This paper presents a passive radar system using a signal of opportunity from Digital Video Broadcasting Satellite (DVB-S). The ultimate purpose of the system is to be used as an air traffic monitoring and surveillance system. However, the work focuses on drone detection as a proof of the concept. Detecting a drone by using satellite-based passive radar possess inherent challenges, such as the small radar cross section and low speed. Therefore, this paper proposes a unique method by leveraging the advantage of forward-scattering radar (FSR) topology and characteristics to detect a drone; in other words, the system is known as a passive FSR (p-FSR) system. In the signal-processing algorithm, the empirical mode decomposition (EMD) is applied to the received signal to extract the unique feature vector of the micro-Doppler frequency from the drone’s rotating blades. The paper highlights the p-FSR experimental setup and experiment campaign to detect drones. The experimental results show the feasibility of the p-FSR using a signal transmitted from a satellite to detect flying drone crossing the forward-scatter baseline between the satellite and ground station.

Human echolocation is a biological process wherein the human emits a punctuated acoustic signal, and the ear analyzes the echo in order to perceive the surroundings. The peculiar acoustic signal is normally produced by clicking inside the mouth. This paper utilized this unique acoustic signal from a human echolocator as a source of transmitted signal in a synthetic human echolocation technique. Thus, the aim of the paper was to extract information from the echo signal and develop a classification scheme to identify signals reflected from different textures at various distance. The scheme was based on spectral entropy extracted from Mel-scale filtering output in the Mel-frequency cepstrum coefficient of a reflected echo signal. The classification process involved data mining, features extraction, clustering, and classifier validation. The reflected echo signals were obtained via an experimental setup resembling a human echolocation scenario, configured for synthetic data collection. Unlike in typical speech signals, extracted entropy from the formant characteristics was likely not visible for the human mouth-click signals. Instead, multiple peak spectral features derived from the synthesis signal of the mouth-click were assumed as the entropy obtained from the Mel-scale filtering output. To realize the classification process, K-means clustering and K-nearest neighbor processes were employed. Moreover, the impacts of sound propagation toward the extracted spectral entropy used in the classification outcome were also investigated. The outcomes of the classifier performance herein indicated that spectral entropy is essential for human echolocation.

The rise of unmanned aerial vehicles (UAVs) poses significant airspace security challenges, particularly concerning radar detection capabilities. This study examines the radar cross-section (RCS) of drones operating in LTE frequency bands (2.4 GHz and 5.2 GHz), using the DJI Phantom 3 as a case study. Simulations conducted in CST Studio Technology reveal that payload weight substantially affects RCS, with increased loads resulting in higher radar visibility. Additionally, angle-dependent analyses demonstrate significant variations in RCS based on the angle of incidence, identifying critical angles for enhanced detectability. These findings underscore the necessity for advanced radar detection strategies that consider payload impacts and angle-specific characteristics of RCS. The study's implications extend to the optimization of UAV designs for reduced radar signatures, informing both manufacturers and regulatory frameworks. This research contributes to the development of effective airspace monitoring systems, emphasizing the need for adaptive detection methodologies as UAV usage continues to expand.

The importance of automatic vehicle detection and classification has grown significantly in recent years, as it has become a crucial component of traffic management and monitoring systems. To overcome the limitations of traditional video vehicle detection, this paper proposes the use of forward scatter radar (FSR) technology. The FSR system is tested for the classification of four different vehicle types, each with distinct sizes. To improve the classification accuracy of the FSR system, the paper utilizes a well-established neural network known as a convolutional neural network (CNN). Two time-frequency analyses, continuous wavelet transform (CWT) and short-time Fourier transform (STFT), are used to evaluate the classification performance of the FSR system. The study demonstrates that the CNN classifier significantly improves the classification accuracy of the FSR system in vehicle detection and classification. This finding is supported by the evaluation of the time-frequency analyses, CWT and STFT. Overall, the proposed approach has the potential to enhance traffic management and monitoring systems, thereby improving road safety and traffic efficiency.