In this paper, we proposed a method for radar modulation identification based on the measurement of inequality in the frequency domain. Gini's coefficient was used to exploit the inequality in the powers of spectral components. The maximum likelihood classifier was used to classify the detected radar signal into four types of modulations: unmodulated signal (UM), linear frequency modulation (LFM), non-linear frequency modulation (NLFM), and frequency shift keying (FSK). The simulation results demonstrated that the proposed method achieves an overall identification accuracy of 98.61% at a signal-to-noise ratio (SNR) of -6dB without a priori information such as carrier frequency, pulse arrival times or pulse width.

Cognitive radios are intelligent communications, and are expected to more efficiently utilize the radio channel. Modulation identification is one of the key issues in the cognitive radios. Many works were devoted to the classification of symbol-by-symbol modulations, however, few papers on block modulations have been published. In this paper, an identification error analysis is presented for block orthogonal modulations using General Orthogonal Modulation~(GOM). A symbol error probability is derived for the identified block orthogonal modulation. Numerical results of 4-dimensional block orthogonal modulation are presented with simulation results.

In this paper, an automatic identification method based on frequency discrimination is proposed. The proposed method can be used when the received signal is a constant envelope modulation scheme. To test the proposed method PSK and FSK are considered. Using computer simulations, the performance of the proposed method was evaluated and found to be able to distinguish between PSK and FSK well even in the presence of noise.

A non-data aided carrier recovery technique using digital modulation format identification called multi-mode PLL (Phase Locked Loop) is proposed. This technique can be interpreted as a modulation identification method that is robust against static phase and frequency offsets. The performance of the proposed technique is studied and the analytical expressions are derived for the probability of lock detection, acquisition time over AWGN channel in the cases of M-PSK and M-QAM modulations with respect to frequency offset and signal-to-noise ratio.