#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 "GPURFPC.cuh"

#ifdef __CUDANVCC___





__device__ float GPU_getSigma0dB(CUDASigmaParam param, float theta) {//线性值
	float sigma= param.p1 + param.p2 * exp(-param.p3 * theta) + param.p4 * cos(param.p5 * theta + param.p6);
	return  sigma;
}


__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 };

	// 求解天线增益
	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;

	// 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);
	}
 

	result.theta = ThetaAnt;
	result.phi = PhiAnt;
	result.Rho = Norm;
	return result;
}

__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 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=make_cuComplex(echophiexp.x * amp, echophiexp.y * amp);
	return echo;
}


__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_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 = make_cuComplex(echophiexp.x , echophiexp.y);
		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 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];
		CUDASigmaParam tempsigma = sigma0Paramslist[clsid];
		if (localangle < 0 || localangle >= LAMP_CUDA_PI/2) {
			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;
		}
	}
}


__global__ void CUDA_CalculationEchoAmp(float* sigma0, float* TransAnt, float* ReciveAnt, float* R, 
	float Pt, 
	float* ampArr, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		float r = R[idx];
		float amptemp = Pt * TransAnt[idx] * ReciveAnt[idx] * sigma0[idx];
		amptemp = amptemp / (powf(4 * LAMP_CUDA_PI, 2) * powf(r, 4)); // 反射强度
		ampArr[idx] = amptemp;
	}
}


__global__ void CUDA_CalculationEchoPhase(float* R, float lamda, float* phaseArr, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		float r = R[idx];
		// 处理相位
		float phi = (-4 * LAMP_CUDA_PI / lamda) * r;
		phaseArr[idx] = phi;
	}
}


__global__ void CUDA_CombinationEchoAmpAndPhase(float* R,
	float* echoAmp,float* echoPhase,
	float nearRange, float Fs, long plusepoints,
	cuComplex* echo, long* FreqID, long len) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		float r = R[idx];
		float phase = echoPhase[idx];
		float amp = echoAmp[idx];
		cuComplex echophi = make_cuComplex(0, phase);
		cuComplex echophiexp = cuCexpf(echophi);

		float timeR = 2 * (r - nearRange) / LIGHTSPEED * Fs;
		long timeID = floorf(timeR);
		if (timeID < 0 || timeID >= plusepoints) {
			timeID = 0;
			amp = 0;
		}
		cuComplex echotemp = make_cuComplex(echophiexp.x*amp, echophiexp.y*amp);
		echo[idx] = echotemp;
		FreqID[idx] = timeID;
	}
}


__global__ void CUDAKernel_RFPC_Caluation_R_Gain(
	float antX, float antY, float antZ,
	float* targetX, float* targetY, float* targetZ, long len,
	float* demSlopeX, float* demSlopeY, float* demSlopeZ,  
	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* TransAntpattern,
	float Transtarttheta, float Transstartphi, float Transdtheta, float Transdphi, int Transthetapoints, int Transphipoints,
	float* ReceiveAntpattern, 
	float Receivestarttheta, float Receivestartphi, float Receivedtheta, float Receivedphi, int Receivethetapoints, int Receivephipoints,
	float NearR, float FarR,
	float* outR,
	float* outLocalAngle,
	float* AmpGain) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		float RstX = antX - targetX[idx]; // 计算坐标矢量
		float RstY = antY - targetY[idx];
		float RstZ = antZ - targetZ[idx];
		float slopeX = demSlopeX[idx];
		float slopeY = demSlopeY[idx];
		float slopeZ = demSlopeZ[idx];
		float RstR= sqrtf(RstX* RstX + RstY* RstY + RstZ* RstZ); // 矢量距离

		if (RstR<NearR || RstR>FarR) {
			outLocalAngle[idx] = 0;
			outR[idx] = 0;
			AmpGain[idx] = 0;
		}
		else {
			// 求解坡度
			float slopR = sqrtf(slopeX * slopeX + slopeY * slopeY + slopeZ * slopeZ); //  
			float dotAB = RstX * slopeX + RstY * slopeY + RstZ * slopeZ;
			outLocalAngle[idx] = acosf(dotAB / (RstR * slopR)); // 局地入射角
			float 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
				float temptheta = antVector.theta * r2d;
				float tempphi = antVector.phi * r2d;
				float TansantPatternGain =
					GPU_BillerInterpAntPattern(
						TransAntpattern,
						Transtarttheta, Transstartphi, Transdtheta, Transdphi, Transthetapoints, Transphipoints,
						temptheta, tempphi);

