Passive Forward-Scattering Radar Using Digital Video Broadcasting Satellite Signal for Drone Detection

The feasibility of an X-ray Forward Scatter Radar (FSR) system, which exploits X-ray pulsars as opportunity transmitters, is examined. We provide a power budget estimate for detection of large cosmic objects using such a FSR system and assume the use of the high-resolution X-ray telescopes mounted on a satellite as the receivers. The numerical results are obtained for three types of pulsars and small asteroids, whose diameters equal and overcome 50m.

In a forward scatter radar system, the forward scatter shadow ratio to characterize a target is introduced as the ratio of the total received power density to the incident power density for a receiver at a certain location. Numerical simulations show that the forward scatter shadow ratio is related to the size of the target as well as the position of the receiver relative to the target. It is a more suitable parameter for studying target detectability than the radar cross-section.

This paper explores the concept of a Forward Scattering Radar (FSR) system with a rotational transmitter for target detection and localization. Most of the research and development in FSR used a fixed dedicated transmitter; therefore, the detection of stationary and slow moving target is very difficult. By rotating the transmitter, the received signals at the receiver contain extra information carried by the Doppler due to the relative movement of the transmitter-target-receiver. Hence, rotating the transmitter enhances the detection capability especially for a stationary and slow-moving target. In addition, it increases the flexibility of the transmitter to control the signal direction, which broadens the coverage of FSR networks. In this paper, a novel signal processing for the new mode of FSR system based on the signal’s joint time-frequency is proposed and discussed. Additionally, the concept of the FSR system with the rotational transmitter is analyzed experimentally for the detection and localization of a stationary target, at very low speed and a low profile target crossing the FSR baseline. The system acts as a virtual fencing of a remote sensor for area monitoring. The experimental results show that the proposed mode with the new signal processing scheme can detect a human intruder. The potential applications for this system could be used for security and border surveillance, debris detection on an airport runway, ground aerial monitoring, intruder detection, etc.

The present work illustrates the Fraunhofer FHR contribution to the MAPIS project field trials held in Livorno in June 2017. The MAPIS project, funded by EDA, aims at demonstrating radar imaging capability of passive radar systems in a (military) maritime scenario. In particular, two different Passive Bistatic Radar (PBR) systems, one exploiting Digital Video Broadcasting - Terrestrial (DVB-T) signals, the other one exploiting Digital Video Broadcasting - Satellite (DVB-S) signals have been used to experimentally assess the imaging performance. This paper describes the systems developed by Fraunhofer FHR and used during the trials. In addition, a small overview of the trial itself is provided together with some preliminary results concerning the passive radar imaging.

This paper investigates the passive radar imaging system which exploits digital video broadcasting (DVB) satellites as opportunity illuminators. Firstly, we establish the imaging model and then analyze the imaging performance in the wavenumber domain. Because of the practical limitations, we are likely to get an undersampled wavenumber domain coverage so that traditional imaging methods such as matched filter (MF) would fail to provide good imaging performance. Therefore, we consider to use the sparse recovery techniques to achieve good inversion results. However, the compressive sensing (CS) imaging methods would be severely affected when off-grid scatterers exist. According to the theories of the Xampling and the finite rate of innovation (FRI), we propose a high-resolution sparse imaging method to overcome the problems above, which is based on the multiple signal classification (MUSIC) algorithm. Simulation results verify the effectiveness of the proposed method.

This paper presents a study to demonstrate the feasibility of using digital video broadcasting-satellite (DVB-S) signal for passive radar. The study concentrates on the analysis of DVB-S signal properties, mainly including its spectrum, resolution capability, ambiguity function, and analysis of direct path signal-to-noise ratio (SNR). Since DVB-S signal has a higher range resolution due to its wide bandwidth and wider coverage area, it turns out to be more competitive than other passive radar signals. Finally, results based on field measurements provide some useful references about the performance and potential capability of using DVB-S signal for passive radar applications.

The forward scatter signature of a target provides the radar systems designer with an alternative approach to the detection of 'very low radar cross section (RCS)' targets. This paper outlines the rationale for a forward scatter radar system, the forward scatter principle, and the architecture of a forward scatter radar systems performance model. This model has been developed to analyse the effect of variations in the choice of operating frequency, transmitter and receiver characteristics, detection and tracking algorithms. The paper discusses these aspects with results from modelling and performance predictions for target forward scatter signature, transmit and receive antenna designs, analogue to digital converter (ADC) performance and coverage for a forward scatter radar system operating in the ultra high frequency (UHF) band.

