625 lines
21 KiB
Plaintext
625 lines
21 KiB
Plaintext
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#include <time.h>
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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 "GPURFPC.cuh"
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#ifdef __CUDANVCC___
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__device__ double GPU_getSigma0dB(CUDASigmaParam param, double theta) {//线性值
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double sigma = param.p1 + param.p2 * exp(-param.p3 * theta) + param.p4 * cos(param.p5 * theta + param.p6);
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return sigma;
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}
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__device__ CUDAVectorEllipsoidal GPU_SatelliteAntDirectNormal(
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double RstX, double RstY, double RstZ,
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double antXaxisX, double antXaxisY, double antXaxisZ,
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double antYaxisX, double antYaxisY, double antYaxisZ,
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double antZaxisX, double antZaxisY, double antZaxisZ,
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double antDirectX, double antDirectY, double antDirectZ
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) {
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CUDAVectorEllipsoidal result{ 0,0,-1 };
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// 求解天线增益
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double Xst = -1 * RstX; // 卫星 --> 地面
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double Yst = -1 * RstY;
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double Zst = -1 * RstZ;
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double AntXaxisX = antXaxisX;
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double AntXaxisY = antXaxisY;
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double AntXaxisZ = antXaxisZ;
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double AntYaxisX = antYaxisX;
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double AntYaxisY = antYaxisY;
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double AntYaxisZ = antYaxisZ;
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double AntZaxisX = antZaxisX;
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double AntZaxisY = antZaxisY;
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double AntZaxisZ = antZaxisZ;
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// 归一化
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double RstNorm = sqrtf(Xst * Xst + Yst * Yst + Zst * Zst);
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double AntXaxisNorm = sqrtf(AntXaxisX * AntXaxisX + AntXaxisY * AntXaxisY + AntXaxisZ * AntXaxisZ);
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double AntYaxisNorm = sqrtf(AntYaxisX * AntYaxisX + AntYaxisY * AntYaxisY + AntYaxisZ * AntYaxisZ);
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double AntZaxisNorm = sqrtf(AntZaxisX * AntZaxisX + AntZaxisY * AntZaxisY + AntZaxisZ * AntZaxisZ);
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double Rx = Xst / RstNorm;
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double Ry = Yst / RstNorm;
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double Rz = Zst / RstNorm;
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double Xx = AntXaxisX / AntXaxisNorm;
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double Xy = AntXaxisY / AntXaxisNorm;
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double Xz = AntXaxisZ / AntXaxisNorm;
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double Yx = AntYaxisX / AntYaxisNorm;
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double Yy = AntYaxisY / AntYaxisNorm;
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double Yz = AntYaxisZ / AntYaxisNorm;
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double Zx = AntZaxisX / AntZaxisNorm;
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double Zy = AntZaxisY / AntZaxisNorm;
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double Zz = AntZaxisZ / AntZaxisNorm;
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double 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);
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double 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);
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double 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);
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// 计算theta 与 phi
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double Norm = sqrtf(Xant * Xant + Yant * Yant + Zant * Zant); // 计算 pho
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double ThetaAnt = acosf(Zant / Norm); // theta 与 Z轴的夹角
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double PhiAnt = atanf(Yant / Xant); // -pi/2 ~pi/2
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if (abs(Yant) < PRECISIONTOLERANCE) { // X轴上
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PhiAnt = 0;
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}
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else if (abs(Xant) < PRECISIONTOLERANCE) { // Y轴上，原点