				// 接收方向图
				float antPatternGain = GPU_BillerInterpAntPattern(
					ReceiveAntpattern,
					Receivestarttheta, Receivestartphi, Receivedtheta, Receivedphi, Receivethetapoints, Receivephipoints,
					temptheta, tempphi);

				ampGain = TansantPatternGain * antPatternGain;
				ampGain = ampGain / (powf(4 * LAMP_CUDA_PI, 2) * powf(RstR, 4)); // 反射强度
				AmpGain[idx] = ampGain;
				outR[idx] = RstR;
			}
			else {
				outR[idx] = 0;
				AmpGain[idx] = 0;
			}
		}
	}
}

__global__ void CUDARFPCKernel_Target_Freq_EchoData(
	float* InR,
	float* InlocalAngle,
	float* InampGain,
	long* Indemcls,
	long len,
	float Pt, float freq,
	CUDASigmaParam* sigma0Paramslist, long sigmaparamslistlen,
	cuComplex* OutechoArr) {
	long  idx = blockIdx.x * blockDim.x + threadIdx.x;
	if (idx < len) {
		float sigma0 = 0;
		{
			long clsid = Indemcls[idx];
			float localangle = InlocalAngle[idx];
			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 {
				float sigma = GPU_getSigma0dB(tempsigma, localangle);
				sigma0 = powf(10.0, sigma / 10.0);// 后向散射系数
			}
		}
		float amp = Pt * InampGain[idx] * sigma0;
		float phi = 4 * PI * freq / LIGHTSPEED * InR[idx];
		// 欧拉公式 exp(ix)=cos(x)+isin(x)
		// echo=Aexp(ix)=A*cos(x)+i*A*sin(x)
		cuComplex echotemp = make_cuComplex(amp * cosf(phi), amp * sinf(phi));
		OutechoArr[idx] = echotemp;
	}
}



__global__ void  CUDACkernel_Complex_SUM_reduce_dynamicshared(cuComplex* d_x, cuComplex* d_y, long N)
{
	const int tid = threadIdx.x;              //  某个block内的线程标号 index
	const int bid = blockIdx.x;               //  某个block在网格grid内的标号 index
	const int n = bid * blockDim.x + tid;      //  n 是某个线程的标号 index
	__shared__ cuComplex s_y[128];                  //  分配共享内存空间，不同的block都有共享内存变量的副本
	s_y[tid] = (n < N) ? d_x[n] : make_cuComplex(0.0,0.0); //  每个block的共享内存变量副本，都用全局内存数组d_x来赋值，最后一个多出来的用0
	__syncthreads();  //  线程块内部直接同步

	for (int offset = blockDim.x >> 1; offset > 0; offset >>= 1) // 折半
	{

		if (tid < offset)           // 线程标号的index 不越界  折半
		{
			s_y[tid] = cuCaddf(s_y[tid], s_y[tid + offset]);  //  某个block内的线程做折半规约
		}
		__syncthreads();        // 同步block内部的线程
	}

	if (tid == 0)        // 某个block只做一次操作
	{
		d_y[bid] = s_y[0];     //  复制共享内存变量累加的结果到全局内存
	}
}














/** 对外封装接口 *******************************************************************************************************/

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_RFPC_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_RFPC_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_RFPC_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_RFPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
		// Possibly: exit(-1) if program cannot continue....
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

extern "C" void CUDACalculationEchoAmp(float* sigma0, float* TransAnt, float* ReciveAnt, float* R, float Pt, float* ampArr, 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_CalculationEchoAmp << <numBlocks, blockSize >> > (
		sigma0, TransAnt, ReciveAnt, R,	Pt, ampArr, len);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDA_RFPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
		// Possibly: exit(-1) if program cannot continue....
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();

}

extern "C" void CUDACalculationEchoPhase(float* R, float lamda, float* phaseArr, 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_CalculationEchoPhase << <numBlocks, blockSize >> > (
		R, lamda, phaseArr, len);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDA_RFPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
		// Possibly: exit(-1) if program cannot continue....
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();


