Review of radar classification and RCS characterisation techniques for small UAVs or drones

This review explores radar-based techniques currently utilised in the literature to monitor small unmanned aerial vehicle (UAV) or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration are briefly outlined. Key difficulties involved in the identification and hence tracking of these `radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and radar cross section (RCS) characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum

Tracking an air target in multistatic radar networks

The first radars used in military scenarios to detect enemies were bistatic because the technology that would allow a transmitter and a receiver to use the same antenna had not been developed. Then, with the development of monostatic radars, there was almost no interest in the bistatic radars subject. Nowadays, due to the fact that monostatic radars alone have reached its limits in terms of performance and because of the existence of new threats, the interest in bistatic and multistatic radars should last longer. Bistatic and multistatic radars are particularly interesting in military scenarios where it is important to be able to detect and track stealth targets and also to be able to operate with minimized risks of being affected by jamming attacks. This thesis investigates how much multistatic radars can surpass stand alone monostatic radars when attempting to track a target. Simulations with different geometries and different target trajectories are performed in order to assess the tracking performance in each scenario. Tracking performance is assessed in terms of estimated position, velocity and acceleration accuracies. Different geometries include monostatic radar, netted monostatic radars, bistatic radars with target crossing and not crossing the baseline, multistatic radars with only 1 TX and many RXs, multistatic radars with many TXs and only 1 RX and multistatic radars with many TXs and RXs. Simulations are performed using real radar characteristics in order to assess whether it is possible to use navigation radars to track targets with low RCS. The research herein presented shows that it is possible to achieve a good accuracy configuring a geometry that is suitable for the requirements of a system. Also, from the results of the simulations it is possible to understand why multistatic radars can still work with acceptable accuracy if a TXs is lost/destroyed

Methodology for the Development of Radar Control Systems for Flying Targets with an Artificially Reduced RCS

The article explores methods for detecting and tracking air targets in radar with an artificially reduced radar cross-section (RCS). A technique for control the radar antenna system using an adaptive method is presented, which is based on adapting the system to the conditions of information uncertainty due to fluctuations in the excitation signal of the radar target's radar-absorbing coating. Known methods of radar aircraft (active and passive, which do not take into account the structure of the coating of the aircraft) do not allow obtaining information about an air target with an artificially reduced RCS. The main contribution of this article is the development of radar and control methods based on the resonant frequency-phase interaction of the microwave electromagnetic field with the crystal structure of the radar absorbing coating of the aircraft. The stages of antenna system control based on the "frequency-phase detection method", "passive-active" tracking method, and "adaptive method" of antenna control have been studied. To test the proposed methods, an experiment was conducted to determine the transient process in the control system under conditions of information uncertainty. As a result of the experiment, the probability of tracking the target is increased by 14-19%. The findings will be useful for developers of radar and control systems for modern air facilities with an artificially reduced RCS

The potential of LIDAR as an antisubmarine warfare sensor

Traditionally, antisubmarine warfare (ASW) has been dominated by acoustic sensors, active and passive. Ending the Cold War, the ASW forces have refocused towards a theatre of war in the littorals, and the traditional acoustic sensors do not perform very well in such an environment. The sensors are working much closer to the surface, and there is a lot more surface traffic to disturb the acoustic environment. Environmental and topographic factors also play a major role. Removing or significantly reducing the acoustic capability, one forces the ASW forces to look to other technologies and sensors to compliment or replace the acoustic ones. This is where the interest of LIDAR as an aerial ASW sensor comes into play. The aim of this thesis is to evaluate “the potential for using LIDAR technology for aerial ASW on Norwegian ASW platforms”. In addition to this main research question, the history of LIDAR has been researched, in order to find historical and existing LIDAR projects for ASW purposes. Antisubmarine warfare is a complicated business, but speed of reaction, flexibility to change operating areas quickly and efficiently, and the ability to deploy sophisticated buoys are all in the advantage to the aerial ASW platform. But as the submarines get quieter and quieter, new means of detection must be found to cover the complicated upper layers of the water column. The signal components of LIDAR and the increasing processing capability have made LIDAR technology somewhat mature, but limitations such as scattering and attenuation of light in water are severely hampering. After a decline in ASW focus after the Cold War, the Western world is finding itself in a littoral submarine threat scenario, and do not have the sensors to sufficiently meet this threat. Several LIDAR programs have been initiated and carried through, but most have been directed towards finding and neutralizing mines. Lately, a new interest of applying LIDAR-technology in the search for submarines has risen. But LIDAR itself does not seem to be able to cover the upper layers of the water column consistently enough, and other technologies might be able to compliment LIDAR in a multi-sensor solution. Synthetic Aperture Radar (SAR) and Hyperspectral Imagery seem to be the most applicable of these. A recommendation is given to military commanders to pursue a multi-sensor pod for several areas of use by Maritime Patrol Aircraft and military helicopters