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if (Yant > 0) {
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PhiAnt = PI / 2;
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}
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else {
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PhiAnt = -PI / 2;
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}
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}
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else if (Xant < 0) {
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if (Yant > 0) {
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PhiAnt = PI + PhiAnt;
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}
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else {
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PhiAnt = -PI + PhiAnt;
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}
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}
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else { // Xant>0 X 正轴
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}
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if (isnan(PhiAnt)) {
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printf("V=[%f,%f,%f];norm=%f;thetaAnt=%f;phiAnt=%f;\n", Xant, Yant, Zant, Norm, ThetaAnt, PhiAnt);
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}
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result.theta = ThetaAnt;
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result.phi = PhiAnt;
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result.Rho = Norm;
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return result;
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}
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__device__ double GPU_BillerInterpAntPattern(double* antpattern,
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double starttheta, double startphi, double dtheta, double dphi,
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long thetapoints, long phipoints,
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double searththeta, double searchphi) {
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double stheta = searththeta;
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double sphi = searchphi;
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if (stheta > 90) {
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return 0;
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}
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else {}
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double pthetaid = (stheta - starttheta) / dtheta;//
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double 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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double x = stheta;
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double y = sphi;
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double x1 = lasttheta * dtheta + starttheta;
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double x2 = nextTheta * dtheta + starttheta;
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double y1 = lastphi * dphi + startphi;
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double y2 = nextPhi * dphi + startphi;
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double z11 = antpattern[lasttheta * phipoints + lastphi];
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double z12 = antpattern[lasttheta * phipoints + nextPhi];
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double z21 = antpattern[nextTheta * phipoints + lastphi];
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double z22 = antpattern[nextTheta * phipoints + nextPhi];
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//z11 = powf(10, z11 / 10); // dB-> 线性
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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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double 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(double sigma0, double TransAnt, double ReciveAnt,
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double localangle, double R, double slopeangle, double Pt, double lamda) {
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double 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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double 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 = make_cuComplex(echophiexp.x * amp, echophiexp.y * amp);
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return echo;
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}
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__global__ void CUDA_SatelliteAntDirectNormal(double* RstX, double* RstY, double* RstZ,
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double antXaxisX, double antXaxisY, double antXaxisZ,
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double antYaxisX, double antYaxisY, double antYaxisZ,
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double antZaxisX, double antZaxisY, double antZaxisZ,
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double antDirectX, double antDirectY, double antDirectZ,
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double* thetaAnt, double* 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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double Xst = -1 * RstX[idx]; // 卫星 --> 地面
