Specific Emitter Identification is an important technology employed to identify the unique radio transmitter at the physical level regardless of specific information carried. RF fingerprint contains rich nonlinear characteristics and reflects the differences of internal components in radio transmitters. Radio frequency fingerprint extraction and identification has been extensively adopted to enhance the security of radio frequency communication in both military and civilian use. On the basis of time-frequency analysis of emitter signals, this paper proposes an individual emitter recognition algorithm based on square integral bispectrum features, and uses softmax classifier to classify and recognize five types of emitter individuals, and studies the influence of square integral bispectrum features dimensions and Gaussian noise on recognition results. The algorithm can effectively reduce the feature dimensions and greatly improve the correct recognition rate of emitter. The experiment results demonstrate the practicality of our proposal.

It is known that affinity propagation can perform exemplar-based unsupervised image clustering by taking as input similarities between pairs of images and producing a set of exemplars that best represent the images, and then assigning each nonexemplar image to its most appropriate exemplar. However, the clustering performance of affinity propagation is largely limited by the adopted similarity between any pair of images. As the scale invariant feature transform (SIFT) has been widely employed to extract image features, the nonmetric similarity between any pair of images was proposed by “hard” matching of SIFT features (e.g., counting the number of matching SIFT features). In this letter, we notice, however, that the decision of hard matching of SIFT features is binary, which is not necessary for deriving similarities. Hence, we propose a novel measure of similarities by replacing hard matching with the so-called soft matching. Experimental examples show that significant performance gains can be achieved by the resulting affinity propagation algorithm.

Specific emitter identification (SEI) can distinguish communication radio emitters with the fingerprint features carried by the received signal, and this technology has been widely used in military and civilian fields. However, in the real electromagnetic environment, the number of communication radio emitters is large and the signal‐to‐noise ratio (SNR) is low, which leads to poor nonlinear fingerprint analysis of SEI in a single domain. Therefore, combining the exploration of multiple domains of electromagnetic spatial information resources, this paper proposed a radio frequency (RF) fingerprint extraction method based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multidomain joint entropy. The proposed method is an attempt and exploration further extraction of nonlinear fingerprint features in multiple domains. Firstly, considering the nonstationarity of the communication signal, this article adopts the CEEMDAN method to decompose the signal to multiple intrinsic mode functions (IMF). Then, the decomposed signal is represented in multiple spaces by a multidimensional phase space reconstruction technique. Nonlinear analysis of the original signal is performed in multiple spaces: multidimensional differential approximate entropy space, singular spectral entropy space, and power spectral entropy space. Finally, the support vector machine (SVM) is adopted in the classification stage. To demonstrate the robustness of the method, the method is verified on the universal software radio peripheral (USRP) dataset and the Northeastern University public dataset. In terms of the identification accuracy, the proposed method performs with 98.5% accuracy on the 5‐class USRP dataset. It also performs with 94.7% accuracy on the 16‐class public dataset. The experimental results show that the proposed method has a stable identification performance and has a more than 85% recognition rate in the SNR above 5dB.

Pairwise classification is a task which predicts whether two samples belong to the same class or not. Boosting provides a way of combining many weak classifiers to produce a strong one and has been regarded as one of the most successful classification methodologies. The problem of pairwise classification is addressed by boosting decision stumps, the simplest weak classifier. Based on gentle AdaBoost, pairwise gentle AdaBoost of decision stumps is proposed to do pairwise classification. To make the classifier deal with a pair of inputs, sample‐weighted linear discriminant analysis (LDA) is proposed, which is tailored to boosting the framework. For pairwise classification, the proposed algorithm shows better performance than traditional boosting of decision stumps on two UCI data sets.