The most common case I have using the FIR filtering functions is with complex time series data and real coefficients. However all of the functions I've found to use on complex data have complex filter coefficients. I've tried splitting the real and imaginary parts into two arrays, filtering each individually, then putting them back together into a complex array -- and I've converted the real coefficients to complex and performed a complex-complex FIR filter operation. Both yield the same answers. However the complex FIR is about 3 times the execution speed (which is a problem for me). It seems that having to split the real and imaginary parts and then putting them back together again should be totally unnecessary. Is there anyway around these extra steps while still getting the speed of doing scalar multiplies during the filter execution?
