Statistical assessment on Non-cooperative Target Recognition using the Neyman-Pearson statistical test

Electromagnetic simulations of a X-target were performed in order to obtain its Radar Cross Section (RCS) for several positions and frequencies. The software used is the CST MWS©. A 1 : 5 scale model of the proposed aircraft was created in CATIA© V5 R19 and imported directly into the CST MWS© environment. Simulations on the X-band were made with a variable mesh size due to a considerable wavelength variation. It is intended to evaluate the Neyman-Pearson (NP) simple hypothesis test performance by analyzing its Receiver Operating Characteristics (ROCs) for two different radar detection scenarios - a Radar Absorbent Material (RAM) coated model, and a Perfect Electric Conductor (PEC) model for recognition purposes. In parallel the radar range equation is used to estimate the maximum range detection for the simulated RAM coated cases to compare their shielding effectiveness (SE) and its consequent impact on recognition. The AN/APG-68(V)9’s airborne radar specifications were used to compute these ranges and to simulate an airborne hostile interception for a Non-Cooperative Target Recognition (NCTR) environment. Statistical results showed weak recognition performances using the Neyman-Pearson (NP) statistical test. Nevertheless, good RCS reductions for most of the simulated positions were obtained reflecting in a 50:9% maximum range detection gain for the PAniCo RAM coating, abiding with experimental results taken from the reviewed literature. The best SE was verified for the PAniCo and CFC-Fe RAMs.Simulações electromagnéticas do alvo foram realizadas de modo a obter a assinatura radar (RCS) para várias posições e frequências. O software utilizado é o CST MWS©. O modelo proposto à escala 1:5 foi modelado em CATIA© V5 R19 e importado diretamente para o ambiente de trabalho CST MWS©. Foram efectuadas simulações na banda X com uma malha de tamanho variável devido à considerável variação do comprimento de onda. Pretende-se avaliar estatisticamente o teste de decisão simples de Neyman-Pearson (NP), analisando as Características de Operação do Receptor (ROCs) para dois cenários de detecção distintos - um modelo revestido com material absorvente (RAM), e outro sendo um condutor perfeito (PEC) para fins de detecção. Em paralelo, a equação de alcance para radares foi usada para estimar o alcance máximo de detecção para ambos os casos de modo a comparar a eficiência de blindagem electromagnética (SE) entre os diferentes revestimentos. As especificações do radar AN/APG-68(V)9 do F-16 foram usadas para calcular os alcances para cada material, simulando uma intercepção hostil num ambiente de reconhecimento de alvos não-cooperativos (NCTR). Os resultados mostram performances de detecção fracas usando o teste de decisão simples de Neyman-Pearson como detector e uma boa redução de RCS para todas as posições na gama de frequências selecionada. Um ganho de alcance de detecção máximo 50:9 % foi obtido para o RAM PAniCo, estando de acordo com os resultados experimentais da bibliografia estudada. Já a melhor SE foi verificada para o RAM CFC-Fe e PAniCo

A simulation of the single scan accuracy of a two-dimensional pulsed surveillance radar

Bibliography: leaves 194-198.The following dissertation considers the single-scan two-dimensional positional accuracy of a pulsed surveillance radar. The theoretical aspects to the positional accuracy are considered and a generalized analytical approach is presented. Practical position estimators are often complex, and theoretical predictions of their performance generally yield unfriendly mathematical equations. In order to evaluate the performance of these estimators, a simulation method is described based on replicating the received video signal. The accuracy of such a simulation is determined largely by the accuracy of the models applied, and these are considered in detail. Different azimuth estimation techniques are described, and their performances are evaluated with the aid of the signal simulation. The best azimuth accuracy performance is obtained with the class of analogue processing estimators, but they are found to be more susceptible to interference than their binary processing counterparts. The class of binary processing estimators offer easily implemented techniques which are relatively insensitive to radar cross-section scintillation characteristics. A hybrid estimator, using both analogue and binary processing, is also evaluated and found to give an improved accuracy performance over the binary processing method while still maintaining the relative insensitivity to radar cross-section fluctuation

Practical classification of different moving targets using automotive radar and deep neural networks

In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed

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

Two-Step Detection Algorithm for Fluctuating Weak Target Based on Dynamic Programming

Multi-frame data are processed simultaneously in Track-before-Detect (TBD) algorithm, which is an effective means to improve signal-to-noise ratio. However, some key factors, such as fluctuation loss and multi-frame joint threshold, are neglected when detecting weak target, which leading to detection performance loss inevitably. In order to address the above problems, a novel dynamic programming-TBD (DP-TBD) algorithm based on two-step thresholds is proposed in this paper. Firstly, the multi-frame accumulation amplitude for the observation scene is recalculated based on fluctuation loss analysis and measurement updating, as a result, the accumulation amplitude is closer to the real situation. Then, the first level threshold based on multi-frame data is achieved through the proposed threshold setting method, which avoiding the disadvantage of CFAR algorithm in which the false alarm trajectory cannot be separated effectively from the real target trajectory. Finally, the quantity for false alarm trajectory is decreased further by setting the second level thresholds, which depending on the distribution characteristics of the residual false alarm trajectories. The proposed algorithm takes full advantage of multi-frame joint detection for fluctuating weak targets, which giving consideration to both detection performance and false alarm performance. Simulation results show the effectiveness of the proposed algorithm

Lane and Road Marking Detection with a High Resolution Automotive Radar for Automated Driving

