Although classical cipher is simple and efficient, but it has a serious defect of being cracked easily under the current social computing power. A new classical cipher model based on rough sets was developed to solve this problem. Firstly, two features of rough sets were integrated into the model to weaken the statistical law of the model. One feature is that certainty contains uncertainty in rough sets, another is that the approximate space scale tends to increase sharply with the slight increase of the domain size. Secondly, the ability of producing random sequences of the model was improved by using mixed congruence method. Finally, part of plaintext information was involved in the encryption process by using self-defined arithmetic and congruence method to enhance the anti-attack ability of the model. The analysis shows that the model not only has the same level of time and space complexity as traditional classical cipher, but also has nearly ideal performance of diffusion and confusion, which completely overcomes the defects that classical cipher can be easily cracked, and can effectively resist the attacks such as exhaustive method and statistical analysis method.

Concerning the problem of detecting unknown Linear Frequency Modulated (LFM) signal, according to the feature that the phase of the signal is stable, a LFM signal detection method based on the frequency domain phase variance weighting was proposed. The proposed method utilized the characteristics that the phase of LFM signal frequency unit was stable, and the phase of noise frequency unit was random, to weight each frequency unit by the phase variance, which could further restrain the background noise energy disturbances, enhanced the Signal-to-Noise Ratio (SNR) gain of signal detection, and achieved detecting unknown LFM signal. Under simulation conditions, when the input average Spectrum Level Ratio (SLR) was greater than -10 dB, compared with phase difference alignment method, the output average SLR of the proposed method was further improved, and with the input the average SLR became higher, the output SLR was further improved. The theoretical analysis and experimental results show that the proposed method can well enhance the energy of LFM signal, restrain the background noise energy, and improve SNR.