## 1 Introduction  
Frequency hopping (FH) communication is one of the main types of spread spectrum communication [1]. The carrier frequency of the FH signal hops under the control of a pseudo-noise (PN) code, by which the spectrum is spread. FH communication systems have been widely used in military communications and covert communications due to their strong anti-interference ability and low probability of interception. Tracking and network sorting of multiple FH signals are important tasks to obtain useful information in communication reconnaissance. The tracking of FH signals refers to rapid estimation of the carrier frequency of the signal when the signal is hopping [2]. There are two aspects of FH signal tracking: detecting the timing of carrier frequency hopping, which means estimating the hop timing and estimating the frequency of the new hop. Thus, tracking of FH signals can be regarded as a real-time estimation of hop timing and frequency. The purpose of the network sorting is to separate multiple FH signals into their own sources [3]. Network sorting depends on estimating the unique parameters of each signal and the independence of signals. Thus, it is a problem of information processing based on the characteristics of signals. A number of blind signal tracking and sorting structures have been proposed for FH signals. One widely used method is spatial time-frequency analysis, which is usually utilized to describe sparse signals in the time-frequency domain [4]. In [5], the sparsity of the FH signal in the time-frequency domain was described and the parameters of multiple frequency hopping signals were estimated via sparse linear regression. In [6; 7; 8; 9; 10], the time of arrival (TOA), direction of arrival (DOA), frequency and other parameters were estimated based on time-frequency analysis. In [6; 7], joint signal parameter estimation of FH signals was proposed. In [8], a blind source separation approach exploiting the differences in time-frequency signatures of the sources to be separated was proposed, which is based on the diagonalization of a combined set of spatial time-frequency distributions and is applicable to FH signals. In [9], a kind of blind separation algorithm of FHSS signals with a time-frequency ratio of the matrix based on short-time Fourier transform (STFT) was proposed, which calculated the elements of the mixing matrix in the frequency domain and gave the solution of the inverse matrix directly. In [10], the proposed method uses the smoothed pseudo Wigner-Ville distribution (SPWVD) of each separated FHSS signal to estimate its transmission parameters and separates the signals by the joint approximate diagonalization of eigen-matrices (JADE) algorithm. In [4], a novel method to detect single source points in the time-frequency domain for separation was proposed, which also improves the subspace projection method to recover signal [4; 6; 7; 8; 9; 10]. These methods are intuitive and effective. It can be concluded that estimating parameters is a way to obtain the conditions for tracking and sorting. However, the methods in [4; 6; 7; 8; 9; 10] cannot process signals in real time, because they are all based on clustering information of a large amount of data. In many practical applications, such as jamming and dynamic reconnaissance, these methods do not satisfy the command. For FH signal tracking, [PERSON] and [PERSON] proposed a tracking model for FH signals, by which the frequency of multiple FH signals can be tracked in real time based on the temporal autoregressive moving average (ARMA) and sparse Bayesian learning (SBL) model [2; 11]. However, they suffer from enormous computing in practice. As a result, the computational complexity may exceed the capability of the hardware. To solve these problems, one method is to track and separate the signals depending on their irrelevance and another is to reduce the sampling rate of processing. As the network sorting of FH signals is a special case of the blind source separation (BSS) problem, some universal approaches for BSS are effective.