Wireless Localization Systems: Statistical Modeling and Algorithm Design

Wireless localization systems are essential for emerging applications that rely on context-awareness, especially in civil, logistic, and security sectors. Accurate localization in indoor environments is still a challenge and triggers a fervent research activity worldwide. The performance of such systems relies on the quality of range measurements gathered by processing wireless signals within the sensors composing the localization system. Such range estimates serve as observations for the target position inference. The quality of range estimates depends on the network intrinsic properties and signal processing techniques. Therefore, the system design and analysis call for the statistical modeling of range information and the algorithm design for ranging, localization and tracking. The main objectives of this thesis are: (i) the derivation of statistical models and (ii) the design of algorithms for different wire- less localization systems, with particular regard to passive and semi-passive systems (i.e., active radar systems, passive radar systems, and radio frequency identification systems). Statistical models for the range information are derived, low-complexity algorithms with soft-decision and hard-decision are proposed, and several wideband localization systems have been analyzed. The research activity has been conducted also within the framework of different projects in collaboration with companies and other universities, and within a one-year-long research period at Massachusetts Institute of Technology, Cambridge, MA, USA. The analysis of system performance, the derived models, and the proposed algorithms are validated considering different case studies in realistic scenarios and also using the results obtained under the aforementioned projects

Performance prediction and improvement of a Bistatic Passive coherent location Radar.

Passive Coherent Location (PCL) radar has proved to be feasible in a number of experimental systems, but the lack of comprehensive, published flight trials detracts somewhat from serious consideration of these PCL systems for operational applications, such as Air Traffic Control (ATC). The carrying out of flight trials is, in any case, difficult and very expensive. This dissertation presents a method for accurately predicting the performance of a bistatic passive coherent location radar with the effects of the environment taken into account. The effect of the environement on a propagating electromagnetic wave is obtained from the Advanced Refractive Effects Prediction System (AREPS) model. The resulting performance predictions, in the form of spatial signal-to-noise ratio (SNR), signal-to-interference ratio (SIR) and signal-to-noise-plus-interference ratio (SNIR) maps, provide a powerful planning tool for the application of systems such as ATC. Furthermore, the spatial coverage maps, based on the bistatic radar equation, can be related to a particular probability of detection and false alarm as well as to a required dynamic range of the receiver ADC. Overall, the method provides a visual, as well as a quantitative measure of radar coverage with region-specific atmospheric and terrain effects taken into account. The method proposed in this dissertation offers a marked improvement over traditional performance prediction methods based on the bistatic radar equation within a free space or flat terrain environment. It is understood that the direct path signal of the illuminating transmitter is the cause of some severe limitations within a PCL system. In the interest of suppressing the strong direct signal before the ADC and to complement the development of the prediction method, an antenna pattern was synthesised and applied to an array of folded dipoles in order to place a null in the direction of the strong transmitter. The synthesised antenna pattern and its improvement on the performance of the PCL system was then evaluated using the proposed prediction method presented in this dissertation

Drones Detection Using Smart Sensors

Drones are modern and sophisticated technology that have been used in numerous fields. Nowadays, many countries use them in exploration, reconnaissance operations, and espionage in military operations. Drones also have many uses that are not limited to only daily life. For example, drones are used for home delivery, safety monitoring, and others. However, the use of drones is a double-edged sword. Drones can be used for positive purposes to improve the quality of human lives, but they can also be used for criminal purposes and other detrimental purposes. In fact, many countries have been attacked by terrorists using smart drones. Hence, drone detection is an active area of research and it receives the attention of many scholars. Advanced drones are, many times, difficult to detect, and hence they, sometimes, can be life threatening. Currently, most detection methods are based on video, sound, radar, temperature, radio frequency (RF), or Wi-Fi techniques. However, each detection method has several flaws that make them imperfect choices for drone detection in sensitive areas. Our aim is to overcome the challenges that most existing drone detection techniques face. In this thesis, we propose two modeling techniques and compare them to produce an efficient system for drone detection. Specifically, we compare the two proposed models by investigating the risk assessments and the probability of success for each model

Advanced signal processing tools for ballistic missile defence and space situational awareness