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double Yst = -1 * RstY[idx];
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double Zst = -1 * RstZ[idx];
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double AntXaxisX = antXaxisX;
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double AntXaxisY = antXaxisY;
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double AntXaxisZ = antXaxisZ;
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double AntYaxisX = antYaxisX;
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double AntYaxisY = antYaxisY;
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double AntYaxisZ = antYaxisZ;
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double AntZaxisX = antZaxisX;
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double AntZaxisY = antZaxisY;
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double AntZaxisZ = antZaxisZ;
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// 归一化
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double RstNorm = sqrtf(Xst * Xst + Yst * Yst + Zst * Zst);
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double AntXaxisNorm = sqrtf(AntXaxisX * AntXaxisX + AntXaxisY * AntXaxisY + AntXaxisZ * AntXaxisZ);
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double AntYaxisNorm = sqrtf(AntYaxisX * AntYaxisX + AntYaxisY * AntYaxisY + AntYaxisZ * AntYaxisZ);
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double AntZaxisNorm = sqrtf(AntZaxisX * AntZaxisX + AntZaxisY * AntZaxisY + AntZaxisZ * AntZaxisZ);
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double Rx = Xst / RstNorm;
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double Ry = Yst / RstNorm;
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double Rz = Zst / RstNorm;
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double Xx = AntXaxisX / AntXaxisNorm;
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double Xy = AntXaxisY / AntXaxisNorm;
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double Xz = AntXaxisZ / AntXaxisNorm;
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double Yx = AntYaxisX / AntYaxisNorm;
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double Yy = AntYaxisY / AntYaxisNorm;
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double Yz = AntYaxisZ / AntYaxisNorm;
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double Zx = AntZaxisX / AntZaxisNorm;
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double Zy = AntZaxisY / AntZaxisNorm;
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double Zz = AntZaxisZ / AntZaxisNorm;
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double 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);
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double 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);
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double 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);
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// 计算theta 与 phi
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double Norm = sqrtf(Xant * Xant + Yant * Yant + Zant * Zant); // 计算 pho
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double ThetaAnt = acosf(Zant / Norm); // theta 与 Z轴的夹角
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double PhiAnt = atanf(Yant / Xant); // -pi/2 ~pi/2
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if (abs(Yant) < PRECISIONTOLERANCE) { // X轴上
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PhiAnt = 0;
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}
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else if (abs(Xant) < PRECISIONTOLERANCE) { // Y轴上，原点
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if (Yant > 0) {
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PhiAnt = PI / 2;
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}
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else {
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PhiAnt = -PI / 2;
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}
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}
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else if (Xant < 0) {
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if (Yant > 0) {
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PhiAnt = PI + PhiAnt;
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}
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else {
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PhiAnt = -PI + PhiAnt;
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}
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}
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else { // Xant>0 X 正轴
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}
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if (isnan(PhiAnt)) {
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printf("V=[%f,%f,%f];norm=%f;thetaAnt=%f;phiAnt=%f;\n", Xant, Yant, Zant, Norm, ThetaAnt, PhiAnt);
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}
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//if (abs(ThetaAnt - 0) < PRECISIONTOLERANCE) {
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// PhiAnt = 0;
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//}
