Specific Emitter Identification Using IMF-DNA with a Joint Feature Selection Algorithm

Abstract

Specific emitter identification (SEI) is a technique to distinguish among different emitters of the same type using weak individual characteristics instead of conventional modulation parameters. The biggest challenge in SEI is to not only distinguish the different emitters with close modulation parameters but also to identify a specific emitter when its modulation parameters change significantly. For this paper, individual differences in pulse envelopes were investigated and four types of pulse envelopes were modeled. To exploit the individual features along with the pulse envelope for the identification of a specific emitter, an intrinsic mode function distinct native attribute (IMF-DNA) feature extraction algorithm and a joint feature selection (JFS) algorithm were proposed, which together constitute the final proposed SEI technique. Compared with four other feature selection methods, the proposed feature selection algorithm performed better for finding the most useful features for classification, which greatly helps in the reduction of feature dimension. Compared with radio frequency DNA (RF-DNA), IMF-DNA had a far superior correct emitter identification rate under similar conditions. A real data verification method was developed to verify the performance of IMF-DNA for specific emitter identification. The method achieved a correct identification rate of 85.3% at a sampling rate of 200 MHz and had an estimated signal-to-noise ratio (SNR) of approximately 10 dB.

Research on emitter individual identification technology based on Automatic Dependent Surveillance–Broadcast signal

“…The stray components produced by different emitters cannot be the same, that is, the intra-pulse UIM features are different; therefore, it is feasible to identify individuals based on the intrapulse UIM features. 17 First, the Hilbert transform is used to estimate the instantaneous frequency and phase curve in the intrapulse. Then, the instantaneous frequency curve is processed by means of de-averaging (de-straightening), the instantaneous phase curve is processed by de-skewing, and finally the features of unintentional frequency and phase modulation are extracted.…”

“…The stray components produced by different emitters cannot be the same, that is, the intra-pulse UIM features are different; therefore, it is feasible to identify individuals based on the intra-pulse UIM features. 17…”

A Nonlinear Fingerprint-Level Radar Simulation Modeling Method for Specific Emitter Identification

“…Then, the fingerprint feature is extracted to obtain the fine features containing the individual information of the emitter; finally, compared with the database, the specific emitter of the signal is determined by the classification and recognition algorithm, and the individual emitter is identified. In recent years, the theory and practical application of emitter individual identification technology are constantly improved, and the research of fingerprint feature extraction method has made great progress [4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Improved algorithm for de‐interleaving radar signals with overlapping features in the dynamically varying electromagnetic environment

“…Dudczyk and Wnuk [1] proposed the SEI method based on distinctive radiated emission features and built a database to detect target radar emission. On this basis, algorithms based on intra‐pulse features such as fractal features [2], pulse envelope [3], radio frequency (RF) fingerprint [4], spectral features [5] etc. are widely used in the SEI process and achieved good recognition effects.…”