslopey, float* demslopez, float* demslopeangle,
|
||
float* Tantpattern, float Tstarttheta, float Tstartphi, float Tdtheta, float Tdphi, long Tthetapoints, long Tphipoints,
|
||
float* Rantpattern, float Rstarttheta, float Rstartphi, float Rdtheta, float Rdphi, long Rthetapoints, long Rphipoints,
|
||
float lamda, float fs, float nearrange, float Pt, long Freqnumbers, // 参数
|
||
CUDASigmaParam* sigma0Paramslist, long sigmaparamslistlen,// 地表覆盖类型-sigma插值对应函数-ulaby
|
||
cuComplex* outecho, int* d_echoAmpFID,
|
||
int linecount,int plusepoint) {
|
||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
//printf("\nidx:\t %d %d %d\n", idx, linecount, plusepoint);
|
||
if (idx < linecount* plusepoint) {
|
||
long clsid = demcls[idx];
|
||
CUDAVector Rs{ antPx,antPy,antPz };
|
||
CUDAVector Rt{ demx[idx],demy[idx],demz[idx] };
|
||
CUDAVector Rst{ Rs.x - Rt.x,Rs.y - Rt.y,Rs.z - Rt.z };
|
||
CUDAVector Vslope{ demslopex[idx],demslopey[idx],demslopez[idx] };
|
||
float R = GPU_VectorNorm2(Rst); // 斜距
|
||
float slopeangle = demslopeangle[idx];
|
||
CUDAVectorEllipsoidal Rtanttheta = GPU_SatelliteAntDirectNormal( // 地面目标在天线的位置
|
||
Rst.x, Rst.y, Rst.z,
|
||
antXaxisX, antXaxisY, antXaxisZ,
|
||
antYaxisX, antYaxisY, antYaxisZ,
|
||
antZaxisX, antZaxisY, antZaxisZ,
|
||
antDirectX, antDirectY, antDirectZ);
|
||
|
||
float localangle = GPU_CosAngle_VectorA_VectorB(Rst, Vslope); // 距地入射角
|
||
float sigma = GPU_getSigma0dB(sigma0Paramslist[clsid], localangle * r2d);
|
||
sigma = powf(10.0, sigma / 10.0);// 后向散射系数
|
||
//printf("\ntheta: %f\t,%f ,%f ,%f ,%f ,%f ,%f \n", localangle * r2d, sigma0Paramslist[clsid].p1, sigma0Paramslist[clsid].p2, sigma0Paramslist[clsid].p3,
|
||
// sigma0Paramslist[clsid].p4, sigma0Paramslist[clsid].p5, sigma0Paramslist[clsid].p6);
|
||
// 发射方向图
|
||
float transPattern = GPU_BillerInterpAntPattern(Tantpattern,
|
||
Tstarttheta, Tstartphi, Tdtheta, Tdphi, Tthetapoints, Tphipoints,
|
||
Rtanttheta.theta, Rtanttheta.phi) * r2d;
|
||
|
||
// 接收方向图
|
||
float receivePattern = GPU_BillerInterpAntPattern(Rantpattern,
|
||
Rstarttheta, Rstartphi, Rdtheta, Rdphi, Rthetapoints, Rphipoints,
|
||
Rtanttheta.theta, Rtanttheta.phi) * r2d;
|
||
// 计算振幅、相位
|
||
float amp = Pt * transPattern * receivePattern * sigma * (1 / cos(slopeangle) * sin(localangle));
|
||
amp = amp / (powf(4 * LAMP_CUDA_PI, 2) * powf(R, 4));
|
||
float phi = (-4 * LAMP_CUDA_PI / lamda) * R;
|
||
|
||
// 构建回波
|
||
cuComplex echophi = make_cuComplex(0, phi);
|
||
cuComplex echophiexp = cuCexpf(echophi);
|
||
float timeR = 2 * (R - nearrange) / LIGHTSPEED * fs;
|
||
long timeID = floorf(timeR);
|
||
if (timeID < 0 || timeID >= Freqnumbers) {
|
||
timeID = 0;
|
||
amp = 0;
|
||
}
|
||
else {}
|
||
|
||
cuComplex echo;
|
||
echo.x = echophiexp.x * amp;
|
||
echo.y = echophiexp.y * amp;
|
||
outecho[idx] = echo;
|
||
d_echoAmpFID[idx] = timeID;
|
||
}
|
||
|
||
|
||
}
|
||
|
||
|
||
__global__ void CUDA_TBPImage(
|
||
float* antPx, float* antPy, float* antPz,
|
||
float* imgx, float* imgy, float* imgz,
|
||
cuComplex* echoArr, cuComplex* imgArr,
|
||
float freq, float fs, float Rnear, float Rfar,
|
||
long rowcount, long colcount,
|
||
long prfid, long freqcount
|
||
) {
|
||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
//printf("\nidx:\t %d %d %d\n", idx, linecount, plusepoint);
|
||
if (idx < rowcount * colcount) {
