## 1 Introduction
For instance, independent component analysis (ICA) has better results on stationary and independent signals [12; 13; 14]. In [12], the ICA algorithm was introduced, which can be considered to be a foundation for further separation methods. In [13; 14], a FastICA algorithm was introduced for separation, which is more efficient than the traditional ICA. The correlation coefficient was taken as the evaluation criterion of the separation performance. Although these separation methods based on ICA do not need to obtain the array structure beforehand, they constrain the number of sources to be less than the number of sensors. In many practical applications, this condition is not satisfied. Furthermore, these methods cannot take advantage of sparsity in the processing and the signal-to-noise ratio (SNR) is poor. Recently, tensor decomposition [15], subspace-based projection [3] and neural network [16] were used for sorting FH signals. However, all of these methods start from the premise that the total sampling rate of channels or array receivers should meet or exceed the Nyquist sampling rate. The sampling rates of analog-to-digital converters (ADCs) can be relatively high to meet the Nyquist theorem. Meanwhile, large amounts of data must be cached for calculation and the amount of computations for further digital processing is enormous. Compressed sensing (CS) has been applied for wideband sparse signal processing to overcome the obstacle of high sampling rate [17]. Using CS to separate signals that are sparse in the time-frequency domain, such as radar, linear frequency modulation (LFM) and FH signals, is a hot topic in recent research [18; 19; 20; 21; 22]. The core idea of these methods is to recover the time-frequency representation of signals. Thus, the sparse signals can be separated in the time-frequency domain. However, these methods cannot process the signals in real time and certain time-frequency information is required in advance, because the signals must be recovered before further processing and it takes a lot of computation. It can be concluded that CS has great advantages in practical applications, but studies on the tracking and separation of FH signals using CS are scarce and not well-rounded. The major difficulties in using the classical CS theory for the tracking and sorting of FH signals are as follows:  
1. The traditional methods are not suitable for FH signals due to sparsity. 2. The structure of the traditional sensing matrix is planned and cannot be configured according to the signals. 3. The CS structures change the phase and amplitude characteristics of the signals. As a result, the performance of the method based on these parameters cannot be guaranteed. 4. The traditional CS structures require caching data for recovery, which is not suitable for real-time signal processing. Nowadays, the problem of tracking time-varying sparse signals based on sparse signal processing and dynamic CS has been tackled. Dynamic CS has been applied in dynamic magnetic resonance imaging (MRI) [23], underwater channel estimation [24], and radar imaging [25]. In [26; 27], tracking and dynamic filtering of time-varying sparse signals are analyzed under certain assumptions. In [28; 29], a series of universal approaches were proposed for online optimization in dynamic environments. These methods transform the CS of time-varying sparse signals to online convex optimization, which proceed from the perspective of regret analysis. For FH signals, a time-frequency analysis based on sparse recovery was proposed and the unconstrained sparse representation model of FH signals was established according to the punish function theory [30]. However, these dynamic CS methods have difficulties processing FH signals. First, since the carrier frequency of FH signals is under the control of a PN code, the iterations of solving the optimization problem will increase under the condition of full blindness, which will affect the real-time tracking. Secondly, the punish function cannot adapt to the changes in signal sparsity.