Algorithms used to detect Wi-Fi transmitter transients are discussed in this paper, and the start of a Wi-Fi transmitter transient, in our opinion, has a new definition. Current algorithms, namely Variance Fractal Dimension Threshold Detection, Bayesian Step Change Detection and Phase Detection are analyzed at the beginning of this article. According to the disadvantages such as complexity, accuracy and a threshold needed in the final determination of these traditional methods, an improved algorithm based on Mean Change Point Detection is put forward. Threshold for determination and nonparametric estimation for hypothesis test are not needed in our improved approach, it detects the start of transient only by calculating the maximum of the difference of statistic. Moreover, the experimental results show that this improved method outperforms the other three methods, particularly in the case of low SNR.

Most of the existing fingerprint identification techniques are unable to distinguish different wireless transmitters, whose emitted signals are highly attenuated, long-distance propagating, and of strong similarity to their transient waveforms. Therefore, this paper proposes a new method to identify different wireless transmitters based on compressed sensing. A data acquisition system is designed to capture the wireless transmitter signals. Complex analytical wavelet transform is used to obtain the envelope of the transient signal, and the corresponding features are extracted by using the compressed sensing theory. Feature selection utilizing minimum redundancy maximum relevance (mRMR) is employed to obtain the optimal feature subsets for identification. The results show that the proposed method is more efficient for the identification of wireless transmitters with similar transient waveforms.

Recently, radio individual identification draws the attention of related research institutions because of its great significance in various fields. In this paper, transient envelope feature is applied to the identification of radio transmitters. A novel approach is proposed to extract the transient envelope feature of radio transmitters based on fitting. According to the characteristics of the radio signal, the transient envelope obtained from the signal by complex analytical wavelet transform, is fitted by Gaussian function and sinusoidal function. The fitting coefficients regarded as feature vector are used for individual identification. The results of data analysis indicate that both Gaussian fitting and sinusoidal fitting have a good recognition performance of identifying different radio transmitters.

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