Estimation of intrapulse modulation parameters of LPI radar under noisy conditions

2021 ◽  
pp. 1-18
Author(s):  
Chilukuri Raja Kumari ◽  
Hari Kishore Kakarla ◽  
K. Subbarao
Keyword(s):  
Frequency Modulation ◽  
Precise Measurement ◽  
Signal To Noise Ratio ◽  
Nonlinear Frequency ◽  
Pulse Modulation ◽  
Spectral Correlation ◽  
Electronic Warfare ◽  
Spectral Correlation Density ◽  
Low Probability ◽  
Better Than

Abstract Low probability of intercept (LPI) radars utilize specially designed waveforms for intra-pulse modulation and hence LPI radars cannot be easily intercepted by passive receivers. The waveforms include linear frequency modulation, nonlinear frequency modulation, polyphase, and polytime codes. The advantages of LPI radar are wide bandwidth, frequency variability, low power, and the ability to hide their emissions. On the other hand, the main purpose of intercept receiver is to classify and estimate the parameters of the waveforms even when the signals are contaminated with noise. Precise measurement of the parameters will provide necessary information about a threat to the radar so that the electronic attack or electronic warfare support system could take instantaneous counter action against the enemy. In this work, noisy polyphase and polytime coded waveforms are analyzed using cyclostationary (CS) algorithm. To improve the signal quality, the noisy signal is pre-processed using two types of denoising filters. The denoised signal is analyzed using CS techniques and the coefficients of spectral correlation density are computed. With this method, modulation parameters of nine types of waveforms up to −12 dB signal-to-noise ratio with an accuracy of better than 95% are extracted. When compared with literature values, it is found that the results are superior.

scholarly journals Recognition method of radar intra-pulse modulation type based on signal square spectrum bandwidth ratio

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Dongmei Li ◽  
Zhiyuan Xu ◽  
Lei Gu ◽  
Lanxiang Zhu
Keyword(s):  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Noise Signal ◽  
Reconstruction Error ◽  
Information Warfare ◽  
60 Ghz ◽  
Bandwidth Utilization ◽  
Pulse Modulation ◽  
Electronic Warfare ◽  
Time Frequency

AbstractThe twenty-first century is the era of electronic warfare and information warfare. The focus is of the battle between all parties. CEEMD can link the time domain and frequency domain, describe the two-dimensional time–frequency characteristics of the signal, and draw the time–frequency diagram of the signal, so as to reduce the noise signal and improve the signal-to-noise ratio of the signal. The purpose of this paper was to study how to adjust the signal square spectrum bandwidth ratio in the subject of identifying the intra-pulse modulation of radar, so as to solve the problem of identifying the type of radar intra-pulse modulation. The experimental results in this paper show that the decomposition result of EEMD is incomplete and the signal reconstruction error is larger. Compared with the previous two methods, not only the CEEMD method can effectively suppress modal aliasing, but also the decomposition result is complete; the signal reconstruction error is very small, and the decomposition results close to ideal value. The interleaving filter with a bandwidth ratio of 1:2 can divide the 100 GHz channel spacing into asymmetric output spectra with bandwidths greater than 60 GHz and 30 GHz, which effectively improves the current mix of 10 Gb/s and 40 Gb/s The bandwidth utilization of the system illustrates the success of the simulation experiment.

Pre-Processing of Hyperspectral Images Using Nonlinear Filters

2013 ◽  
Vol 11 (1) ◽  
pp. 8-13
Author(s):  
V. Behar ◽  
V. Bogdanova
Keyword(s):  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Edge Preserving ◽  
Improvement Factor ◽  
Filter Output ◽  
Average Improvement ◽  
Processing Procedure

Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6

scholarly journals Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2840
Author(s):  
Hubert Milczarek ◽  
Czesław Leśnik ◽  
Igor Djurović ◽  
Adam Kawalec
Keyword(s):  
Instantaneous Frequency ◽  
Early Warning Systems ◽  
Vital Role ◽  
Estimation Methods ◽  
Radar Signal ◽  
Nonlinear Frequency ◽  
Modulation Recognition ◽  
Electronic Warfare ◽  
Time Frequency ◽  
Radar Signals

Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.

scholarly journals Velody 2—Resilient High-Capacity MIDI Steganography for Organ and Harpsichord Music

