Radar signal sorting is the key technology of electronic countermeasures. Extracting and supplementing other new feature parameters is the effective measures of solving the sorting problem of complex modulation radar signals. In view of the ambiguity function’s unique effect on characterizing signal inherent structure, this paper adopts the improved particle swarm optimization (PSO) algorithm to search the slice of ambiguity function main ridge of the considered signals, and then proposes a feature extraction method which bases on the local difference to extract three local area characteristics, that is the sum of characteristic value, the maximum characteristic value, and the characteristic value distribution entropy. These features can well reflect the local difference of the signal waveform structure. To verify the feasibility and effectiveness of the proposed method, three simulation experiments are designed and the fuzzy C-means algorithm is used to test the clustering performance of the extracted three feature parameters. The experimental results show that, when SNR is not lower than 0 dB, the average clustering accuracy rate of six kinds of the considered signals，i.e., LFM，BFSK，CON，QPSK，M-SEQ and BPSK, is 93.2% and the average accuracy of CON, LFM and BFSK signals achieves to 98.7%. When SNR changes from 0 dB to 20 dB, the average clustering accuracy rate keeps above 80.5%. Meanwhile, the time-effectiveness of the proposed model is better than those compared method. These results illustrate the good performance of the extracted local characteristics.