1,807 research outputs found
Analysis of Radar Doppler Signature from Human Data
This paper presents the results of time (autocorrelation) and time-frequency (spectrogram) analyses of radar signals returned from the moving human targets. When a radar signal falls on the human target which is moving toward or away from the radar, the signals reflected from different parts of his body produce a Doppler shift that is proportional to the velocity of those parts. Moving parts of the body causes the characteristic Doppler signature. The main contribution comes from the torso which causes the central Doppler frequency of target. The motion of arms and legs induces modulation on the returned radar signal and generates sidebands around the central Doppler frequency, referred to as micro-Doppler signatures. Through analyses on experimental data it was demonstrated that the human motion signature extraction is better using spectrogram. While the central Doppler frequency can be determined using the autocorrelation and the spectrogram, the extraction of the fundamental cadence frequency using the autocorrelation is unreliable when the target is in the clutter presence. It was shown that the fundamental cadence frequency increases with increasing dynamic movement of people and simultaneously the possibility of its extraction is proportional to the degree of synchronization movements of persons in the group
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
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
Drones Classification by the Use of a Multifunctional Radar and Micro-Doppler Analysis
The classification of targets by the use of radars has received great interest in recent years, in particular in defence and military applications, in which the development of sensor systems that are able to identify and classify threatening targets
is a mandatory requirement. In the specific case of drones, several classification techniques have already been proposed and, up to now, the most effective technique was considered to be micro-Doppler analysis used in conjunction with machine learning tools. The micro-Doppler signatures of targets are usually represented in the form of the spectrogram, that is a timeāfrequency diagram that is obtained by performing a short-time Fourier transform (STFT) on the radar return signal. Moreover, frequently it is possible to extract useful information that can also be used in the classification task from the spectrogram of a target. The main aim of the paper is comparing different ways to exploit the droneās micro-Doppler analysis on different stages of a multifunctional radar. Three different classification approaches are compared: classic spectrogram-based classification; spectrum-based classification in which the received signal from the target is picked up after the moving target detector (MTD); and features-based classification, in which the received signal from the target undergoes the detection step after the MTD, after which discriminating features are extracted and used as input to the classifier. To compare the three approaches, a theoretical model for the radar return signal of different types of drone and aerial target is developed, validated by comparison with real recorded data, and used to simulate the targets. Results show that the third approach (features-based) not only has better performance than the others but also is the one that requires less modification and less processing power in a modern multifunctional radar because it reuses most of the processing facility already present
Advanced signal processing solutions for ATR and spectrum sharing in distributed radar systems
Previously held under moratorium from 11 September 2017 until 16 February 2022This Thesis presents advanced signal processing solutions for Automatic
Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems.
Two Synthetic Aperture Radar (SAR) ATR algorithms are described for
full- and single-polarimetric images, and tested on the GOTCHA and the
MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments,
that, being discrete defined, provide better representations of targetsā details. The proposed image moments based framework can be extended to
the availability of several images from multiple sensors through the implementation of a simple fusion rule.
A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopterās rotor and received by
the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating
the number, the length and the rotation speed of the blades, parameters
that are peculiar for each helicopterās model. The algorithm is extended to
deal with the identification of multiple helicopters flying in formation that
cannot be resolved in another domain. Moreover, a fusion rule is presented
to integrate the results of the identification performed from several sensors
in a distributed radar system. Tests performed both on simulated signals
and on real signals acquired from a scale model of a helicopter, confirm
the validity of the algorithm.
Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp
sub-carriers generated through the Fractional Fourier Transform (FrFT),
with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a
cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based
waveform is extensively tested and compared with Orthogonal Frequency
Division Multiplexing (OFDM) and LFM waveforms, in order to assess
both its radar and communication performance.This Thesis presents advanced signal processing solutions for Automatic
Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems.
Two Synthetic Aperture Radar (SAR) ATR algorithms are described for
full- and single-polarimetric images, and tested on the GOTCHA and the
MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments,
that, being discrete defined, provide better representations of targetsā details. The proposed image moments based framework can be extended to
the availability of several images from multiple sensors through the implementation of a simple fusion rule.
A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopterās rotor and received by
the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating
the number, the length and the rotation speed of the blades, parameters
that are peculiar for each helicopterās model. The algorithm is extended to
deal with the identification of multiple helicopters flying in formation that
cannot be resolved in another domain. Moreover, a fusion rule is presented
to integrate the results of the identification performed from several sensors
in a distributed radar system. Tests performed both on simulated signals
and on real signals acquired from a scale model of a helicopter, confirm
the validity of the algorithm.
Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp
sub-carriers generated through the Fractional Fourier Transform (FrFT),
with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a
cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based
waveform is extensively tested and compared with Orthogonal Frequency
Division Multiplexing (OFDM) and LFM waveforms, in order to assess
both its radar and communication performance
A novel algorithm for radar classification based on Doppler characteristics exploiting orthogonal pseudo-Zernike polynomials
Phase modulation induced by target micro-motions introduces side-bands in the radar spectral signature returns. Time-frequency distributions facilitate the representation of such modulations in a micro-Doppler signature that is useful in the characterization and classification of targets. Reliable micro-Doppler signature classification requires the use of robust features that is capable of uniquely describing the micro-motion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper, the application of the pseudo-Zernike moments for micro-Doppler classification is introduced. Specifically, the proposed algorithm consists in the extraction of the pseudo-Zernike moments from the Cadence Velocity Diagram (CVD). The use of pseudo-Zernike moments allows invariant features to be obtained that are able to discriminate the content of two-dimensional matrices with a small number of coefficients. The analysis has been conducted both on simulated and on real radar data, demonstrating the effectiveness of the proposed approach for classification purposes
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
Integrated helicopter survivability
A high level of survivability is important to protect military personnel and equipment and is
central to UK defence policy. Integrated Survivability is the systems engineering
methodology to achieve optimum survivability at an affordable cost, enabling a mission to
be completed successfully in the face of a hostile environment. āIntegrated Helicopter
Survivabilityā is an emerging discipline that is applying this systems engineering approach
within the helicopter domain. Philosophically the overall survivability objective is āzero
attritionā, even though this is unobtainable in practice.
