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

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.

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LPI Radar Waveform Recognition Based on CNN and TPOT

Symmetry ◽

10.3390/sym11050725 ◽

2019 ◽

Vol 11 (5) ◽

pp. 725 ◽

Cited By ~ 5

Author(s):

Jian Wan ◽

Xin Yu ◽

Qiang Guo

Keyword(s):

Signal To Noise Ratio ◽

Recognition Rate ◽

Recognition System ◽

Radar Signal ◽

Automatic Identification ◽

Identification System ◽

Electronic Warfare ◽

Time Frequency ◽

Barker Codes ◽

Intelligence Support

The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB.

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Estimation of intrapulse modulation parameters of LPI radar under noisy conditions

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078721001537 ◽

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.

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Emitter Signal Waveform Classification Based on Autocorrelation and Time-Frequency Analysis

Electronics ◽

10.3390/electronics8121419 ◽

2019 ◽

Vol 8 (12) ◽

pp. 1419 ◽

Cited By ~ 1

Author(s):

Zhiyuan Ma ◽

Zhi Huang ◽

Anni Lin ◽

Guangming Huang

Keyword(s):

Hybrid Model ◽

Signal To Noise Ratio ◽

Recognition Rate ◽

Complete Classification ◽

Construction Technique ◽

Electronic Warfare ◽

Image Construction ◽

Time Frequency ◽

Low Snr ◽

Signal Waveform

Emitter signal waveform recognition and classification are necessary survival techniques in electronic warfare systems. The emitters use various techniques for power management and complex intra-pulse modulations, which can create what looks like a noisy signal to an intercept receiver, so emitter signal waveform recognition at a low signal-to-noise ratio (SNR) has gained increased attention. In this study, we propose an autocorrelation feature image construction technique (ACFICT) combined with a convolutional neural network (CNN) to maintain the unique feature of each signal, and a structure optimization for CNN input layer called hybrid model is designed to achieve image enhancement of the signal autocorrelation, which is different from using a single image combined with CNN to complete classification. We demonstrate the performance of ACFICT by comparing feature images generated by different signal pre-processing algorithms, and the evaluation indicators are signal recognition rate, image stability degree, and image restoration degree. This paper simulates six types of the signals by combining ACFICT with three types of hybrid model, the simulation results compared with the literature show that the proposed methods not only has a high universality, but also better adapts to waveform recognition at low SNR environment. When the SNR is –6 dB, the overall recognition rate of the method reaches 88%.

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Signal Reconstruction Based on a Fusion Compressed Sensing Frame

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.785-786.1315 ◽

2013 ◽

Vol 785-786 ◽

pp. 1315-1323

Author(s):

Xu Hua Li ◽

Yue Li Chen ◽

Nan Jun Hu ◽

Wei Li ◽

Tian Jun Yuan ◽

...

Keyword(s):

Compressed Sensing ◽

Matching Pursuit ◽

Signal To Noise Ratio ◽

Signal Reconstruction ◽

Greedy Algorithms ◽

Reconstruction Error ◽

Reconstruction Accuracy ◽

Support Sets ◽

Reconstruction Effects ◽

Better Than

Greedy algorithms represented by orthogonal matching pursuit (OMP) and subspace pursuit (SP) algorithms are practically used in image processing based upon compressed sensing theory. However, there are two disadvantages: 1)Relatively poor signal reconstruction accuracy; 2) High computation complexity and measurements time. This paper proposes a frame of greedy algorithms obtaining a novel fusion of matching pursuit (FMP), combining the OMP and SP algorithms. FMP unites the two support sets from OMP and SP selecting the most appropriate atoms to achieve secondary screening of the original two support sets, finally realizing the accurate signal reconstruction. Using same test conditions, image reconstruction experiments and stability of Frame, the proposed FMP algorithm can effectively improve signal-to-noise ratio (SNR) with improved reconstruction error. Reconstruction effects using proposed FMP are better than separately using other two greedy algorithms for both high and low resolution images.

