Recursive discrete Fourier transformation with unevenly spaced data

New recursive techniques for Fourier spectral analysis are reported, for which ongoing spectral estimates are generated from unevenly spaced data in real time. The algorithms are robust and computationally efficient, and are well suited to state variable form involving real number calculations. These methods are particularly attractive in filtering and signal processing applications where signals are not necessarily sampled at a uniform rate.