2020 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Eric Järpe ◽  
Mattias Weckstén
Keyword(s):  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
High Capacity ◽  
Absolute Error ◽  
Music Technology ◽  
Alternative Methods ◽  
File Size ◽  
User Friendly ◽  
Different Levels ◽  
Better Than

A new method for musical steganography for the MIDI format is presented. The MIDI standard is a user-friendly music technology protocol that is frequently deployed by composers of different levels of ambition. There is to the author’s knowledge no fully implemented and rigorously specified, publicly available method for MIDI steganography. The goal of this study, however, is to investigate how a novel MIDI steganography algorithm can be implemented by manipulation of the velocity attribute subject to restrictions of capacity and security. Many of today’s MIDI steganography methods—less rigorously described in the literature—fail to be resilient to steganalysis. Traces (such as artefacts in the MIDI code which would not occur by the mere generation of MIDI music: MIDI file size inflation, radical changes in mean absolute error or peak signal-to-noise ratio of certain kinds of MIDI events or even audible effects in the stego MIDI file) that could catch the eye of a scrutinizing steganalyst are side-effects of many current methods described in the literature. This steganalysis resilience is an imperative property of the steganography method. However, by restricting the carrier MIDI files to classical organ and harpsichord pieces, the problem of velocities following the mood of the music can be avoided. The proposed method, called Velody 2, is found to be on par with or better than the cutting edge alternative methods regarding capacity and inflation while still possessing a better resilience against steganalysis. An audibility test was conducted to check that there are no signs of audible traces in the stego MIDI files.

scholarly journals Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Qi An ◽  
Zi-shu He ◽  
Hui-yong Li ◽  
Yong-hua Li
Keyword(s):  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Frequency Offset ◽  
Estimation Accuracy ◽  
Modulation Classification ◽  
Electronic Warfare ◽  
Wireless Communication Network ◽  
Shift Keying ◽  
Analytical Formulas ◽  
Fractional Function

Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

Artificial neural network-based seismic detector

1995 ◽  
Vol 85 (1) ◽  
pp. 308-319 ◽  
Author(s):  
Jin Wang ◽  
Ta-Liang Teng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Data ◽  
Seismic Event ◽  
Signal To Noise Ratio ◽  
Type B ◽  
Earthquake Detection ◽  
Seismic Event Detection ◽  
Artificial Neural ◽  
Better Than

Abstract An artificial neural network-based pattern classification system is applied to seismic event detection. We have designed two types of Artificial Neural Detector (AND) for real-time earthquake detection. Type A artificial neural detector (AND-A) uses the recursive STA/LTA time series as input data, and type B (AND-B) uses moving window spectrograms as input data to detect earthquake signals. The two AND's are trained under supervised learning by using a set of seismic recordings, and then the trained AND's are applied to another set of recordings for testing. Results show that the accuracy of the artificial neural network-based seismic detectors is better than that of the conventional algorithms solely based on the STA/LTA threshold. This is especially true for signals with either low signal-to-noise ratio or spikelike noises.

scholarly journals A new and homogeneous metallicity scale for Galactic classical Cepheids

2018 ◽  
Vol 616 ◽  
pp. A82 ◽  
Author(s):  
B. Proxauf ◽  
R. da Silva ◽  
V. V. Kovtyukh ◽  
G. Bono ◽  
L. Inno ◽  
...  
Keyword(s):  
Effective Temperature ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Physical Parameters ◽  
Classical Cepheids ◽  
Microturbulent Velocity ◽  
Metal Abundances ◽  
Pulsation Cycle ◽  
Intrinsic Error ◽  
Better Than