|
||
float R = sqrtf(powf(antPx[prfid] - imgx[idx], 2) + powf(antPy[prfid] - imgy[idx], 2) + powf(antPz[prfid] - imgz[idx], 2));
|
||
float Ridf = ((R - Rnear) * 2 / LIGHTSPEED) * fs;
|
||
long Rid = floorf(Ridf);
|
||
if(Rid <0|| Rid >= freqcount){}
|
||
else {
|
||
float factorj = freq * 4 * PI / LIGHTSPEED;
|
||
cuComplex Rphi =cuCexpf(make_cuComplex(0, factorj * R));// 校正项
|
||
imgArr[idx] = cuCaddf(imgArr[idx], cuCmulf(echoArr[Rid] , Rphi));// 矫正
|
||
}
|
||
}
|
||
}
|
||
|
||
|
||
__global__ void CUDA_calculationEcho(float* sigma0, float* TransAnt, float* ReciveAnt,
|
||
float* localangle, float* R, float* slopeangle,
|
||
float nearRange, float Fs, float Pt, float lamda, long FreqIDmax,
|
||
cuComplex* echoArr, long* FreqID,
|
||
long len) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (idx < len) {
|
||
float r = R[idx];
|
||
float amp = Pt * TransAnt[idx] * ReciveAnt[idx];
|
||
amp = amp * sigma0[idx];
|
||
amp = amp / (powf(4 * LAMP_CUDA_PI, 2) * powf(r, 4)); // 反射强度
|
||
|
||
// 处理相位
|
||
float phi = (-4 * LAMP_CUDA_PI / lamda) * r;
|
||
cuComplex echophi = make_cuComplex(0, phi);
|
||
cuComplex echophiexp = cuCexpf(echophi);
|
||
|
||
float timeR = 2 * (r - nearRange) / LIGHTSPEED * Fs;
|
||
long timeID = floorf(timeR);
|
||
if (timeID < 0 || timeID >= FreqIDmax) {
|
||
timeID = 0;
|
||
amp = 0;
|
||
}
|
||
|
||
cuComplex echo;
|
||
echo.x = echophiexp.x * amp;
|
||
echo.y = echophiexp.y * amp;
|
||
echoArr[idx] = echo;
|
||
FreqID[idx] = timeID;
|
||
}
|
||
}
|
||
|
||
|
||
__global__ void CUDA_AntPatternInterpGain(float* anttheta, float* antphi, float* gain,
|
||
float* antpattern, float starttheta, float startphi, float dtheta, float dphi, int thetapoints, int phipoints, long len) {
|
||
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
|
||
if (idx < len) {
|
||
|
||
float temptheta = anttheta[idx];
|
||
float tempphi = antphi[idx];
|
||
|
||
|
||
float antPatternGain = GPU_BillerInterpAntPattern(antpattern,
|
||
starttheta, startphi, dtheta, dphi, thetapoints, phipoints,
|
||
temptheta, tempphi) ;
|
||
gain[idx] = antPatternGain;
|
||
}
|
||
}
|
||
|
||
//__global__ void Sigma0InterpPixel(long* demcls, float* demslopeangle, CUDASigmaParam* sigma0Paramslist, float* localangle, float* sigma0list, long sigmaparamslistlen, long len)
|
||
//{
|
||
// long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
// if (idx < len) {
|
||
// long clsid = demcls[idx];
|
||
// if(clsid<=)
|
||
// sigma0list[idx] = 0;
|
||
// }
|
||
//}
|
||
|
||
|
||
__global__ void CUDA_InterpSigma(
|
||
long* demcls, float* sigmaAmp, float* localanglearr, long len,
|
||
CUDASigmaParam* sigma0Paramslist, long sigmaparamslistlen) {
|
||
long idx = blockIdx.x * blockDim.x + threadIdx.x;
|
||
if (idx < len) {
|
||
long clsid = demcls[idx];
|
||
float localangle = localanglearr[idx] * r2d;
|
||
CUDASigmaParam tempsigma = sigma0Paramslist[clsid];
|
||
//printf("cls:%d;localangle=%f;\n",clsid, localangle);
|
||
|
||
if (localangle < 0 || localangle >= 90) {
|
||
sigmaAmp[idx] = 0;
|
||
}
|
||
else {}
|
||
|
||
if (abs(tempsigma.p1)< PRECISIONTOLERANCE&&
|
||
abs(tempsigma.p2) < PRECISIONTOLERANCE &&
|
||
abs(tempsigma.p3) < PRECISIONTOLERANCE &&
|
||
abs(tempsigma.p4) < PRECISIONTOLERANCE&&
|
||
abs(tempsigma.p5) < PRECISIONTOLERANCE&&
|
||
abs(tempsigma.p6) < PRECISIONTOLERANCE
|
||
) {
|
||