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//else {}
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thetaAnt[idx] = ThetaAnt * r2d;
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phiAnt[idx] = PhiAnt * r2d;
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//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
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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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// , phiAnt[idx]
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// , thetaAnt[idx]
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//);
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}
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}
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__global__ void CUDA_BillerInterpAntPattern(double* antpattern,
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double starttheta, double startphi, double dtheta, double dphi,
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long thetapoints, long phipoints,
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double* searththeta, double* searchphi, double* 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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double stheta = searththeta[idx];
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double sphi = searchphi[idx];
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double pthetaid = (stheta - starttheta) / dtheta;//
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double 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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double x = stheta;
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double y = sphi;
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double x1 = lasttheta * dtheta + starttheta;
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double x2 = nextTheta * dtheta + starttheta;
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double y1 = lastphi * dphi + startphi;
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double y2 = nextPhi * dphi + startphi;
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double z11 = antpattern[lasttheta * phipoints + lastphi];
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double z12 = antpattern[lasttheta * phipoints + nextPhi];
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double z21 = antpattern[nextTheta * phipoints + lastphi];
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double 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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double 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_AntPatternInterpGain(double* anttheta, double* antphi, double* gain,
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double* antpattern, double starttheta, double startphi, double dtheta, double dphi, int thetapoints, int phipoints, long len) {
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < len) {
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double temptheta = anttheta[idx];
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double tempphi = antphi[idx];
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double antPatternGain = GPU_BillerInterpAntPattern(antpattern,
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starttheta, startphi, dtheta, dphi, thetapoints, phipoints,
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temptheta, tempphi);
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gain[idx] = antPatternGain;
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}
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}
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__global__ void CUDA_InterpSigma(
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long* demcls, double* sigmaAmp, double* localanglearr, long len,
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CUDASigmaParam* sigma0Paramslist, long sigmaparamslistlen) {
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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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double localangle = localanglearr[idx];
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CUDASigmaParam tempsigma = sigma0Paramslist[clsid];
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if (localangle < 0 || localangle >= LAMP_CUDA_PI / 2) {
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sigmaAmp[idx] = 0;
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}
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else {}
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if (abs(tempsigma.p1) < PRECISIONTOLERANCE &&
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abs(tempsigma.p2) < PRECISIONTOLERANCE &&
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abs(tempsigma.p3) < PRECISIONTOLERANCE &&
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abs(tempsigma.p4) < PRECISIONTOLERANCE &&
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abs(tempsigma.p5) < PRECISIONTOLERANCE &&
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abs(tempsigma.p6) < PRECISIONTOLERANCE
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) {
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sigmaAmp[idx] = 0;
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}