We gathered more than 1130 high-resolution optical spectra for more than 250 Galactic classical Cepheids. The spectra were collected with the optical spectrographs UVES at VLT, HARPS at 3.6 m, FEROS at 2.2 m MPG/ESO, and STELLA. To improve the effective temperature estimates, we present more than 150 new line depth ratio (LDR) calibrations that together with similar calibrations already available in the literature allowed us to cover a broad range in wavelength (5348 ≤ λ ≤ 8427 Å) and in effective temperature (3500 ≤ Teff ≤ 7700 K). This gives us the unique opportunity to cover both the hottest and coolest phases along the Cepheid pulsation cycle and to limit the intrinsic error on individual measurements at the level of ~100 K. As a consequence of the high signal-to-noise ratio of individual spectra, we identified and measured hundreds of neutral and ionized lines of heavy elements, and in turn, have the opportunity to trace the variation of both surface gravity and microturbulent velocity along the pulsation cycle. The accuracy of the physical parameters and the number of Fe I (more than one hundred) and Fe II (more than ten) lines measured allowed us to estimate mean iron abundances with a precision better than 0.1 dex. We focus on 14 calibrating Cepheids for which the current spectra cover either the entire or a significant portion of the pulsation cycle. The current estimates of the variation of the physical parameters along the pulsation cycle and of the iron abundances agree very well with similar estimates available in the literature. Independent homogeneous estimates of both physical parameters and metal abundances based on different approaches that can constrain possible systematics are highly encouraged.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2017 ◽  
Vol 8 (4) ◽  
pp. 58-83 ◽  
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image ◽  
Better Than

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

scholarly journals A High-Granularity Timing Detector for the Phase-II upgrade of the ATLAS Calorimeter system: detector concept, description and R&D and beam test results

2021 ◽  
Vol 253 ◽  
pp. 11012
Author(s):  
H. Imam
Keyword(s):  
Signal To Noise Ratio ◽  
Liquid Argon ◽  
High Signal ◽  
Test Results ◽  
Forward Region ◽  
Test Beam ◽  
Pile Up ◽  
Flux Increase ◽  
Silicon Sensor ◽  
Better Than

The particle flux increase (pile-up) at the HL-LHC with luminosities of L = 7.5 × 1034 cm−2 s−1 will have a significant impact on the reconstruction of the ATLAS detector and on the performance of the trigger. The forward region and the end-cap where the internal tracker has poorer longitudinal track impact parameter resolution, and where the liquid argon calorimeter has coarser granularity, will be significantly affected. A High Granularity Time Detector (HGTD) is proposed to be installed in front of the LAr end-cap calorimeter for the mitigation of the pileup effect, as well as measurement of luminosity. It will have coverage of 2.4 to 4.0 from the pseudo-rapidity range. Two dual-sided silicon sensor layers will provide accurate timing information for minimum-ionizing particles with a resolution better than 30 ps per track (before irradiation), for assigning each particle to the correct vertex. The readout cells are about 1.3 mm × 1.3 mm in size, which leads to a high granular detector with 3 million channels. The technology of low-gain avalanche detectors (LGAD) with sufficient gain was chosen to achieve the required high signal-to-noise ratio. A dedicated ASIC is under development with some prototypes already submitted and evaluated. The requirements and general specifications of the HGTD will be maintained and discussed. R&D campaigns on the LGAD are carried out to study the sensors, the related ASICs and the radiation hardness. Both laboratory and test beam results will be presented.

scholarly journals Global spectral clustering in dynamic networks

2018 ◽  
Vol 115 (5) ◽  
pp. 927-932 ◽  
Author(s):  
Fuchen Liu ◽  
David Choi ◽  
Lu Xie ◽  
Kathryn Roeder
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Signal To Noise Ratio ◽  
Dynamic Networks ◽  
Brain Regions ◽  
Tuning Parameter ◽  
Expression Data ◽  
Dynamic Community Detection ◽  
Dynamic Community ◽  
Better Than

Community detection is challenging when the network structure is estimated with uncertainty. Dynamic networks present additional challenges but also add information across time periods. We propose a global community detection method, persistent communities by eigenvector smoothing (PisCES), that combines information across a series of networks, longitudinally, to strengthen the inference for each period. Our method is derived from evolutionary spectral clustering and degree correction methods. Data-driven solutions to the problem of tuning parameter selection are provided. In simulations we find that PisCES performs better than competing methods designed for a low signal-to-noise ratio. Recently obtained gene expression data from rhesus monkey brains provide samples from finely partitioned brain regions over a broad time span including pre- and postnatal periods. Of interest is how gene communities develop over space and time; however, once the data are divided into homogeneous spatial and temporal periods, sample sizes are very small, making inference quite challenging. Applying PisCES to medial prefrontal cortex in monkey rhesus brains from near conception to adulthood reveals dense communities that persist, merge, and diverge over time and others that are loosely organized and short lived, illustrating how dynamic community detection can yield interesting insights into processes such as brain development.

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