sigmaAmp[idx] = 0;
|
||
}
|
||
else {
|
||
float sigma = GPU_getSigma0dB(tempsigma, localangle);
|
||
sigma = powf(10.0, sigma / 10.0);// 后向散射系数
|
||
//printf("cls:%d;localangle=%f;sigma0=%f;\n", clsid, localangle, sigma);
|
||
sigmaAmp[idx] = sigma;
|
||
}
|
||
}
|
||
}
|
||
|
||
|
||
|
||
|
||
//错误提示
|
||
void checkCudaError(cudaError_t err, const char* msg) {
|
||
if (err != cudaSuccess) {
|
||
std::cerr << "CUDA error: " << msg << " (" << cudaGetErrorString(err) << ")" << std::endl;
|
||
exit(EXIT_FAILURE);
|
||
}
|
||
|
||
}
|
||
|
||
// 主机参数内存声明
|
||
extern "C" void* mallocCUDAHost(long memsize) {
|
||
void* ptr;
|
||
cudaMallocHost(&ptr, memsize);
|
||
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("mallocCUDAHost CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
return ptr;
|
||
}
|
||
|
||
// 主机参数内存释放
|
||
extern "C" void FreeCUDAHost(void* ptr) {
|
||
cudaFreeHost(ptr);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("FreeCUDAHost CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
// GPU参数内存声明
|
||
extern "C" void* mallocCUDADevice(long memsize) {
|
||
void* ptr;
|
||
cudaMalloc(&ptr, memsize);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("mallocCUDADevice CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
return ptr;
|
||
}
|
||
|
||
// GPU参数内存释放
|
||
extern "C" void FreeCUDADevice(void* ptr) {
|
||
cudaFree(ptr);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("FreeCUDADevice CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
// GPU 内存数据转移
|
||
extern "C" void HostToDevice(void* hostptr, void* deviceptr, long memsize) {
|
||
cudaMemcpy(deviceptr, hostptr, memsize, cudaMemcpyHostToDevice);
|
||
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("HostToDevice CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
extern "C" void DeviceToHost(void* hostptr, void* deviceptr, long memsize) {
|
||
cudaMemcpy(hostptr, deviceptr, memsize, cudaMemcpyDeviceToHost);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("DeviceToHost CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
extern "C" void CUDATestHelloWorld(float a,long len) {
|
||
// 设置 CUDA 核函数的网格和块的尺寸
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
// 调用 CUDA 核函数
|
||
CUDA_Test_HelloWorld << <numBlocks, blockSize >> > (a, len);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("FreeCUDADevice CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
void CUDATBPImage(float* antPx, float* antPy, float* antPz,
|
||
float* imgx, float* imgy, float* imgz,
|
||
cuComplex* echoArr, cuComplex* imgArr,
|
||
float freq, float fs, float Rnear, float Rfar,
|
||
long rowcount, long colcount,
|
||
long prfid, long freqcount)
|
||
{
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (rowcount * colcount + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
//printf("\nCUDA_RTPC_SiglePRF blockSize:%d ,numBlock:%d\n",blockSize,numBlocks);
|
||
// 调用 CUDA 核函数 CUDA_RTPC_Kernel
|
||
|
||
CUDA_TBPImage << <numBlocks, blockSize >> > (
|
||
antPx, antPy, antPz,
|
||
imgx, imgy, imgz,
|
||
echoArr, imgArr,
|
||
freq, fs, Rnear, Rfar,
|
||
rowcount, colcount,
|
||
prfid, freqcount
|
||
);
|
||
|
||
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDATBPImage CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
extern "C" void distanceAB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz, float* R, long len) {