In this paper we consider a modified sub-optimal signal processing algorithm for marine target detection in a Forward Scatter Radar (FSR) system. The algorithm is based on a matched filtering approach where the filter parameters should be previously estimated. In contrast to the previously developed algorithms, the estimation of target parameters (time, velocity, length) is carried out by means of maximisation of the cross-correlation coefficient between frequencies of the input signal and the reference signal. The algorithm presented in this paper is verified by experimental data, and it can be also applied in some other bistatic radars. (5 pages)

Unmanned Aerial Vehicles (UAV), commonly known as drones, can fly below the radar horizon, can have very small radar cross-sections, and can drop munitions, which makes them a serious potential threat to public facilities, such as airports and shopping centers. However, UAVs emanate unique acoustic signatures that can be used to identify them and track their locations using microphone arrays. This paper presents a deep-learning approach to performing noise-robust drone detection and identification in real-time. In particular, a Convolutional Neural Network (CNN) with densely connected layers at the output was employed. Detection was accomplished by including an additional output for when only background environmental noise is present. The inputs were log-magnitude FFTs extracted from 10 ms Hanning windowed frames with 50% overlap. As long as the training data include a higher or equal level of noise than the target data, accuracy at detecting and identifying seven drones was above 90% for environmental noise levels as low as 20 dB SNR. Current research is focused on using a Bayesian version of the network to enable the classifier to report confidence levels in its decisions as well as detect novel drones, i.e., when a detected craft is not represented in the training data.

The Empirical Mode Decomposition (EMD) is becoming increasingly popular for the multi-scale analysis of signals. However, the data-driven and adaptive nature of the EMD raises concerns regarding the uniqueness of the decomposition as well as the extend to which oscillatory modes can be mixed across different IMFs. This paper proposes a solution to this problem for the analysis of ECG signals. The bivariate extension of the decomposition (BEMD) is used as the basis of an analysis in which a synthetic ECG signal of idealized waveform guides the decomposition of an input ECG segment. Essentially, this work provides the necessary ground for the deployment of signal processing algorithms on the ECG signal using a more robust EMD analysis.

Noise suppression: Empirical modal decomposition in non-dispersive infrared gas detection systems

This article presents results on drone detection in a passive radar framework, which exploits 4G long-term evolution signals. The results are based on an experiment using a noncooperative transmitter. The proposed processing was developed from a general passive radar algorithm, modified to account for both the specificities of long-term evolution signals and experimental observations. In particular, it is shown that the fluctuations of the ambiguity function can be mitigated by using the symbols containing pilots for range–Doppler, thus guaranteeing a more stable time– frequency content. This results in improved detection performance. An analysis of the eigenvalues of the covariance matrix, used for an angular–frequency clutter rejection, exhibits the presence of several distinct superimposed signals, complexifying the processing. In particular, it complicates the decoding of the reference signal, limiting the choices of the clutter rejection method. To address the rejection, the choice of an angular–frequency method rather than a temporal-based one (such as extended cancellation algorithm) is then discussed. Both approaches are applied to real data for comparison. Detection results are finally analyzed in various configurations, providing insights on experimental limitations.

The focus of this paper is on the detection of moving targets with a Forward Scatter Radar system. More precisely, a non-coherent detection strategy is considered and theoretical closed-form expressions of the probability of false alarm and of the probability of detection are derived in order to allow performance prediction and support system design. Finally the theoretical contribution is verified through simulations.

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

In power distributed generation system, analysis of power quality is a major issue and hence it is essential to implement an efficient power quality system. To address and end this issue, the proposed work is mainly focused on the detection and classification of power quality disturbances or events by applying Hilbert–Huang transform (HHT) technique. HHT technique is a novel signal processing algorithm that comprises two processes. First is the empirical mode decomposition (EMD), which is an iterative process where N number of power quality signals are decomposed into intrinsic mode functions (IMFs). The second process, Hilbert transform, is applied to each IMF component. The advantage of using these two processes makes it an attractive power tool for quality event analysis. Once the IMF is obtained, it is easy to construct a time–frequency representation model. Then, the significant features are extracted from amplitude, phase and frequency instantly, which are the contour of IMFs of each power quality disturbance. For classification, adaptive neuro-fuzzy system (NFS) classifier is used. It is an intelligent system used for the combination of neural network and fuzzy logic. Power quality features are given as input to the NFS system. The experimental simulation is conducted in a MATLAB environment, and proposed HHT has higher efficiency compared to existing methods.