Research of radar target detection algorithm based on FRFT. Journal of University of Science and Technology of China, 2010, 40(11): 1142⁃1147. An outlier removal method based on WTMM and multifractal. Algorithm complexity analysis of radar emitter characteristic parameter extraction. Deinterleaving models and algorithms for advanced radar emitter signals.Chengdu: Southwest Jiaotong University,2007. Extracting slice of ambiguity function main ridge using improved particle swarm optimization. An improved PSO algorithm and its application in fast feature extraction of radar emitter signals//Proceedings of the IEEE International Conference on Natural Computation. An improve PSO based hybrid algorithms//Proceedings of IEEE International Conference on Management and Service Science. Parameter selection in particle swarm optimization//Proceedings of the 7th Annual Conference on Evolutionary Programming. Kunming: Kunming University of Science and Technology,2013. Intelligent search method and selection feature extraction of AFMR of radar emitter signal. Specific emitter identification based on ambiguity function. Extracting the main ridge slice characteristics of ambiguity function for radar emitter signals.
On methods for Specific Radar Emitter Identification. Novel radar signal sorting method with low complexity. Feature extraction of radar emitter signals based on time⁃frequency atoms. Emitter signals recognition based on wavelet packet transform.Signal Processing, 2005, 21(5): 460⁃464. Research on key technologies of radar signal sorting. Radar emitter signal ambiguity function main ridge slice improved PSO local difference These results illustrate the good performance of the extracted local characteristics. Meanwhile, the time⁃effectiveness of the proposed model is better than those compared method. When SNR changes from 0 dB to 20 dB, the average clustering accuracy rate keeps above 80.5%. 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%. 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. These features can well reflect the local difference of the signal waveform structure. 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.
Extracting and supplementing other new feature parameters is the effective measures of solving the sorting problem of complex modulation radar signals. Radar signal sorting is the key technology of electronic countermeasures.
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