Multiple receiver specific emitter identification

Abstract

Specific emitter identification (SEI) is a technique for identifying emitters based on the principle that the hardware chain is not ideal, causing the emitted signal to contain emitter-specific information. However, the receiver is also non-ideal, which affects recognition accuracy and introduces receiver-specific information that makes SEI difficult to generalise across receiving systems. In this work, a new multi-receiver receiving and processing system (MR-SEI) scheme is proposed to mitigate the influence of receivers based on the analysis of receiver distortion models. After receiving and processing in a specific manner, recognition performance can be enhanced. Therefore, extracted features can be shared among different receivers and platforms, and can even be applied to newly added receivers. The concept of common waveform (CW) is first defined, referring to the received signal without receiver distortions. Different receiving devices are working synchronously, and the CW is estimated using multiple copies of the signal obtained from multiple receivers through the iterative reweighted least squares (IRLS) method. For each receiver, a maximum linear correlation algorithm is proposed to calculate the received signal without being affected by distortions. Experimental results show that the proposed scheme can enhance identification performance. With the increase in the number of receivers, the improvement is more noticeable. Using 10 distorted receivers operating under an SNR of 25 dB, the proposed algorithm can significantly improve the identification performance, achieving over 95% and approaching the ideal scenario of no receiver distortion. Meanwhile, influences caused by receiver distortions can be effectively eliminated, and the database can be shared with new receivers, overperforming other SEI methods that eliminate the receiver.