95 research outputs found
Radar HRRP Modeling using Dynamic System for Radar Target Recognition
High resolution range profile (HRRP) is being known as one of the most powerful tools for radar target recognition. The main problem with range profile for radar target recognition is its sensitivity to aspect angle. To overcome this problem, consecutive samples of HRRP were assumed to be identically independently distributed (IID) in small frames of aspect angles in most of the related works. Here, considering the physical circumstances of maneuver of an aerial target, we have proposed dynamic system which models the short dependency between consecutive samples of HRRP in segments of the whole HRRP sequence. Dynamic system (DS) is used to model the sequence of PCA (principal component analysis) coefficients extracted from the sequence of HRRPs. Considering this we have proposed a model called PCA+DS. We have also proposed a segmentation algorithm which segments the HRRP sequence reliably. Akaike information criterion (AIC) used to evaluate the quality of data modeling showed that our PCA+DS model outperforms factor analysis (FA) model. In addition, target recognition results using simulated data showed that our method based on PCA+DS achieves better recognition rates compared to the method based on FA
A New MCMC Sampling Based Segment Model for Radar Target Recognition
One of the main tools in radar target recognition is high resolution range profile (HRRP)â. âHoweverâ, âit is very sensitive to the aspect angleâ. âOne solution to this problem is to assume the consecutive samples of HRRP identically independently distributed (IID) in small frames of aspect anglesâ, âan assumption which is not true in realityâ. âHowever, bââased on this assumptionâ, âsome models have been developed to characterize the sequential information contained in the multi-aspect radar echoesâ. âThereforeâ, âthey only consider the short dependency between consecutive samplesâ. âHereâ, âwe propose an alternative modelâ, âthe segment modelâ, âto address the shortcomings of these assumptionsâ. âIn additionâ, âusing a Markov chain Monte-Carlo (MCMC) based Gibbs sampler as an iterative approach to estimate the parameters of the segment modelâ, âwe will show that the proposed method is able to estimate the parameters with quite satisfying accuracy and computational loadâ
Non-cooperative identification of civil aircraft using a generalised mutual subspace method
The subspace-based methods are effectively applied to classify sets of feature vectors by modelling them as
subspaces. However, their application to the field of non-cooperative target identification of flying aircraft is barely seen in the literature. In these methods, setting the subspace dimensionality is always an issue. Here, it is demonstrated that a modified mutual subspace method, which uses softweights to set the importance of each subspace basis, is a promising classifier for identifying sets of range profiles coming from real in-flight targets with no need to set the subspace dimensionality in advance. The assembly of a recognition database is also a challenging task.
In this study, this database comprises predicted range profiles coming from electromagnetic simulations. Even though the predicted and actual profiles differ, the high recognition rates achieved reveal that the algorithm might be a good candidate for its application in an operational target recognition system
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
An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems
This paper presents a brief examination of Automatic Target Recognition (ATR)
technology within ground-based radar systems. It offers a lucid comprehension
of the ATR concept, delves into its historical milestones, and categorizes ATR
methods according to different scattering regions. By incorporating ATR
solutions into radar systems, this study demonstrates the expansion of radar
detection ranges and the enhancement of tracking capabilities, leading to
superior situational awareness. Drawing insights from the Russo-Ukrainian War,
the paper highlights three pressing radar applications that urgently
necessitate ATR technology: detecting stealth aircraft, countering small
drones, and implementing anti-jamming measures. Anticipating the next wave of
radar ATR research, the study predicts a surge in cognitive radar and machine
learning (ML)-driven algorithms. These emerging methodologies aspire to
confront challenges associated with system adaptation, real-time recognition,
and environmental adaptability. Ultimately, ATR stands poised to revolutionize
conventional radar systems, ushering in an era of 4D sensing capabilities
Bispectrum- and Bicoherence-Based Discriminative Features Used for Classification of Radar Targets and Atmospheric Formations
This chapter is dedicated to bispectrum-based signal processing in the surveillance radar applications. Detection, recognition, and classification of the targets by surveillance radars have various applications including security, military intelligence, battlefield purposes, boundary protection, as well as weather forecast. One of the particular and effective discriminative features commonly exploited in modern radar automatic target recognition (ATR) systems is the micro-Doppler (m-D) contributions extracted from joint time-frequency (TF) distribution. However, a common drawback of the energy-based strategy lies in the impossibility to retrieve additional particular information related to frequency-coupling and phase-coupling contributions containing in the radar backscattering. Phase coupling contains additional discriminative features related to individual target properties. Bispectrum-based strategy allows retrieving a phase-coupled data containing unique discriminative features related to individual target properties. Bispectrum tends to zero for a stationary zero-mean additive white Gaussian noise (AWGN), providing smoothing of AWGN in TF distributions. Hence, bispectrum-based approach allows improving extraction of robust discriminative features for ATR radar systems
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)9âs 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.SimulaçÔes electromagnéticas do alvo foram realizadas de modo a obter a assinatura radar (RCS)
para vĂĄrias posiçÔes e frequĂȘncias. O software utilizado Ă© o CST MWS©. O modelo proposto Ă
escala 1:5 foi modelado em CATIA© V5 R19 e importado diretamente para o ambiente de trabalho
CST MWS©. Foram efectuadas simulaçÔes na banda X com uma malha de tamanho variåvel
devido à consideråvel variação do comprimento de onda. Pretende-se avaliar estatisticamente
o teste de decisĂŁo simples de Neyman-Pearson (NP), analisando as CaracterĂsticas de Operação
do Receptor (ROCs) para dois cenårios 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 måximo de
detecção para ambos os casos de modo a comparar a eficiĂȘncia de blindagem electromagnĂ©tica
(SE) entre os diferentes revestimentos. As especificaçÔes 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 nĂŁo-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 posiçÔes na gama de frequĂȘncias selecionada. Um ganho
de alcance de detecção måximo 50:9 % foi obtido para o RAM PAniCo, estando de acordo com
os resultados experimentais da bibliografia estudada. JĂĄ a melhor SE foi verificada para o RAM
CFC-Fe e PAniCo
Toward Deep Learning-Based Human Target Analysis
In this chapter, we describe methods toward deep learning-based human target analysis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are demonstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration
Sparsity-based autoencoders for denoising cluttered radar signatures
Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high-range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal-to-noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single-layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of â10 dB and label mismatch error of 50%
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