570 lines
21 KiB
Plaintext
570 lines
21 KiB
Plaintext
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#include <iostream>
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#include <memory>
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#include <cmath>
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#include <complex>
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#include <device_launch_parameters.h>
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#include <cuda_runtime.h>
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#include <cublas_v2.h>
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#include <cuComplex.h>
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#include "BaseConstVariable.h"
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#include "GPUTool.cuh"
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#ifdef __CUDANVCC___
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#define CUDAMEMORY Memory1MB*100
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#define LAMP_CUDA_PI 3.141592653589793238462643383279
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// 定义参数
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__device__ cuComplex cuCexpf(cuComplex x)
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{
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float factor = exp(x.x);
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return make_cuComplex(factor * cos(x.y), factor * sin(x.y));
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}
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// 定义仿真所需参数
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__device__ float GPU_getSigma0dB(CUDASigmaParam param,float theta) {
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return param.p1 + param.p2 * exp(-param.p3 * theta) + param.p4 * cos(param.p5 * theta + param.p6);
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}
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__device__ CUDAVector GPU_VectorAB(CUDAVector A, CUDAVector B) {
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CUDAVector C;
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C.x = B.x - A.x;
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C.y = B.y - A.y;
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C.z = B.z - A.z;
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return C;
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}
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__device__ float GPU_VectorNorm2(CUDAVector A) {
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return sqrtf(A.x * A.x + A.y * A.y + A.z * A.z);
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}
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__device__ float GPU_dotVector(CUDAVector A, CUDAVector B) {
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return A.x * B.x + A.y * B.y + A.z * B.z;
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}
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__device__ float GPU_CosAngle_VectorA_VectorB(CUDAVector A, CUDAVector B) {
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return GPU_dotVector(A, B) / (GPU_VectorNorm2(A)*GPU_VectorNorm2(B));
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}
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__device__ CUDAVectorEllipsoidal GPU_SatelliteAntDirectNormal(float RstX, float RstY, float RstZ,
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float antXaxisX, float antXaxisY, float antXaxisZ,
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float antYaxisX, float antYaxisY, float antYaxisZ,
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float antZaxisX, float antZaxisY, float antZaxisZ,
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float antDirectX, float antDirectY, float antDirectZ
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) {
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CUDAVectorEllipsoidal result{0,0,-1};
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float Xst = -1 * RstX; // 卫星 --> 地面
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float Yst = -1 * RstY;
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float Zst = -1 * RstZ;
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float AntXaxisX = antXaxisX;
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float AntXaxisY = antXaxisY;
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float AntXaxisZ = antXaxisZ;
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float AntYaxisX = antYaxisX;
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float AntYaxisY = antYaxisY;
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float AntYaxisZ = antYaxisZ;
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float AntZaxisX = antZaxisX;
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float AntZaxisY = antZaxisY;
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float AntZaxisZ = antZaxisZ;
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// 天线指向在天线坐标系下的值
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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));
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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));
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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));
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// 计算theta 与 phi
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float Norm = sqrtf(Xant * Xant + Yant * Yant + Zant * Zant); // 计算 pho
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float ThetaAnt = acosf(Zant / Norm); // theta 与 Z轴的夹角
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float YsinTheta = Yant / sinf(ThetaAnt);
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float PhiAnt = (YsinTheta / abs(YsinTheta)) * acosf(Xant / (Norm * sinf(ThetaAnt)));
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result.theta = ThetaAnt;
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result.phi = PhiAnt;
