The existing Frequency-Hopping(FH) signal processing algorithms often require sufficient data, therefore cannot meet the need of real-time operation or require handling the FH signal with high hopping speed. In order to process FH signals with few samples, a real-time tracking and parameter estimation method is proposed. According to the sparsity in frequency domain, Sparse Bayesian Learning(SBL) is introduced to reconstruct Multiple Measurement Vector(MMV). By constructing new statistic parameter, a hop timing detecting method with constant false alarm probability is derived. Then FH signals can be tracked dynamically according to a sliding strategy. Finally, the proposed method estimates the carrier frequency and Direction-Of-Arrival(DOA) by gravity of geometric center and least square method respectively. Experiments show that the proposed method has lower false alarm probability under low Signal-to-Noise Ratio(SNR), and improves the accuracy of parameter estimation remarkably.