By using the sparsity of frequency hopping(FH) signals,an underdetermined blind source separation(UBSS) algorithm is presented. Firstly, the short time Fourier transform(STFT) is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival(DOA) of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio(SNR) is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions. By using the sparsity of frequency hopping (FH) signals, an underdetermined blind source separation (UBSS) algorithm is presented. The short time Fourier transform (STFT) is performed on the mixed signals. hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival (DOA) of the source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained . The results of the correlation coefficient between the estimated FH signal and the source signal are above 0.9 when the signal-to-noise ratio (SNR) is higher than 0 d B and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.