}

extern "C" void CUDACombinationEchoAmpAndPhase(float* R,
	float* echoAmp, float* echoPhase,
	float nearRange, float Fs, long plusepoints, cuComplex* echo, 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_CombinationEchoAmpAndPhase << <numBlocks, blockSize >> > (
		R, 
		echoAmp, echoPhase,
		nearRange, Fs, plusepoints, echo, FreqID, len
		);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDA_RFPC_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_RFPC_SiglePRF CUDA Error: %s\n", cudaGetErrorString(err));
		// Possibly: exit(-1) if program cannot continue....
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();
}

extern "C" void CUDARFPC_Caluation_R_Gain(float antX, float antY, float antZ,
	float* targetX, float* targetY, float* targetZ, long TargetPixelNumber, 
	float* demSlopeX, float* demSlopeY, float* demSlopeZ,
	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* TransAntpattern, float Transtarttheta, float Transstartphi, float Transdtheta, float Transdphi, int Transthetapoints, int Transphipoints, 
	float* ReceiveAntpattern, float Receivestarttheta, float Receivestartphi, float Receivedtheta, float Receivedphi, int Receivethetapoints, int Receivephipoints, 
	float NearR, float FarR,
	float* outR, 
	float* outLocalAngle, 
	float* AmpGain)
{

	int blockSize = 256; // 每个块的线程数
	int numBlocks = (TargetPixelNumber + blockSize - 1) / blockSize; // 根据 pixelcount 计算网格大小
	// 调用 CUDA 核函数
	CUDAKernel_RFPC_Caluation_R_Gain << <numBlocks, blockSize >> > (
		antX,  antY,  antZ,
		targetX,targetY,  targetZ,  TargetPixelNumber,
		demSlopeX, demSlopeY, demSlopeZ,
		antXaxisX,  antXaxisY,  antXaxisZ,
		antYaxisX,  antYaxisY,  antYaxisZ,
		antZaxisX,  antZaxisY,  antZaxisZ,
		antDirectX,  antDirectY,  antDirectZ,
		TransAntpattern,  
		Transtarttheta,  Transstartphi,  Transdtheta,  Transdphi,  Transthetapoints,  Transphipoints,
		ReceiveAntpattern,  
		Receivestarttheta,  Receivestartphi,  Receivedtheta,  Receivedphi,  Receivethetapoints,  Receivephipoints,
		NearR, FarR,
		outR,
		outLocalAngle,
		AmpGain
		);


#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDARFPC_Caluation_R_Gain CUDA Error: %s\n", cudaGetErrorString(err));
		// Possibly: exit(-1) if program cannot continue....
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();



}

extern "C" void CUDARFPC_Target_Freq_EchoData(float* InR,
	float* InlocalAngle, 
	float* InampGain, 
	long* Indemcls, 
	long len,
	float Pt, float freq, 
	CUDASigmaParam* sigma0Paramslist, long sigmaparamslistlen, 
	cuComplex* OutechoArr

)
{
	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
	CUDARFPCKernel_Target_Freq_EchoData << <numBlocks, blockSize >> > (
		InR,
		InlocalAngle,
		InampGain,
		Indemcls,
		len,
		Pt, freq,
		sigma0Paramslist, sigmaparamslistlen,
		OutechoArr
		);
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDARFPC_Target_Freq_EchoData CUDA Error: %s\n", cudaGetErrorString(err));
		// Possibly: exit(-1) if program cannot continue....
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();

}


extern "C" void CUDA_DemEchoSUM_NoMalloc(cuComplex* d_dem_echo,long N,
	cuComplex* d_echosum_temp, int grid_size,
	cuComplex* d_echo,long ehcoid
) {
	long NUM_REPEATS = 100;
	const int smem = sizeof(float) * BLOCK_SIZE;
	for (long i = 0; i < grid_size; i++) { // 初始化
		d_echosum_temp[i] = make_cuComplex(0,0);
	}

	CUDACkernel_Complex_SUM_reduce_dynamicshared << <grid_size, BLOCK_SIZE, smem >> > (d_dem_echo, d_echosum_temp,N); //归约求和
#ifdef __CUDADEBUG__
	cudaError_t err = cudaGetLastError();
	if (err != cudaSuccess) {
		printf("CUDALinearInterp1 CUDA Error: %s\n", cudaGetErrorString(err));
		// Possibly: exit(-1) if program cannot continue....
	}
#endif // __CUDADEBUG__
	cudaDeviceSynchronize();

 
	for (int n = 0; n < grid_size; ++n)
	{
		d_echo[ehcoid] =cuCaddf(d_echo[ehcoid],d_echosum_temp[n]);
	}

}



#endif