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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. [ABSTRACT FROM AUTHOR]

The existing parameter recognition algorithms for non-punctured polar codes default the frozen bit values to 0, and do not have the ability to judge the frozen bit values. Aiming at this problem, the parity check relation between codeword matrix and column vectors of generator matrix is deduced, where the identification of bit type and the judgment of frozen bit value are converted into a test of the check relationship. Based on the above, the average likelihood difference is introduced as a statistic volume, and the theoretical distribution and decision threshold of the statistic volume are given. The algorithm uses the proportion of information positions in the decision result to identify the parameters such as code rate and code length. Simulation experiments show that the derivation of the relevant properties is consistent with the actual situation. The proposed algorithm overcomes the shortcomings of existing methods that cannot identify frozen bit values on the basis of the same computational complexity level and without reducing the recognition performance. When the signal to noise ratio is 5. 5 dB, the recognition accuracy is still close to 100% for polar codes with a code length of 512 of the polar code. [ABSTRACT FROM AUTHOR]