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Most research on drone detection and classification focus on using features from micro-Doppler signatures with blade flashes. However, these methods are limited in range and require radars with high pulse repetition frequency (PRF)–at least twice the maximum tip velocity. A different method to detect and classify drones at longer ranges using a low PRF radar is desired. In the literature, the cepstrum method was shown to be able to estimate the rotation rate when the PRF is insufficient. An alternative way of analyzing micro-Doppler is by using a long windowed Short-time Fourier transform (STFT) to generate HElicopter Rotation Modulation (HERM) lines. HERM lines exhibit similar behavior to a cepstrogram, with spectral lines separated in frequency by a value related to the rotation rate. In this paper, the separation frequency of HERM lines was estimated using a log harmonic summation algorithm. The proposed algorithm was tested on a simple HERM line model and also on real data obtained from two blade single rotor micro-helicopter drone. The algorithm was shown to be more resilient than cepstrum under Gaussian noise.