|
||
// 设置 CUDA 核函数的网格和块的尺寸
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
// 调用 CUDA 核函数
|
||
CUDA_DistanceAB << <numBlocks, blockSize >> > (Ax, Ay, Az, Bx, By, Bz, R, len);
|
||
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
extern "C" void BdistanceAs(float* Ax, float* Ay, float* Az, float Bx, float By, float Bz, float* R, long len) {
|
||
// 设置 CUDA 核函数的网格和块的尺寸
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
// 调用 CUDA 核函数
|
||
CUDA_B_DistanceA << <numBlocks, blockSize >> > (Ax, Ay, Az, Bx, By, Bz, R, len);
|
||
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
extern "C" void make_VectorA_B(float sX, float sY, float sZ, float* tX, float* tY, float* tZ, float* RstX, float* RstY, float* RstZ, long len) {
|
||
// 设置 CUDA 核函数的网格和块的尺寸
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
// 调用 CUDA 核函数
|
||
CUDA_make_VectorA_B << <numBlocks, blockSize >> > (sX, sY, sZ, tX, tY, tZ, RstX, RstY, RstZ, len);
|
||
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
extern "C" void Norm_Vector(float* Vx, float* Vy, float* Vz, float* R, long len) {
|
||
// 设置 CUDA 核函数的网格和块的尺寸
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
// 调用 CUDA 核函数
|
||
CUDA_Norm_Vector << <numBlocks, blockSize >> > (Vx, Vy, Vz, R, len);
|
||
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
extern "C" void cosAngle_VA_AB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz, float* anglecos, long len) {
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
// 调用 CUDA 核函数
|
||
CUDA_cosAngle_VA_AB << <numBlocks, blockSize >> > (Ax, Ay, Az, Bx, By, Bz, anglecos, len);
|
||
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
extern "C" void SatelliteAntDirectNormal(float* RstX, float* RstY, float* RstZ,
|
||
float antXaxisX, float antXaxisY, float antXaxisZ,
|
||
float antYaxisX, float antYaxisY, float antYaxisZ,
|
||
float antZaxisX, float antZaxisY, float antZaxisZ,
|
||
float antDirectX, float antDirectY, float antDirectZ,
|
||
float* thetaAnt, float* phiAnt
|
||
, long len) {
|
||
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
// 调用 CUDA 核函数
|
||
CUDA_SatelliteAntDirectNormal << <numBlocks, blockSize >> > (RstX, RstY, RstZ,
|
||
antXaxisX, antXaxisY, antXaxisZ,
|
||
antYaxisX, antYaxisY, antYaxisZ,
|
||
antZaxisX, antZaxisY, antZaxisZ,
|
||
antDirectX, antDirectY, antDirectZ,
|
||
thetaAnt, phiAnt
|
||
, len);
|
||
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
|
||
}
|
||
|
||
extern "C" void AntPatternInterpGain(float* anttheta, float* antphi, float* gain,
|
||
float* antpattern, float starttheta, float startphi, float dtheta, float dphi, int thetapoints, int phipoints, long len) {
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
//printf("\nCUDA_RTPC_SiglePRF blockSize:%d ,numBlock:%d\n", blockSize, numBlocks);
|
||
|
||
CUDA_AntPatternInterpGain << <numBlocks, blockSize >> > ( anttheta,antphi, gain,
|
||
antpattern,
|
||
starttheta, startphi, dtheta, dphi, thetapoints, phipoints,
|
||
len);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
|
||
extern "C" void CUDARTPCPRF(float antPx, long len) {
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
printf("\nCUDA_RTPC_SiglePRF blockSize:%d ,numBlock:%d\n", blockSize, numBlocks);
|
||
CUDA_Test_HelloWorld << <numBlocks, blockSize >> > (antPx, len);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
