However, the system based on image and optical will face difficulty during weather conditions such as rain and snow. Other works including detecting a drone by listening to the reflected echo of transmitted continuous wireless signals and tracking the radio frequency (RF) transmission between the drone and its controlled point [6]. Cost-effective off the shelves (COTS) technologies involving wireless fidelity (WIFI) and software-defined radios (SDR) were implemented in the work. The author adopted three stages, i.e., detection, location, and mitigation of the RF communication link. The received signature was matched with the bank of signatures stored, using banded search software. The result seems to be successful, yet some challenges due to the new emerging drones may be faced. The problems may include some complications while operating with the 2.4-GHz industrial, scientific and medical (ISM) band, and some drones do not emit RF signals while operating. In contrast, the radar system is considered suitable due to its robustness in a day or night, noisy and blurred or misty environments [7,8]. However, a drone has peculiar characteristics that makes radar based detection difficult, for example:  
1. A drone has a low radar cross-section (RCS) due to its small size, shape, and construction material;
2. A drone flying at low speed and erratic flight path is difficult for conventional short-range radar to detect and measure;
3. A drone flying at low altitude difficult for conventional short/long-range radar, usually 122 m (400 ft);
4. A drone has similar behavior to a bird, making it difficult for classification and recognition. In an attempt to overcome these disadvantages, this paper proposes a passive FSR by utilizing a signal of opportunity from Digital Video Broadcasting Satellite (DVB-S) to detect a drone, which was briefly introduced in the conference paper [9]. This paper provides a comprehensive study on digital video broadcasting satellite (DVB-S) based passive FSR (p-FSR), and a numerical analysis on drone blade's radar cross section, full-scale experiment and result analysis, as well as new signal processing technique to detect a drone, was presented in the previous paper [10]. The key features to detect the drone in the DVB-S p-FSR rely on the received signal's Doppler frequency and its micro-Doppler scattered by the drones' blade. The proposed system will enjoy the advantages of:  
1. High RCS in the main lobe of forward-scatter region. Unlike normal radar, target RCS in FSR is practically independent of the radar absorbing material (RAM) coating, which means it can detect drone with stealth capability;
2. Long coherent intervals due to a low drone speed providing a high resolution (\(\Delta\)fFS = 1/\(\Delta\)TFS). This makes it capable of using inverse shadow synthetic aperture for automatic classification/recognition;
3. The integration of FSR into passive mode will make the proposed system covert and possibly low cost as there is no need to develop a dedicated transmitter;
4. The leakage/direct signal, which is 'unwanted' in conventional passive bistatic radar, is good for FSR for target detection due to direct signal perturbation;
5. DVB-S is known to have 24 h signal availability and a large coverage footprint. Thus, the system will have good coverage. Micro-Doppler is a vital feature that resulted from an additional frequency modulation generated from the rotation of the drone's blade. Detecting micro-Doppler is quite challenging due to its non-constant nature, mainly that the drone is engaged in multiple modes while flying. Because of the non-stationary and non-linear nature of the drone motion, the micro-Doppler became a time-varying component, thus traditional time frequency-processing techniques are non-optimal to extract micro-Doppler. For this reason, an improved signal processing technique is proposed based on empirical mode decomposition (EMD), to extract the unique feature vector from the received signal.