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result.pho = Norm;
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return result;
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}
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/**
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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。
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其中行是theta、列是phi
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*/
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__device__ float GPU_BillerInterpAntPattern(float* antpattern,
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float starttheta, float startphi, float dtheta, float dphi,
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long thetapoints, long phipoints,
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float searththeta, float searchphi) {
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float stheta = searththeta;
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float sphi = searchphi;
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float pthetaid = (stheta - starttheta) / dtheta;//
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float pphiid = (sphi - startphi) / dphi;
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long lasttheta = floorf(pthetaid);
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long nextTheta = lasttheta + 1;
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long lastphi = floorf(pphiid);
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long nextPhi = lastphi + 1;
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if (lasttheta < 0 || nextTheta < 0 || lastphi < 0 || nextPhi < 0 ||
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lasttheta >= thetapoints || nextTheta >= thetapoints || lastphi >= phipoints || nextPhi >= phipoints)
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{
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return 0;
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}
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else {
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float x = stheta;
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float y = sphi;
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float x1 = lasttheta * dtheta + starttheta;
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float x2 = nextTheta * dtheta + starttheta;
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float y1 = lastphi * dphi + startphi;
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float y2 = nextPhi * dphi + startphi;
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float z11 = antpattern[lasttheta * phipoints + lastphi];
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float z12 = antpattern[lasttheta * phipoints + nextPhi];
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float z21 = antpattern[nextTheta * phipoints + lastphi];
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float z22 = antpattern[nextTheta * phipoints + nextPhi];
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z11 = powf(10, z11 / 10);
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z12 = powf(10, z12 / 10);
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z21 = powf(10, z21 / 10);
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z22 = powf(10, z22 / 10);
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float GainValue = (z11 * (x2 - x) * (y2 - y)
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+ z21 * (x - x1) * (y2 - y)
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+ z12 * (x2 - x) * (y - y1)
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+ z22 * (x - x1) * (y - y1));
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GainValue = GainValue / ((x2 - x1) * (y2 - y1));
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return GainValue;
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}
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}
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__device__ cuComplex GPU_calculationEcho(float sigma0, float TransAnt, float ReciveAnt,
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float localangle, float R, float slopeangle,float Pt, float lamda ) {
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float r = R;
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float amp = Pt * TransAnt * ReciveAnt;
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amp = amp * sigma0;
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amp = amp / (powf(4 * LAMP_CUDA_PI, 2) * powf(r, 4)); // 反射强度
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float phi = (-4 * LAMP_CUDA_PI / lamda) * r;
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cuComplex echophi = make_cuComplex(0, phi);
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cuComplex echophiexp = cuCexpf(echophi);
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cuComplex echo;
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echo.x = echophiexp.x * amp;
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echo.y = echophiexp.y * amp;
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return echo;
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}
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__global__ void CUDA_DistanceAB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz,float *R, long len) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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R[idx] = sqrtf(powf(Ax[idx]-Bx[idx], 2) + powf(Ay[idx] - By[idx], 2) + powf(Az[idx] - Bz[idx], 2));
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}
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}
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__global__ void CUDA_B_DistanceA(float* Ax, float* Ay, float* Az, float Bx, float By, float Bz, float* R, long len) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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R[idx] = sqrtf(powf(Ax[idx] - Bx, 2) + powf(Ay[idx] - By, 2) + powf(Az[idx] - Bz, 2));
