Recursive Decimation/Interpolation for ML Chirp Parameter Estimation

We present a fast implementation of the optimal linear processor for “chirp” signals with O(N2) computational load for data length N. The method is technically an approximation since it has an interpolation step but we show that the errors are negligible. In simulations it performs up to 20 times faster than the existing method using radix-2 fast Fourier transform (FFT) and has significantly lower threshold signal-to-noise ratio (SNR) against noise and interference compared with a leading fast method.