为新的ArrayFire版本调整MatchedFilter算法时出现问题

Question

我正在尝试将匹配的滤波器算法(此处给出(适用于3.6.4版本的arrayfire。以下是我最终得到的：

#include <arrayfire.h>
using namespace af;
struct SAR_data { //! SAR data structure format
double deltaF;  //! step size of frequency data (Hz)
double maxWr;   //! maximum scene size in range direction (m)
double maxWx;   //! maximum scene size in cross-range direction (m)
array minF;   //! (Np x 1): Vector containing the start frequency of 
//! each pulse (Hz)
array x_mat;  //! (Sx x Sy): the x-positions of each pixel (m)
array y_mat;  //! (Sx x Sy): the y-positions of each pixel (m)
array z_mat;  //! (Sx x Sy): the z-positions of each pixel (m)
array AntX;   //! (Np x 1): the x-position of the sensor at each pulse (m)
array AntY;   //! (Np x 1): the y-position of the sensor at each pulse (m)
array AntZ;   //! (Np x 1): the z-position of the sensor at each pulse (m)
array R0;     //! (Np x 1): the range to scene center (m)
array phdata; //! (K x Np): phase history data (frequency domain),
//! fast time in rows, slow time in columns
array im_final; //! (Sx x Sy): the complex image value at each pixel
};      
// matched filter realization using ArrayFire
void matched_filter_ArrayFire(SAR_data& data) {
double c = 299792458.0; // speed of light
// Determine the size of the phase history data
int K  = data.phdata.dims(0);  // # of frequency bins per pulse
int Np = data.phdata.dims(1);  // # of pulses
// Initialize the image with all zero values (complex)
data.im_final = constant<cdouble>(0, data.x_mat.dims(), c64);
array im_slices = constant<cdouble>(0, K, data.x_mat.dims(0),
data.x_mat.dims(1), c64);
array fspan = array(seq(0.0, K-1)) * data.deltaF;
cdouble unit = {0, 1};
for (int ii = 0; ii < Np; ii++) {
// compute differential range for each image pixel (m)
array dx = data.AntX(ii) - data.x_mat;
array dy = data.AntY(ii) - data.y_mat;
array dz = data.AntZ(ii) - data.z_mat;
array dR = sqrt(dx*dx + dy*dy + dz*dz) - data.R0(ii);
// calculate the frequency of each sample in the pulse (Hz)
array freq = data.minF(ii) + fspan;
array tt = data.phdata(span,ii);
// perform the Matched Filter operation
gfor (array jj, K) {
im_slices(jj,span,span) = 
tt(jj)*exp(unit*((double)(4.0*Pi/c)*freq(jj)*dR)); 
}
tt = sum(im_slices,0);
data.im_final = data.im_final + moddims(tt, data.x_mat.dims());
}
}

代码编译得很好，但当我尝试运行它时，它抛出了一个异常：

What() is:ArrayFire Exception (Invalid input size:203):
In function class af::dim4 __cdecl getOutDims(const class af::dim4 &,const class af::dim4 &,bool)
In file srcbackendcommonArrayInfo.cpp:130
Invalid dimension for argument 1
Expected: ldims == rdims
In function class af::array __cdecl af::operator -(const class af::array &,const class af::array &)
In file srcapicpparray.cpp:838

正如我所发现的，它抱怨以下几行：

array dx = data.AntX(ii) - data.x_mat;
array dy = data.AntY(ii) - data.y_mat;
array dz = data.AntZ(ii) - data.z_mat;

由于数据的维度。AntX(ii((其为1x1(和data.x_mat(其为Sx x Sy(不匹配。。不过，在旧版本的ArrayFire中，它曾经运行良好。显然，我需要一些方法来告诉ArrayFire"扩展"数据的价值。AntX(ii(对于data.x_mat的整个矩阵维度。但是如何做到呢？

Answer 1

使用@pradep:提示的算法的工作版本

// matched filter realization using ArrayFire
void matched_filter_ArrayFire(SAR_data& data) {
double c = 299792458.0; // speed of light
// Determine the size of the phase history data
int K  = data.phdata.dims(0);  // # of frequency bins per pulse
int Np = data.phdata.dims(1);  // # of pulses
// Initialize the image with all zero values (complex)
data.im_final = constant<cdouble>({0,0}, data.x_mat.dims(), c64);
array im_slices = constant<cdouble>({0,0}, data.x_mat.dims(0),
data.x_mat.dims(1), K, c64);
array fspan = array(seq(0.0, K-1)) * data.deltaF;
cdouble unit = {0, 1};
for (int ii = 0; ii < Np; ii++) {
// compute differential range for each image pixel (m)
array dx = tile(data.AntX(ii), data.x_mat.dims()) - data.x_mat;
array dy = tile(data.AntY(ii), data.y_mat.dims()) - data.y_mat;
array dz = tile(data.AntZ(ii), data.x_mat.dims()) - data.z_mat;
array dR = sqrt(dx*dx + dy*dy + dz*dz) - tile(data.R0(ii), dx.dims());
// calculate the frequency of each sample in the pulse (Hz)
array freq = tile(data.minF(ii), fspan.dims()) + fspan;
// perform the Matched Filter operation
gfor(seq jj, K) {
im_slices(span,span,jj) = tile(data.phdata(jj,ii), dR.dims())*
exp(unit*(double)(4.0*Pi/c)*tile(freq(jj), dR.dims())*dR); 
}
// sum over the last dimension
data.im_final += moddims(sum(im_slices,2),data.x_mat.dims());
}
}