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}
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}
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__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) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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RstX[idx] = sX - tX[idx];
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RstY[idx] = sY - tY[idx];
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RstZ[idx] = sZ - tZ[idx];
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}
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}
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__global__ void CUDA_Norm_Vector(float* Vx, float* Vy, float* Vz,float *R, long len) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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R[idx] = sqrtf(powf(Vx[idx],2)+powf(Vy[idx],2)+powf(Vz[idx], 2));
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}
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}
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__global__ void CUDA_cosAngle_VA_AB(float* Ax, float* Ay, float* Az, float* Bx, float* By, float* Bz, float* anglecos,long len) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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float tAx = Ax[idx];
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float tAy = Ay[idx];
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float tAz = Az[idx];
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float tBx = Bx[idx];
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float tBy = By[idx];
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float tBz = Bz[idx];
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float AR = sqrtf(powf(tAx,2) + powf(tAy,2) + powf(tAz,2));
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float BR = sqrtf(powf(tBx,2) + powf(tBy,2) + powf(tBz,2));
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float dotAB = tAx * tBx + tAy * tBy + tAz * tBz;
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float result =acosf( dotAB / (AR * BR));
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anglecos[idx] = result;
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}
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}
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__global__ void CUDA_SatelliteAntDirectNormal(float* RstX,float* RstY,float* RstZ,
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float antXaxisX,float antXaxisY,float antXaxisZ,
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float antYaxisX,float antYaxisY,float antYaxisZ,
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float antZaxisX,float antZaxisY,float antZaxisZ,
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float antDirectX,float antDirectY,float antDirectZ,
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float* thetaAnt,float* phiAnt
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, long len) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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float Xst = -1*RstX[idx]; // 卫星 --> 地面
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float Yst = -1*RstY[idx];
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float Zst = -1*RstZ[idx];
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float AntXaxisX=antXaxisX ;
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float AntXaxisY=antXaxisY ;
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float AntXaxisZ=antXaxisZ ;
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float AntYaxisX=antYaxisX ;
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float AntYaxisY=antYaxisY ;
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float AntYaxisZ=antYaxisZ ;
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float AntZaxisX=antZaxisX ;
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float AntZaxisY=antZaxisY ;
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float AntZaxisZ=antZaxisZ ;
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// 天线指向在天线坐标系下的值
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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));
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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));
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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));
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// 计算theta 与 phi
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float Norm = sqrtf(Xant * Xant + Yant * Yant + Zant * Zant); // 计算 pho
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float ThetaAnt = acosf(Zant / Norm); // theta 与 Z轴的夹角
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float YsinTheta = Yant / sinf(ThetaAnt);
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float PhiAnt = (YsinTheta/abs(YsinTheta)) * acosf( Xant / (Norm * sinf(ThetaAnt)));
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thetaAnt[idx] = ThetaAnt;
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phiAnt[idx] = PhiAnt;
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}
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}
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__global__ void CUDA_calculationEcho(float* sigma0, float* TransAnt, float* ReciveAnt,
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float* localangle, float* R,float* slopeangle,
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float nearRange, float Fs,float Pt,float lamda,long FreqIDmax,
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cuComplex* echoArr , long* FreqID,
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long len) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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float r = R[idx];
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float amp = Pt * TransAnt[idx] * ReciveAnt[idx];
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amp= amp * sigma0[idx];
