175 research outputs found
On the antiâintercept features of noise radars
Robustness against Electronic Warfare/Electronic Defence attacks represents an
important advantage of Noise Radar Technology (NRT). An evaluation of the related
Low Probability of Detection (LPD) and of Intercept (LPI) is presented for Continuous
Emission Noise Radar (CEâNR) waveforms with different operational parameters, that is,
âtailoredâ, and with various âdegrees of randomnessâ. In this frame, three different noise
radar waveforms, a phase Noise (APCN) and two âtailoredâ noise waveforms (FMeth
and COSPAR), are compared by timeâfrequency analysis. Using a correlator (i.e. a two
antennas) receiver, assuming a complete knowledge of the band (B) and duration (T) of
the coherent emission of these waveforms, it will be shown that the LPD features of a
CEâNR do not significantly differ from those of any CE radar transmitting deterministic
waveforms. However, in real operations, B and T are unknown; hence, assuming an
instantaneous bandwidth estimation will show that the duration T can be estimated only
for some specific âtailoredâ waveforms (of course, not to be operationally used). The
effect of âtailoringâ is analysed with prospects for future work. Finally, some limitations
in the classification of these radar signals are analysed
Signal design and processing for noise radar
An efficient and secure use of the electromagnetic spectrum by different telecommunications
and radar systems represents, today, a focal research point, as the coexistence
of different radio-frequency sources at the same time and in the same frequency band
requires the solution of a non-trivial interference problem. Normally, this is addressed
with diversity in frequency, space, time, polarization, or code. In some radar applications,
a secure use of the spectrum calls for the design of a set of transmitted waveforms
highly resilient to interception and exploitation, i.e., with low probability of intercept/
exploitation capability. In this frame, the noise radar technology (NRT) transmits
noise-like waveforms and uses correlation processing of radar echoes for their optimal
reception. After a review of the NRT as developed in the last decades, the aim of this
paper is to show that NRT can represent a valid solution to the aforesaid problems
Joint 1D and 2D Neural Networks for Automatic Modulation Recognition
The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O\u27Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these architectures and integrated the models to perform joint detection and classification. To our knowledge, the present research is the first to study and successfully combine a lD ResNet classifier and Yolo v3 object detector to fully automate the process of AMR for parameter estimation, pulse extraction and waveform classification for non-cooperative scenarios. The overall performance of the joint detector/ classifier is 90 at 10 dB signal to noise ratio for 24 digital and analog modulations
Radar intra-pulse modulation classification using convolutional neural networks
This dissertation presents a detailed investigation into the classification of radar intra-pulse modulation schemes. Recent years have seen increased waveform diversity in radar systems which, while making many aspects of pulse analysis more challenging, have presented new opportunities and features for the _eld of classification. This dissertation aims to address the increasing difficulty of pulse classification through the use of modern machine learning techniques - more specifically, by utilising convolutional neural networks. A wide range of modulation schemes was considered and simulated with realistic imperfections to create a dataset that was as representative of real-world scenarios as possible. Data representations of varying levels of abstraction were analysed in order to investigate the effects of data formatting on the performance of various classifiers. A classifier which made use of manual feature extraction was evaluated against a series of convolutional neural network classifiers in order to establish whether improvements in classification accuracy and throughput could be realised. This study also presents research into the viability of classifying data that has been degraded by real transmitter and channel effects using classifiers trained entirely on simulated data. The operation of the tested classifiers is analysed, and parallels are drawn between the feature extraction steps in convolutional neural networks and conventional signal features. The primary research questions in this study are whether machine learning approaches are able to improve on non-machine learning based classification techniques, and which data representations are best suited to convolutional neural network based classification. Classifiers were tested across 28 classes of modulation, with signal-to-noise ratios uniformly distributed between -5 dB and 20 dB. It was found that substantial performance and stability improvements could be achieved when convolutional neural networks were used over the tested non-machine learning based classification technique. The most promising classifier made use of time-frequency representations as an input, and was able to achieve a classification accuracy of 98%, while exhibiting extreme robustness against noise and pulse imperfections
Radar intraâpulse signal modulation classification with contrastive learning
The existing research on deep learning for radar signal intraâpulse modulation classification is mainly based on supervised leaning techniques, which performance mainly relies on a large number of labeled samples. To overcome this limitation, a selfâsupervised leaning framework, contrastive learning (CL), combined with the convolutional neural network (CNN) and focal loss function is proposed, called CLââCNN. A twoâstage training strategy is adopted by CLâCNN. In the first stage, the model is pretrained using abundant unlabeled timeâfrequency images, and data augmentation is used to introduce positiveâpair and negativeâpair samples for selfâsupervised learning. In the second stage, the pretrained model is fineâtuned for classification, which only uses a small number of labeled timeâfrequency images. The simulation results demonstrate that CLâCNN outperforms the other deep models and traditional methods in scenarios with Gaussian noise and impulsive noiseâaffected signals, respectively. In addition, the proposed CLâCNN also shows good generalization ability, i.e., the model pretrained with Gaussian noiseâaffected samples also performs well on impulsive noiseâaffected samples
Adaptive Coding, Modulation and Filtering of Radar Signals
In this chapter, some of the issues associated with radar signal processing are highlighted, with an emphasis on adaptability. Signal processing operations are carried by systems in order to enhance the received signal or to clarify its content of information. Received radar signal should be subjected to processing prior to the extraction of useful target information out of it so as to emphasize desired signal among other accompanying signals. Processing of the radio frequency (RF) signal is generally done in an analogue manner, while digital signal processing (DSP) became dominant in the intermediate-frequency (IF) and low-frequency portions of the system. Since the detectability and immunity against interference and clutter strongly depend on the waveform used, it will be more efficient to apply a diverse waveform instead of confinement to an invariable waveform of a fixed code and pattern. Adaptive coding, modulation and filtering of radar signals provide high degree of diversity as well as flexibility and agility for signal processors versus changing sources of interference and environmentally dependent reflectors. Constant false alarm rate (CFAR) is an adaptive processing technique that reduces noise and clutter. Different methods are applied in CFAR technique to adaptively cope with varying clutter density and distribution
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