Die Automobilindustrie erlebt gerade einen beispiellosen Wandel, und die Fahrerassistenz und das automatisierte Fahren spielen dabei eine entscheidende Rolle. Automatisiertes Fahren System umfasst haupts\"achlich drei Schritte: Wahrnehmung und Modellierung der Umgebung, Fahrtrichtungsplanung, und Fahrzeugsteuerung. Mit einer guten Wahrnehmung und Modellierung der Umgebung kann ein Fahrzeug Funktionen wie intelligenter Tempomat, Notbremsassistent, Spurwechselassistent, usw. erfolgreich durchf\"uhren. F\"ur Fahrfunktionen, die die Fahrpuren erkennen m\"ussen, werden gegenw\"artig ausnahmslos Kamerasensoren eingesetzt. Bei wechselnden Lichtverh\"altnissen, unzureichender Beleuchtung oder bei Sichtbehinderungen z.B. durch Nebel k\"onnen Videokameras aber empfindlich gest\"ort werden. Um diese Nachteile auszugleichen, wird in dieser Doktorarbeit eine \glqq Radar\textendash taugliche\grqq{} Fahrbahnmakierungerkennung entwickelt, mit der das Fahrzeug die Fahrspuren bei allen Lichtverh\"altnissen erkennen kann. Dazu k\"onnen bereits im Fahrzeug verbaute Radare eingesetzt werden. Die heutigen Fahrbahnmarkierungen k\"onnen mit Kamerasensoren sehr gut erfasst werden. Wegen unzureichender R\"uckstreueigenschaften der existierenden Fahrbahnmarkierungen f\"ur Radarwellen werden diese vom Radar nicht erkannt. Um dies zu bewerkstelligen, werden in dieser Arbeit die R\"uckstreueigenschaften von verschiedenen Reflektortypen, sowohl durch Simulationen als auch mit praktischen Messungen, untersucht und ein Reflektortyp vorgeschlagen, der zur Verarbeitung in heutige Fahrbahnmakierungen oder sogar f\"ur direkten Verbau in der Fahrbahn geeignet ist. Ein weiterer Schwerpunkt dieser Doktorarbeit ist der Einsatz von K\"unstliche Intelligenz (KI), um die Fahrspuren auch mit Radar zu detektieren und zu klassifizieren. Die aufgenommenen Radardaten werden mittels semantischer Segmentierung analysiert und Fahrspurverl\"aufe sowie Freifl\"achenerkennung detektiert. Gleichzeitig wird das Potential von KI\textendash tauglichen Umgebungverstehen mit bildgebenden Radardaten aufgezeigt

Short Study on Performances of Air Surveillance Augmented by Twin Radars

Detection of targets at low altitudes with low Radar Cross Section (RCS) such as drones and stealth could challenge Air Surveillance systems and this situation must be addressed in a cost-effective way. This paper summarizes the newly emerged technical challenges of the Control and Reporting Centre (CRC) and gives solutions how to overcome recognized shortcomings, highlights problems and requirements related to drones maneuvering capabilities and their very low RCS. This article summarizes challenges and gives a synthesized solution to the network centric approach based on the short distance Gaussian bi-static radar concept called twin radar coherent signal fusion. The idea is to explore advantages of the twin radar concept such as doubled detection range, increased, superior angular resolution of the system and increased plot update frequency. Examples for the twin radar performance demonstration are based on VHF radar, which has unique capabilities such as exploitation of multipath and resonance frequency for drones. A few peculiarities of the new signal processing are introduced with the extended air traffic control and monitoring network structure

Radar detection performance via frequency agility using measured UAVs RCS data

This paper addresses radar detection performance prediction (via measured data) for drone targets using a frequency agility-based incoherent (square-law) detector. To this end, a preliminary statistical analysis of the integrated Radar Cross Section (RCS) resulting from frequency agile pulses is carried out for drones of different sizes and characteristics, using data acquired in a semi-controlled environment for distinct frequencies, angles, and polarizations. The analysis involves fitting the integrated RCS measurements with commonly used one-parametric and two-parametric probability distributions and leverages the Cramér-von Mises distance and the Kolmogorov Smirnov test. Results show that the Gamma distribution appears to accurately model the resulting fluctuations. Hence, the impact of integration and frequency agility on the RCS fluctuation dispersion is studied. Finally, detection performance of the incoherent square-law detector is assessed for different target and radar parameters, using both measured and simulated data drawn from a Gamma distribution whose parameters follow the preliminary RCS statistical analysis. The results highlight a good agreement between simulated and measurement-based curves

Performance prediction of the coherent radar detector on measured UAVs data

This paper presents measurements of Radar Cross Section (RCS) of five Unmanned Aerial Vehicles (UAVs), comprising both consumer grade and professional small drones, collected in a semi-controlled environment as a function of azimuth aspect angle, polarization and frequency in the range 8.2-18 GHz. A first-order statistical analysis of the measured RCSs is firstly reported prior to assessing the radar detection performance on both measured and bespoke simulated data (leveraging the results of the developed statistical analysis), including, as benchmark terms, the curves for non-fluctuating and Rayleigh fluctuating targets

Comparison of DVB-T Passive Radar Simulated and Measured Bistatic RCS Values for a Pilatus PC-12 Aircraft

Passive radar is a technology that has huge potential for airspace monitoring, taking advantage of existing transmissions. However, to predict whether particular targets can be measured in a particular scenario, it is necessary to be able to model the received signal. In this paper, we present the results of a campaign in which a Pilatus PC-12 single-engine aircraft was measured with a passive radar system relying on DVB-T transmission from a single transmitter. We then present our work to simulate the bistatic RCS of the aircraft along its flight track, using both the method of moments and the shooting and bouncing ray solvers, assess the uncertainty in the simulations, and compare against the measurements. We find that our simulated RCS values are useful in predicting whether or not detection occurs. However, we see poor agreement between simulated and measured RCS values where measurements are available, which we attribute primarily to the difficulties in extracting RCS measurements from the data and to unmodeled transmission and received path effects