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else {
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double sigma = GPU_getSigma0dB(tempsigma, localangle);
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sigma = powf(10.0, sigma / 10.0);// 后向散射系数
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//printf("cls:%d;localangle=%f;sigma0=%f;\n", clsid, localangle, sigma);
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sigmaAmp[idx] = sigma;
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}
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}
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}
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__global__ void CUDAKernel_RFPC_Computer_R_Gain(
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double antX, double antY, double antZ, // 天线的坐标
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double* targetX, double* targetY, double* targetZ, long len, // 地面坐标
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long* demCls,
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double* demSlopeX, double* demSlopeY, double* demSlopeZ, // 地表坡度矢量
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double antXaxisX, double antXaxisY, double antXaxisZ, // 天线坐标系的X轴
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double antYaxisX, double antYaxisY, double antYaxisZ,// 天线坐标系的Y轴
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double antZaxisX, double antZaxisY, double antZaxisZ,// 天线坐标系的Z轴
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double antDirectX, double antDirectY, double antDirectZ,// 天线的指向
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double Pt,// 发射能量
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double refPhaseRange,
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double* TransAntpattern, PatternImageDesc TransAntpatternDesc,// double Transtarttheta, double Transstartphi, double Transdtheta, double Transdphi, int Transthetapoints, int Transphipoints, // 发射天线方向图
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double* ReceiveAntpattern, PatternImageDesc ReceiveAntpatternDesc,// double Receivestarttheta, double Receivestartphi, double Receivedtheta, double Receivedphi, int Receivethetapoints, int Receivephipoints,//接收天线方向图
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double NearR, double FarR, // 距离范围
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CUDASigmaParam* sigma0Paramslist, long sigmaparamslistlen,// 插值图
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float* outR, // 输出距离
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float* outAmp
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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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double tx = targetX[idx];
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double ty = targetY[idx];
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double tz = targetZ[idx];
|
||
double RstX = antX - tx; // 计算坐标矢量
|
||
double RstY = antY - ty;
|
||
double RstZ = antZ - tz;
|
||
|
||
double slopeX = demSlopeX[idx];
|
||
double slopeY = demSlopeY[idx];
|
||
double slopeZ = demSlopeZ[idx];
|
||
|
||
double RstR2 = RstX * RstX + RstY * RstY + RstZ * RstZ;
|
||
double RstR = sqrt(RstR2); // 矢量距离
|
||
|
||
//printf("antX=%f;antY=%f;antZ=%f;targetX=%f;targetY=%f;targetZ=%f;RstR=%.6f;diffR=%.6f;\n",antX,antY,antZ,targetX,targetY,targetZ,RstR, RstR - 9.010858499003178e+05);
|
||
|
||
if (RstR<NearR || RstR>FarR) {
|
||
outAmp[idx] = 0;
|
||
outR[idx] = 0;
|
||
}
|
||
else {
|
||
// 求解坡度
|
||
double slopR = sqrtf(slopeX * slopeX + slopeY * slopeY + slopeZ * slopeZ); //
|
||
double dotAB = RstX * slopeX + RstY * slopeY + RstZ * slopeZ;
|
||
double localangle = acosf(dotAB / (RstR * slopR)); // 局地入射角
|
||
double ampGain = 0;
|
||
// 求解天线方向图指向
|
||
CUDAVectorEllipsoidal antVector = GPU_SatelliteAntDirectNormal(
|
||
RstX, RstY, RstZ,
|
||
antXaxisX, antXaxisY, antXaxisZ,
|
||
antYaxisX, antYaxisY, antYaxisZ,
|
||
antZaxisX, antZaxisY, antZaxisZ,
|
||
antDirectX, antDirectY, antDirectZ
|
||
);
|
||
if (antVector.Rho > 0) {
|
||
// 发射方向图
|
||
double temptheta = antVector.theta * r2d;
|
||
double tempphi = antVector.phi * r2d;
|
||
double TansantPatternGain =
|
||
GPU_BillerInterpAntPattern(
|
||
TransAntpattern,
|
||
TransAntpatternDesc.startTheta, TransAntpatternDesc.startPhi,
|
||
TransAntpatternDesc.dtheta, TransAntpatternDesc.dphi, TransAntpatternDesc.thetanum, TransAntpatternDesc.phinum,
|
||
//Transtarttheta, Transstartphi, Transdtheta, Transdphi, Transthetapoints, Transphipoints,
|
||
temptheta, tempphi);
|
||
|
||
// 接收方向图
|
||
double antPatternGain = GPU_BillerInterpAntPattern(
|
||
ReceiveAntpattern,
|
||
ReceiveAntpatternDesc.startTheta, ReceiveAntpatternDesc.startPhi,
|
||
ReceiveAntpatternDesc.dtheta, ReceiveAntpatternDesc.dphi, ReceiveAntpatternDesc.thetanum, ReceiveAntpatternDesc.phinum,
|
||
//Receivestarttheta, Receivestartphi, Receivedtheta, Receivedphi, Receivethetapoints, Receivephipoints,
|
||
temptheta, tempphi);
|
||
|
||
// 计算
|
||
double sigma0 = 0;
|
||
{
|
||
long clsid = demCls[idx];
|
||
//printf("clsid=%d\n", clsid);
|
||
CUDASigmaParam tempsigma = sigma0Paramslist[clsid];
|
||
if (localangle < 0 || localangle >= LAMP_CUDA_PI / 2) {
|
||
sigma0 = 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