|
||
extern "C" void calculationEcho(float* sigma0, float* TransAnt, float* ReciveAnt,
|
||
float* localangle, float* R, float* slopeangle,
|
||
float nearRange, float Fs, float pt, float lamda, long FreqIDmax,
|
||
cuComplex* echoAmp, long* FreqID,
|
||
long len)
|
||
{
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
// 调用 CUDA 核函数
|
||
CUDA_calculationEcho << <numBlocks, blockSize >> > (sigma0, TransAnt, ReciveAnt,
|
||
localangle, R, slopeangle,
|
||
nearRange, Fs, pt, lamda, FreqIDmax,
|
||
echoAmp, FreqID,
|
||
len);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
extern "C" void CUDA_RTPC_SiglePRF(
|
||
float antPx, float antPy, float antPZ,
|
||
float antXaxisX, float antXaxisY, float antXaxisZ,
|
||
float antYaxisX, float antYaxisY, float antYaxisZ,
|
||
float antZaxisX, float antZaxisY, float antZaxisZ,
|
||
float antDirectX, float antDirectY, float antDirectZ,
|
||
float* demx, float* demy, float* demz, long* demcls,
|
||
float* demslopex, float* demslopey, float* demslopez, float* demslopeangle,
|
||
float* Tantpattern, float Tstarttheta, float Tstartphi, float Tdtheta, float Tdphi, int Tthetapoints, int Tphipoints,
|
||
float* Rantpattern, float Rstarttheta, float Rstartphi, float Rdtheta, float Rdphi, int Rthetapoints, int Rphipoints,
|
||
float lamda, float fs, float nearrange, float Pt, int Freqnumbers,
|
||
CUDASigmaParam* sigma0Paramslist, int sigmaparamslistlen,
|
||
cuComplex* outecho, int* d_echoAmpFID,
|
||
int linecount,int colcount) {
|
||
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (linecount* colcount + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
//printf("\nCUDA_RTPC_SiglePRF blockSize:%d ,numBlock:%d\n",blockSize,numBlocks);
|
||
// 调用 CUDA 核函数 CUDA_RTPC_Kernel
|
||
|
||
CUDA_RTPC << <numBlocks, blockSize >> > (
|
||
antPx, antPy, antPZ,// 天线坐标
|
||
antXaxisX, antXaxisY, antXaxisZ, // 天线坐标系
|
||
antYaxisX, antYaxisY, antYaxisZ, //
|
||
antZaxisX, antZaxisY, antZaxisZ,
|
||
antDirectX, antDirectY, antDirectZ,// 天线指向
|
||
demx, demy, demz,
|
||
demcls, // 地面坐标
|
||
demslopex, demslopey, demslopez, demslopeangle,// 地面坡度
|
||
Tantpattern, Tstarttheta, Tstartphi, Tdtheta, Tdphi, Tthetapoints, Tphipoints,// 天线方向图相关
|
||
Rantpattern, Rstarttheta, Rstartphi, Rdtheta, Rdphi, Rthetapoints, Rphipoints,// 天线方向图相关
|
||
lamda, fs, nearrange, Pt, Freqnumbers, // 参数
|
||
sigma0Paramslist, sigmaparamslistlen,// 地表覆盖类型-sigma插值对应函数-ulaby
|
||
outecho, d_echoAmpFID,
|
||
linecount, colcount
|
||
);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
|
||
extern "C" void CUDAInterpSigma(
|
||
long* demcls,float* sigmaAmp, float* localanglearr,long len,
|
||
CUDASigmaParam* sigma0Paramslist, long sigmaparamslistlen) {// 地表覆盖类型-sigma插值对应函数-ulaby
|
||
int blockSize = 256; // 每个块的线程数
|
||
int numBlocks = (len + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
|
||
// 调用 CUDA 核函数
|
||
CUDA_InterpSigma << <numBlocks, blockSize >> > (
|
||
demcls, sigmaAmp, localanglearr, len,
|
||
sigma0Paramslist, sigmaparamslistlen
|
||
);
|
||
#ifdef __CUDADEBUG__
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess) {
|
||
printf("CUDA_RTPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
|
||
// Possibly: exit(-1) if program cannot continue....
|
||
}
|
||
#endif // __CUDADEBUG__
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
|
||
#endif
|
||
|
||
|