## 1 Introduction  
Global navigation satellite system (GNSS) is the most widely used navigation system. It uses satellites to broadcast positioning signals and provides positioning, navigation, and timing services for worldwide users. However, it also has some problems: (1) The signal landing power is about \(-130\) dBm, which is easy to be interfered and spoofed. (2) The signal is easy to be blocked by obstacles, making it difficult to use in dense urban areas or indoor environments [1]. In perspective of the above-mentioned problems of GNSS, an ever-increasing amount of researchers have begun to explore reliable positioning methods that do not rely on GNSS systems. SOP navigation utilizes all potential wireless signals in the surroundings for positioning [2]. SOP include various signals that are not specifically designed for navigation, such as digital audio broadcasting (DAB), digital video broadcasting (DVB), amplitude modulation radio (AM) and frequency modulation radio (FM), cellular signals, Bluetooth, ZigBee, Wi-Fi, and other wireless signals [3]. These signals are widespread and usually used for communication rather than navigation. We can extract useful information from SOP such as signal strength, ranging and time information for navigation. Common types of SOP are demonstrated in Table 1.  
The process of SOP positioning can be roughly divided into signal perception, data preprocessing, information extraction, and positioning solution [4]. Effectively identifying the SOP is the primary task of SOP navigation. It can be seen from the Table 1 that there are many types of opportunistic signals, with different distribution frequency bands, bandwidths, and signal modulation methods, which brings difficulties to signal perception. The common signal perception methods include coherent detection, energy detection, cyclostationary feature detection, etc. [5]. The schematic flow charts of three common detection methods as indicated in Figure 1.  
* Coherent detection [6]:  
Matched filters are a common way for coherent detection. Its advantages are high recognition efficiency and high accuracy with a short detection time, so in a sense it might be said to be an optimal detector; the disadvantage is that the relatively high computational complexity and needs prior information about the SOP, such as modulation method, modulation order, pulse waveform, data packet format, etc. It also needs time, carrier, and even channel synchronization to enable correlation with the signal, which is complicated to implement for SOP system. For different types of signals, special receivers are required too. * Energy detection [7; 8; 9]:  
Most signals are broadcast with a fixed frequency. By detecting the energy of the specific frequency, we can judge whether the corresponding signal exists. This method is simple to implement, has strong adaptability, and does not require more prior signal information. To measure the energy of a signal at a certain frequency, the signal output by the band-pass filter with bandwidth W is squared and integrated over the observation time. Then the calculated energy value is compared with a threshold to determine the existence of signal. The energy detection algorithm has low complexity, but the threshold is easily affected by changes in noise power and becomes invalid. Meanwhile this algorithm is not suitable for direct sequence spread spectrum (DSSS) signal, frequency hopping signals, and co-band signals (e.g., Industrial Scientific Medical (ISM) band). * Cyclostationary feature detection [10; 11]:  
Communication signals usually include carrier frequencies, frequency hopping sequences, cyclic prefixes, etc., which make the signal statistical characteristics such as the mean value and correlation function periodic. However, noise does not have this characteristic, which can use to separate the noise from the target signal. This method has good detection performance even in the case of a low signal-to-noise ratio. The drawback of this method are higher complexity and longer detection time.