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An Effective LFM Signal Reconstruction Method for Signal Denoising

Journal of Circuits System and Computers ◽

10.1142/s0218126618501402 ◽

2018 ◽

Vol 27 (09) ◽

pp. 1850140

Author(s):

Shan Luo ◽

Guoan Bi ◽

Tong Wu ◽

Yong Xiao ◽

Rongping Lin

Keyword(s):

Signal To Noise Ratio ◽

Signal Reconstruction ◽

Experimental Results ◽

Signal Denoising ◽

Reconstruction Method ◽

Signal To Noise ◽

Time Frequency ◽

Effective Time ◽

Reconstructed Signal

One of the main challenges in signal denoising is to accurately restore useful signals in low signal-to-noise ratio (SNR) scenarios. In this paper, we investigate the signal denoising problem for multi-component linear frequency modulated (LFM) signals. An effective time-frequency (TF) analysis-based approach is proposed. Compared to the existing approaches, our proposed one can further increase the noise suppressing performance and improve the quality of the reconstructed signal. Experimental results are presented to show that the proposed denoising approach is able to effectively separate the multi-component LFM signal from the strong noise environments.

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A Block Method Using the Chirp Rate Estimation for NLFM Radar Pulse Reconstruction

Sensors ◽

10.3390/s19225015 ◽

2019 ◽

Vol 19 (22) ◽

pp. 5015

Author(s):

Karol Abratkiewicz ◽

Piotr Samczyński

Keyword(s):

Signal Reconstruction ◽

Real Life ◽

Pulse Signal ◽

Chirp Rate ◽

Main Concept ◽

Electronic Warfare ◽

Practical Applications ◽

Time Frequency ◽

Novel Approach ◽

Non Linear

This paper presents a novel approach to fast and accurate non-linear pulse signal reconstruction dedicated for electromagnetic sensors and their applications such as ELectronic INTelligence (ELINT), electronic warfare (EW), electronic reconnaissance (ER) systems, as well as for passive bistatic radar purposes in which other pulse radars are used as a source of illumination. The method is based on the instantaneous chirp rate (CR) estimation in the time-frequency (TF) domain providing a calculation of the frequency rate between every two consecutive samples. Such a new method allows for the precise reconstruction of the non-linear frequency modulated (NLFM) signal to be carried out in significantly shorter time in comparison to methods known in the literature. The presented approach was tested and validated using both simulated and real-life radar signals proving the usability of the proposed solution in practical applications. The results were compared with the precise extended generalized chirp transform (EGCT) method as a reference technique, using optimal matched filtration as the main concept.

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

Sensors ◽

10.3390/s21082840 ◽

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.

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A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

Applied Sciences ◽

10.3390/app11020673 ◽

2021 ◽

Vol 11 (2) ◽

pp. 673

Author(s):

Guangli Ben ◽

Xifeng Zheng ◽

Yongcheng Wang ◽

Ning Zhang ◽

Xin Zhang

Keyword(s):

Maximum Likelihood ◽

Local Search ◽

Signal To Noise Ratio ◽

Estimation Accuracy ◽

Chirp Signal ◽

Estimation Of Parameters ◽

Parameter Estimator ◽

Denoising Method ◽

Time Frequency ◽

Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

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Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

Mathematical Problems in Engineering ◽

10.1155/2016/7616393 ◽

2016 ◽

Vol 2016 ◽

pp. 1-16 ◽

Cited By ~ 19

Author(s):

Irena Orović ◽

Vladan Papić ◽

Cornel Ioana ◽

Xiumei Li ◽

Srdjan Stanković

Keyword(s):

Signal Processing ◽

Fourier Transform ◽

Compressive Sensing ◽

Signal Reconstruction ◽

Signal Acquisition ◽

Transform Domain ◽

Hermite Transform ◽

Time Frequency ◽

Reconstruction Methods ◽

Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

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

Mobile Information Systems ◽

10.1155/2016/2398464 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 1

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.

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