The research presented in this Thesis deals with signal processing algorithms for the classification of sensitive targets for defence applications and with novel solutions for the detection of space objects. These novel tools include classification algorithms for Ballistic Targets (BTs) from both micro-Doppler (mD) and High Resolution Range Profiles (HRRPs) of a target, and a space-borne Passive Bistatic Radar (PBR) designed for exploiting the advantages guaranteed by the Forward Scattering (FS) configuration for the detection and identification of targets orbiting around the Earth.;Nowadays the challenge of the identification of Ballistic Missile (BM) warheads in a cloud of decoys and debris is essential in order to optimize the use of ammunition resources. In this Thesis, two different and efficient robust frameworks are presented. Both the frameworks exploit in different fashions the effect in the radar return of micro-motions exhibited by the target during its flight.;The first algorithm analyses the radar echo from the target in the time-frequency domain, with the aim to extract the mD information. Specifically, the Cadence Velocity Diagram (CVD) from the received signal is evaluated as mD profile of the target, where the mD components composing the radar echo and their repetition rates are shown.;Different feature extraction approaches are proposed based on the estimation of statistical indices from the 1-Dimensional (1D) Averaged CVD (ACVD), on the evaluation of pseudo-Zerike (pZ) and Krawtchouk (Kr) image moments and on the use of 2-Dimensional (2D) Gabor filter, considering the CVD as 2D image. The reliability of the proposed feature extraction approaches is tested on both simulated and real data, demonstrating the adaptivity of the framework to different radar scenarios and to different amount of available resources.;The real data are realized in laboratory, conducting an experiment for simulating the mD signature of a BT by using scaled replicas of the targets, a robotic manipulator for the micro-motions simulation and a Continuous Waveform (CW) radar for the radar measurements.;The second algorithm is based on the computation of the Inverse Radon Transform (IRT) of the target signature, represented by a HRRP frame acquired within an entire period of the main rotating motion of the target, which are precession for warheads and tumbling for decoys. Following, pZ moments of the resulting transformation are evaluated as final feature vector for the classifier. The features guarantee robustness against the target dimensions and the initial phase and the angular velocity of its motion.;The classification results on simulated data are shown for different polarization of the ElectroMagnetic (EM) radar waveform and for various operational conditions, confirming the the validity of the algorithm.The knowledge of space debris population is of fundamental importance for the safety of both the existing and new space missions. In this Thesis, a low budget solution to detect and possibly track space debris and satellites in Low Earth Orbit (LEO) is proposed.;The concept consists in a space-borne PBR installed on a CubeSaT flying at low altitude and detecting the occultations of radio signals coming from existing satellites flying at higher altitudes. The feasibility of such a PBR system is conducted, with key performance such as metrics the minimumsize of detectable objects, taking into account visibility and frequency constraints on existing radio sources, the receiver size and the compatibility with current CubeSaT's technology.;Different illuminator types and receiver altitudes are considered under the assumption that all illuminators and receivers are on circular orbits. Finally, the designed system can represent a possible solution to the the demand for Ballistic Missile Defence (BMD) systems able to provide early warning and classification and its potential has been assessed also for this purpose.The research presented in this Thesis deals with signal processing algorithms for the classification of sensitive targets for defence applications and with novel solutions for the detection of space objects. These novel tools include classification algorithms for Ballistic Targets (BTs) from both micro-Doppler (mD) and High Resolution Range Profiles (HRRPs) of a target, and a space-borne Passive Bistatic Radar (PBR) designed for exploiting the advantages guaranteed by the Forward Scattering (FS) configuration for the detection and identification of targets orbiting around the Earth.;Nowadays the challenge of the identification of Ballistic Missile (BM) warheads in a cloud of decoys and debris is essential in order to optimize the use of ammunition resources. In this Thesis, two different and efficient robust frameworks are presented. Both the frameworks exploit in different fashions the effect in the radar return of micro-motions exhibited by the target during its flight.;The first algorithm analyses the radar echo from the target in the time-frequency domain, with the aim to extract the mD information. Specifically, the Cadence Velocity Diagram (CVD) from the received signal is evaluated as mD profile of the target, where the mD components composing the radar echo and their repetition rates are shown.;Different feature extraction approaches are proposed based on the estimation of statistical indices from the 1-Dimensional (1D) Averaged CVD (ACVD), on the evaluation of pseudo-Zerike (pZ) and Krawtchouk (Kr) image moments and on the use of 2-Dimensional (2D) Gabor filter, considering the CVD as 2D image. The reliability of the proposed feature extraction approaches is tested on both simulated and real data, demonstrating the adaptivity of the framework to different radar scenarios and to different amount of available resources.;The real data are realized in laboratory, conducting an experiment for simulating the mD signature of a BT by using scaled replicas of the targets, a robotic manipulator for the micro-motions simulation and a Continuous Waveform (CW) radar for the radar measurements.;The second algorithm is based on the computation of the Inverse Radon Transform (IRT) of the target signature, represented by a HRRP frame acquired within an entire period of the main rotating motion of the target, which are precession for warheads and tumbling for decoys. Following, pZ moments of the resulting transformation are evaluated as final feature vector for the classifier. The features guarantee robustness against the target dimensions and the initial phase and the angular velocity of its motion.;The classification results on simulated data are shown for different polarization of the ElectroMagnetic (EM) radar waveform and for various operational conditions, confirming the the validity of the algorithm.The knowledge of space debris population is of fundamental importance for the safety of both the existing and new space missions. In this Thesis, a low budget solution to detect and possibly track space debris and satellites in Low Earth Orbit (LEO) is proposed.;The concept consists in a space-borne PBR installed on a CubeSaT flying at low altitude and detecting the occultations of radio signals coming from existing satellites flying at higher altitudes. The feasibility of such a PBR system is conducted, with key performance such as metrics the minimumsize of detectable objects, taking into account visibility and frequency constraints on existing radio sources, the receiver size and the compatibility with current CubeSaT's technology.;Different illuminator types and receiver altitudes are considered under the assumption that all illuminators and receivers are on circular orbits. Finally, the designed system can represent a possible solution to the the demand for Ballistic Missile Defence (BMD) systems able to provide early warning and classification and its potential has been assessed also for this purpose