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amp = amp / (powf(4* LAMP_CUDA_PI,2)*powf(r,4)); // 反射强度
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// 处理相位
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float phi = (-4 * LAMP_CUDA_PI / lamda) * r;
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cuComplex echophi = make_cuComplex(0, phi) ;
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cuComplex echophiexp = cuCexpf(echophi);
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float timeR = 2 * (r - nearRange) / LIGHTSPEED * Fs;
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long timeID = floorf(timeR);
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if (timeID < 0 || timeID >= FreqIDmax) {
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timeID = 0;
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amp = 0;
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}
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cuComplex echo;
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echo.x = echophiexp.x * amp;
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echo.y = echophiexp.y * amp;
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echoArr[idx] = echo;
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}
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}
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__global__ void CUDA_BillerInterpAntPattern(float* antpattern,
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float starttheta, float startphi, float dtheta, float dphi,
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long thetapoints, long phipoints,
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float* searththeta, float* searchphi,float* searchantpattern,
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long len) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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float stheta = searththeta[idx];
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float sphi = searchphi[idx];
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float pthetaid = (stheta - starttheta) / dtheta;//
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float pphiid = (sphi - startphi) / dphi;
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long lasttheta = floorf(pthetaid);
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long nextTheta = lasttheta + 1;
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long lastphi = floorf(pphiid);
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long nextPhi = lastphi + 1;
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if (lasttheta < 0 || nextTheta < 0 || lastphi < 0 || nextPhi < 0 ||
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lasttheta >= thetapoints || nextTheta >= thetapoints || lastphi >= phipoints || nextPhi >= phipoints)
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{
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searchantpattern[idx] = 0;
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}
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else {
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float x = stheta;
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float y = sphi;
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float x1 = lasttheta * dtheta + starttheta;
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float x2 = nextTheta * dtheta + starttheta;
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float y1 = lastphi * dphi + startphi;
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float y2 = nextPhi * dphi + startphi;
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float z11 = antpattern[lasttheta * phipoints + lastphi];
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float z12 = antpattern[lasttheta * phipoints + nextPhi];
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float z21 = antpattern[nextTheta * phipoints + lastphi];
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float z22 = antpattern[nextTheta * phipoints + nextPhi];
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z11 = powf(10, z11 / 10);
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z12 = powf(10, z12 / 10);
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z21 = powf(10, z21 / 10);
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z22 = powf(10, z22 / 10);
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float GainValue = (z11 * (x2 - x) * (y2 - y)
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+ z21 * (x - x1) * (y2 - y)
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+ z12 * (x2 - x) * (y - y1)
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+ z22 * (x - x1) * (y - y1));
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GainValue = GainValue / ((x2 - x1) * (y2 - y1));
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searchantpattern[idx] = GainValue;
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}
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}
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}
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__global__ void CUDA_RTPC_Kernel(
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float antPx, float antPy, float antPz,// 天线坐标
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float antXaxisX, float antXaxisY, float antXaxisZ, // 天线坐标系
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float antYaxisX, float antYaxisY, float antYaxisZ, //
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float antZaxisX, float antZaxisY, float antZaxisZ,
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float antDirectX, float antDirectY, float antDirectZ,// 天线指向
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float* demx, float* demy, float* demz, long* demcls, // 地面坐标
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float* demslopex, float* demslopey, float* demslopez, float* demslopeangle,// 地面坡度