The research question was: āHow can helicopter survivability be assessed in an integrated
way so that the best possible level of survivability can be achieved within the constraints and
how will the associated methods support the acquisition process?ā
The research found that principles from safety management could be applied to the
survivability problem, in particular reducing survivability risk to as low as reasonably
practicable (ALARP). A survivability assessment process was developed to support this
approach and was linked into the military helicopter life cycle. This process positioned the
survivability assessment methods and associated input data derivation activities.
The system influence diagram method was effective at defining the problem and capturing
the wider survivability interactions, including those with the defence lines of development
(DLOD). Influence diagrams and Quality Function Deployment (QFD) methods were
effective visual tools to elicit stakeholder requirements and improve communication across
organisational and domain boundaries.
The semi-quantitative nature of the QFD method leads to numbers that are not real. These
results are suitable for helping to prioritise requirements early in the helicopter life cycle, but
they cannot provide the quantifiable estimate of risk needed to demonstrate ALARP. The probabilistic approach implemented within the Integrated Survivability Assessment
Model (ISAM) was developed to provide a quantitative estimate of āriskā to support the
approach of reducing survivability risks to ALARP. Limitations in available input data for
the rate of encountering threats leads to a probability of survival that is not a real number that
can be used to assess actual loss rates. However, the method does support an assessment
across platform options, provided that the ātest environmentā remains consistent throughout
the assessment. The survivability assessment process and ISAM have been applied to an
acquisition programme, where they have been tested to support the survivability decision
making and design process.
The survivability ātest environmentā is an essential element of the survivability assessment
process and is required by integrated survivability tools such as ISAM. This test
environment, comprising of threatening situations that span the complete spectrum of
helicopter operations requires further development. The ātest environmentā would be used
throughout the helicopter life cycle from selection of design concepts through to test and
evaluation of delivered solutions. It would be updated as part of the through life capability
management (TLCM) process.
A framework of survivability analysis tools requires development that can provide
probabilistic input data into ISAM and allow derivation of confidence limits. This systems
level framework would be capable of informing more detailed survivability design work
later in the life cycle and could be enabled through a MATLABĀ® based approach.
Survivability is an emerging system property that influences the whole system capability.
There is a need for holistic capability level analysis tools that quantify survivability along
with other influencing capabilities such as: mobility (payload / range), lethality, situational
awareness, sustainability and other mission capabilities.
It is recommended that an investigation of capability level analysis methods across defence
should be undertaken to ensure a coherent and compliant approach to systems engineering
that adopts best practice from across the domains. Systems dynamics techniques should be
considered for further use by Dstl and the wider MOD, particularly within the survivability
and operational analysis domains. This would improve understanding of the problem space,
promote a more holistic approach and enable a better balance of capability, within which
survivability is one essential element.
There would be value in considering accidental losses within a more comprehensive
āsurvivabilityā analysis. This approach would enable a better balance to be struck between
safety and survivability risk mitigations and would lead to an improved, more integrated
overall design
Investigation of advanced navigation and guidance system concepts for all-weather rotorcraft operations
Results are presented of a survey conducted of active helicopter operators to determine the extent to which they wish to operate in IMC conditions, the visibility limits under which they would operate, the revenue benefits to be gained, and the percent of aircraft cost they would pay for such increased capability. Candidate systems were examined for capability to meet the requirements of a mission model constructed to represent the modes of flight normally encountered in low visibility conditions. Recommendations are made for development of high resolution radar, simulation of the control display system for steep approaches, and for development of an obstacle sensing system for detecting wires. A cost feasibility analysis is included
Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology
This paper discusses the challenges of detecting and categorizing small
drones with radar automatic target recognition (ATR) technology. The authors
suggest integrating ATR capabilities into drone detection radar systems to
improve performance and manage emerging threats. The study focuses primarily on
drones in Group 1 and 2. The paper highlights the need to consider kinetic
features and signal signatures, such as micro-Doppler, in ATR techniques to
efficiently recognize small drones. The authors also present a comprehensive
drone detection radar system design that balances detection and tracking
requirements, incorporating parameter adjustment based on scattering region
theory. They offer an example of a performance improvement achieved using
feedback and situational awareness mechanisms with the integrated ATR
capabilities. Furthermore, the paper examines challenges related to one-way
attack drones and explores the potential of cognitive radar as a solution. The
integration of ATR capabilities transforms a 3D radar system into a 4D radar
system, resulting in improved drone detection performance. These advancements
are useful in military, civilian, and commercial applications, and ongoing
research and development efforts are essential to keep radar systems effective
and ready to detect, track, and respond to emerging threats.Comment: 17 pages, 14 figures, submitted to a journal and being under revie
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