## 1 Introduction  
In the signal processing of a radar reconnaissance system, radar emitter identification (REID) is one of the crucial aspects [1]. Recognition performance has become an important symbol to measure the technology levels of radar countermeasure equipment [2, 3]. With the wide application of modern radar and the degree increase of electronic radar countermeasures (there are all kinds of jamming), the electromagnetic environment is becoming more and more complex. The feature parameters, such as radio frequency (RF), pulse repeat interval (PRI), pulse wide (PW), angle of arrival (AOA), and pulse amplitude (PA) of radar emitter signals measured by reconnaissance equipment, are always illegible and uncertain, which bring out two problems in REID. One is that recognition performance may be decreased because of the uncertainty of the emitter signals. The other is the inability to update the emitter databases because there are no credible feature parameters. With the development of information technology, domestic and foreign scholars have done a lot of indepth and systematic studies in REID. At present, the research results can be divided into two categories. One is the intelligent classifier design, which can process the feature parameter uncertainties of the measured emitter signals and classify them correctly. Many conventional recognition classifiershave been successfully applied, such as fuzzy [4], Bayesian [5], k-nearest neighbor [6], support vector machine [7], neural network [8; 9; 10], and so on. They can solve the problem caused by measurement uncertainty or error to a certain extent. However, when there are no credible feature parameters for the classifier, these classification methods are also helpless. The other category is trying to extract a new feature parameter to make up for the shortcomings of the existing five feature parameters in emitter recognition. Presently, many studies are concerned with the existence of modulation on radar pulses (MOPs), such as entropy [11], resemblance coefficient [12; 13], wavelet gray matrix [14; 15; 16], and so on. However, when the two radars are deployed in close proximity and their signal parameters and modulation types are the same, these recognition methods based on the MOP feature are almost invalid. In this paper, the primary focus is on extracting a new feature parameter. In recent years, with the rapid development of modern radar technology, most radars in use are of the coherent system [17; 18]. The coherent system denotes that the initial phases of the transmitting signals from a coherent radar are continuous and stable, while those from a noncoherent radar are random. The correlation function [19; 20; 21] describes the correlation degree among the instantaneous values of random sample functions at different times, and coherence represents the phase relationship between the adjacent pulses, which is correlated and continuous. Therefore, the instantaneous autocorrelation function is introduced to distinguish the coherent characteristics of these pulses. In this paper, the feasibility of the coherent feature as a feature parameter for REID is demonstrated firstly by theoretical analysis. Then, the instantaneous autocorrelation function is taken to obtain the correlation differences between the coherent and noncoherent signals, and a new feature parameter named the ratio of the secondary peak value to the main peak value (SMR) is defined as the coherent feature to describe these differences. Through the simulation analysis, the simulation result shows that the new feature parameter (SMR) is not sensitive to signal-to-noise ratio (SNR) and signal type changes. To evaluate the recognition performance of the coherent feature, an analytical hierarchy process (AHP) is introduced, which makes a comprehensive evaluation from the three aspects of reliability, accuracy, and robustness of SMR and the existing parameters. Then, a sketch map of REID is constructed and applied in simulation experiments, according to the comprehensively evaluated results.