|
||
) {
|
||
sigma0 = 0;
|
||
}
|
||
else {
|
||
double sigma = GPU_getSigma0dB(tempsigma, localangle);
|
||
sigma0 = powf(10.0, sigma / 10.0);// 后向散射系数
|
||
}
|
||
}
|
||
|
||
ampGain = TansantPatternGain * antPatternGain;
|
||
ampGain = ampGain / (powf(4 * LAMP_CUDA_PI, 2) * powf(RstR, 4)); // 反射强度
|
||
outAmp[idx] = float(ampGain * Pt * sigma0);
|
||
outR[idx] = float(RstR - refPhaseRange);
|
||
}
|
||
else {
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
|
||
|
||
__global__ void CUDAKernel_PRF_GeneratorEcho(float* Rarr, float* ampArr,
|
||
long pixelcount,
|
||
float f0, float dfreq,long freqnum,
|
||
double* echo_real,double* echo_imag, long prfid)
|
||
{
|
||
//// 假定共享内存大小为49152 byte
|
||
//// 假定每个Block 线程数大小为 32
|
||
__shared__ float s_R[GPU_SHARE_MEMORY]; // 距离 32*12 * 8= 49.2kb
|
||
__shared__ float s_Amp[GPU_SHARE_MEMORY]; // 振幅 3072 * 8= 49.2kb 49.2*2 = 98.4 < 100 KB
|
||
|
||
int idx = blockIdx.x * blockDim.x + threadIdx.x;; // 获取当前的线程编码
|
||
int tid = threadIdx.x;// 获取 单个 block 中的线程ID
|
||
|
||
const long startPIX = idx * GPU_SHARE_STEP; // 计算偏移
|
||
int curthreadidx = 0;
|
||
for (long i = 0; i < GPU_SHARE_STEP; i++) {
|
||
curthreadidx = i * BLOCK_SIZE + tid; // 计算分块
|
||
s_R[curthreadidx] = (startPIX + i) < pixelcount ? Rarr[startPIX + i] : 0.0;
|
||
s_Amp[curthreadidx] = (startPIX + i) < pixelcount ? ampArr[startPIX + i] : 0.0;
|
||
}
|
||
|
||
|
||
//__syncthreads(); // 确定所有待处理数据都已经进入程序中
|
||
|
||
if (startPIX < pixelcount) { // 存在可能处理的计算
|
||
float temp_real = 0;
|
||
float temp_imag = 0;
|
||
float factorjTemp = 0;
|
||
float temp_phi = 0;
|
||
float temp_amp = 0;
|
||
long dataid = 0;
|
||
curthreadidx = 0;
|
||
for (long fid = 0; fid < freqnum; fid++) {
|
||
factorjTemp = RFPCPIDIVLIGHT *(f0+ fid* dfreq);
|
||
for (long j = 0; j < GPU_SHARE_STEP; j++) {
|
||
dataid = j * BLOCK_SIZE + tid; //
|
||
temp_phi = s_R[dataid] * factorjTemp;
|
||
temp_amp = s_Amp[dataid];
|
||
|
||
temp_real += temp_amp * cosf(temp_phi);
|
||
temp_imag += temp_amp * sinf(temp_phi);
|
||
}
|
||
atomicAdd(&echo_real[prfid * freqnum + fid], temp_real); // 更新实部
|
||
atomicAdd(&echo_imag[prfid * freqnum + fid], temp_imag); // 更新虚部
|
||
}
|
||
}
|
||
}
|
||
|
||
|
||
|
||
|
||
|
||
|
||
/** 对外封装接口 *******************************************************************************************************/
|
||
|
||
extern "C" void CUDA_RFPC_MainBlock(
|
||
double* antX, double* antY, double* antZ, // 天线的坐标
|
||
double* antXaxisX, double* antXaxisY, double* antXaxisZ, // 天线坐标系的X轴
|
||
double* antYaxisX, double* antYaxisY, double* antYaxisZ,// 天线坐标系的Y轴
|
||
double* antZaxisX, double* antZaxisY, double* antZaxisZ,// 天线坐标系的Z轴
|
||
double* antDirectX, double* antDirectY, double* antDirectZ,// 天线的指向
|
||
long PRFCount, // 脉冲数
|
||
float f0, float dfreq, long freqnum, // 频率数
|
||
double* targetX, double* targetY, double* targetZ, long TargetPixelNumber, // 地面坐标
|
||
long* demCls, // 地表类别
|
||
double* demSlopeX, double* demSlopeY, double* demSlopeZ, // 地表坡度矢量
|
||
double Pt,// 发射能量
|
||
double refPhaseRange,
|
||
double* TransAntpattern, PatternImageDesc TransAntpatternDesc,// double Transtarttheta, double Transstartphi, double Transdtheta, double Transdphi, int Transthetapoints, int Transphipoints, // 发射天线方向图
|
||
double* ReceiveAntpattern, PatternImageDesc ReceiveAntpatternDesc, //double Receivestarttheta, double Receivestartphi, double Receivedtheta, double Receivedphi, int Receivethetapoints, int Receivephipoints,//接收天线方向图
|
||
double NearR, double FarR, // 距离范围
|
||
CUDASigmaParam* sigma0Paramslist, long sigmaparamslistlen,// 插值图
|
||
double* out_echoReal, double* out_echoImag,// 输出回波
|
||
float* temp_R, float* temp_amp
|
||
//, double* temp_phi, double* temp_real, double* temp_imag// 临时变量
|
||
) {
|
||
|
||
long blocknum = 0;
|
||
long pixelcount=TargetPixelNumber;
|
||
int numBlocks = 0;
|
||
for(long pid=0;pid<PRFCount;pid++){
|
||
numBlocks = (TargetPixelNumber + BLOCK_SIZE - 1) / BLOCK_SIZE; // 根据 pixelcount 计算网格大小
|
||
|
||
|
||
CUDAKernel_RFPC_Computer_R_Gain<<<numBlocks , BLOCK_SIZE >>>(
|
||
antX[pid], antY[pid], antZ[pid],
|
||
targetX, targetY, targetZ, TargetPixelNumber,
|
||
demCls,
|
||
demSlopeX, demSlopeY, demSlopeZ,
|
||
antXaxisX[pid], antXaxisY[pid], antXaxisZ[pid],
|
||
antYaxisX[pid], antYaxisY[pid], antYaxisZ[pid],
|
||
antZaxisX[pid], antZaxisY[pid], antZaxisZ[pid],
|
||
antDirectX[pid], antDirectY[pid], antDirectZ[pid],
|
||
Pt,// 增益后发射能量
|
||
refPhaseRange,
|
||
TransAntpattern,
|
||
TransAntpatternDesc,
|
||
//Transtarttheta, Transstartphi, Transdtheta, Transdphi, Transthetapoints, Transphipoints,
|
||
ReceiveAntpattern,
|
||
ReceiveAntpatternDesc,
|
||
//Receivestarttheta, Receivestartphi, Receivedtheta, Receivedphi, Receivethetapoints, Receivephipoints,
|
||
NearR, FarR,
|
||
sigma0Paramslist, sigmaparamslistlen,
|
||
//factorj, freqnum,
|
||
temp_R, // 输出距离
|
||
temp_amp
|
||
//out_echoReal, out_echoImag, pid // 输出振幅
|
||
);
|
||
|
||
blocknum = (pixelcount + GPU_SHARE_STEP - 1) / GPU_SHARE_STEP;
|
||
numBlocks = (blocknum + BLOCK_SIZE - 1) / BLOCK_SIZE; // 网格数量
|
||
CUDAKernel_PRF_GeneratorEcho << <numBlocks, BLOCK_SIZE >> >
|
||
(temp_R, temp_amp, pixelcount,
|
||
f0,dfreq,freqnum,
|
||
out_echoReal, out_echoImag, pid);
|
||
|
||
}
|
||
cudaDeviceSynchronize();
|
||
|
||
}
|
||
|
||
|
||
|
||
#endif
|
||
|
||
|