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float* Tantpattern, float Tstarttheta, float Tstartphi, float Tdtheta, float Tdphi, long Tthetapoints, long Tphipoints,// 天线方向图相关
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float* Rantpattern, float Rstarttheta, float Rstartphi, float Rdtheta, float Rdphi, long Rthetapoints, long Rphipoints,// 天线方向图相关
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float lamda, float fs, float nearrange, float Pt, long Freqnumbers, // 参数
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CUDASigmaParam* sigma0Paramslist, long sigmaparamslistlen,// 地表覆盖类型-sigma插值对应函数-ulaby
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cuComplex* outecho, long* d_echoAmpFID,
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long len
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) {
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long idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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long clsid = demcls[idx];
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CUDAVector Rs{ antPx,antPy,antPz };
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CUDAVector Rt{ demx[idx],demy[idx],demz[idx] };
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CUDAVector Rst{ Rs.x - Rt.x,Rs.y - Rt.y,Rs.z - Rt.z };
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CUDAVector Vslope{ demslopex[idx],demslopey[idx],demslopez[idx] };
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float R = GPU_VectorNorm2(Rst); // 斜距
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CUDAVectorEllipsoidal Rtanttheta = GPU_SatelliteAntDirectNormal( // 地面目标在天线的位置
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Rst.x, Rst.y, Rst.z,
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antXaxisX, antXaxisY, antXaxisZ,
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antYaxisX, antYaxisY, antYaxisZ,
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antZaxisX, antZaxisY, antZaxisZ,
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antDirectX, antDirectY, antDirectZ);
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float localangle=GPU_CosAngle_VectorA_VectorB(Rst, Vslope); // 距地入射角
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float sigma = GPU_getSigma0dB(sigma0Paramslist[clsid], localangle);
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sigma = powf(10.0, sigma / 10.0);// 后向散射系数
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// 发射方向图
|
||
float transPattern = GPU_BillerInterpAntPattern(Tantpattern,
|
||
Tstarttheta, Tstartphi, Tdtheta, Tdphi,Tthetapoints, Tphipoints,
|
||
Rtanttheta.theta, Rtanttheta.phi);
|
||
|
||
// 接收方向图
|
||
float receivePattern = GPU_BillerInterpAntPattern(Rantpattern,
|
||
Rstarttheta, Rstartphi, Rdtheta, Rdphi, Rthetapoints, Rphipoints,
|
||
Rtanttheta.theta, Rtanttheta.phi);
|
||
// 计算振幅、相位
|
||
float amp = Pt * transPattern * receivePattern * sigma / (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;
|
||
}
|
||
}
|
||
|
||
|
||
//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);
|
||
return ptr;
|
||
}
|
||
|
||
// 主机参数内存释放
|
||
extern "C" void FreeCUDAHost(void* ptr) {
|
||
cudaFreeHost(ptr);
|
||
}
|
||
|
||
// GPU参数内存声明
|
||
extern "C" void* mallocCUDADevice( long memsize) {
|
||
void* ptr;
|
||
cudaMalloc(&ptr, memsize);
|
||
return ptr;
|
||
}
|
||
|
||
// GPU参数内存释放
|
||
extern "C" void FreeCUDADevice(void* ptr) {
|
||
cudaFree(ptr);
|
||
}
|
||
|
||
// GPU 内存数据转移
|
||
extern "C" void HostToDevice(void* hostptr, void* deviceptr, long memsize) {
|
||
cudaMemcpy(deviceptr, hostptr, memsize, cudaMemcpyHostToDevice);
|
||
}
|
||
|
||
extern "C" void DeviceToHost(void* hostptr, void* deviceptr, long memsize) {
|
||
cudaMemcpy(hostptr, deviceptr, memsize, cudaMemcpyDeviceToHost);
|
||
}
|
||
|
||
|
||
|
||
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 << <blockSize, numBlocks >> > ( Ax, Ay, Az, Bx, By, Bz, R, len);
|
||
}
|
||
|
||
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 << <blockSize, numBlocks >> > (Ax, Ay, Az, Bx, By, Bz, R, len);
|
||
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 << <blockSize, numBlocks >> > (sX, sY, sZ,tX, tY, tZ, RstX,RstY, RstZ, len);
|
||
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 << <blockSize, numBlocks >> > (Vx,Vy,Vz,R, len);
|
||
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 << <blockSize, numBlocks >> > (Ax, Ay, Az, Bx, By, Bz, anglecos, len);
|
||
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 << <blockSize, numBlocks >> > ( RstX, RstY, RstZ,
|
||
antXaxisX, antXaxisY, antXaxisZ,
|
||
antYaxisX, antYaxisY, antYaxisZ,
|
||
antZaxisX, antZaxisY, antZaxisZ,
|
||
antDirectX, antDirectY, antDirectZ,
|
||
thetaAnt, phiAnt
|
||
, len);
|
||
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 << <blockSize, numBlocks >> > ( sigma0, TransAnt,ReciveAnt,
|
||
localangle, R, slopeangle,
|
||
nearRange, Fs, pt, lamda, FreqIDmax,
|
||
echoAmp, FreqID,
|
||
len);
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
|
||
|
||
|
||
|
||
|
||
extern "C" void CUDA_RTPC_SiglePRF(
|
||
float antPx, float antPy, float antPZ,// 天线坐标
|
||
float antXaxisX, float antXaxisY, float antXaxisZ, // 天线坐标系
|
||
float antYaxisX, float antYaxisY, float antYaxisZ, //
|
||
float antZaxisX, float antZaxisY, float antZaxisZ,
|
||
float antDirectX, float antDirectY, float antDirectZ,// 天线指向
|
||
float* demx, float* demy, float* demz, long* demcls, // 地面坐标
|
||
float* demslopex, float* demslopey, float* demslopez, float* demslopeangle,// 地面坡度
|
||
float* Tantpattern, float Tstarttheta, float Tstartphi, float Tdtheta, float Tdphi, long Tthetapoints, long Tphipoints,// 天线方向图相关
|
||
float* Rantpattern, float Rstarttheta, float Rstartphi, float Rdtheta, float Rdphi, long Rthetapoints, long Rphipoints,// 天线方向图相关
|
||
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, long* d_echoAmpFID,
|
||
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_RTPC_Kernel<<<blockSize,numBlocks>>>(
|
||
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,
|
||
len
|
||
);
|
||
cudaDeviceSynchronize();
|
||
}
|
||
|
||
|
||
|
||
|
||
|
||
#endif |