LPI Radar Waveform Recognition Based on CNN and TPOT

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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A Radar Signal Recognition Approach via IIF-Net Deep Learning Models

Computational Intelligence and Neuroscience ◽

10.1155/2020/8858588 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Ji Li ◽

Huiqiang Zhang ◽

Jianping Ou ◽

Wei Wang

Keyword(s):

Recognition Accuracy ◽

Signal To Noise Ratio ◽

Recognition Rate ◽

Software Radio ◽

Radar Signal ◽

Signal Recognition ◽

Electromagnetic Environment ◽

Time Frequency ◽

Low Snr ◽

Radar Signals

In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.

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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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A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA

Symmetry ◽

10.3390/sym10110659 ◽

2018 ◽

Vol 10 (11) ◽

pp. 659

Author(s):

Jian Wan ◽

Guoqing Ruan ◽

Qiang Guo ◽

Xue Gong

Keyword(s):

Recognition Rate ◽

Recognition System ◽

Inner Product ◽

Radar Signal ◽

Signal Recognition ◽

Recognition Method ◽

Electronic Warfare ◽

Quantum Genetic Algorithm ◽

Optimal Classification ◽

The Difference

Radar electronic reconnaissance is an important part of modern and future electronic warfare systems and is the primary method to obtain non-cooperative intelligence information. As the task requirement of radar electronic reconnaissance, it is necessary to identify the non-cooperative signals from the mixed signals. However, with the complexity of battlefield electromagnetic environment, the performance of traditional recognition system is seriously affected. In this paper, a new recognition method based on optimal classification atom and improved double chains quantum genetic algorithm (IDCQGA) is researched, optimal classification atom is a new feature for radar signal recognition, IDCQGA with symmetric coding performance can be applied to the global optimization algorithm. The main contributions of this paper are as follows: Firstly, in order to measure the difference of multi-class signals, signal separation degree based on distance criterion is proposed and established according to the inter-class separability and intra-class aggregation of the signals. Then, an IDCQGA is proposed to select the best atom for classification under the constraint of distance criterion, and the inner product of the signal and the best atom for classification is taken as the eigenvector. Finally, the extreme learning machine (ELM) is introduced as classifier to complete the recognition of signals. Simulation results show that the proposed method can improve the recognition rate of multi-class signals and has better processing ability for overlapping eigenvector parameters.

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Detection, Localization and Classification of Multiple Mechanized Ocean Vessels over Continental-Shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing

Remote Sensing ◽

10.3390/rs10111699 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1699 ◽

Cited By ~ 9

Author(s):

Chenyang Zhu ◽

Heriberto Garcia ◽

Anna Kaplan ◽

Matthew Schinault ◽

Nils Handegard ◽

...

Keyword(s):

Remote Sensing ◽

Continental Shelf ◽

Signal To Noise Ratio ◽

Automatic Identification ◽

Identification System ◽

Underwater Sound ◽

Constant Frequency ◽

Acoustic Waveguide ◽

Time Frequency ◽

Hydrophone Array

Multiple mechanized ocean vessels, including both surface ships and submerged vehicles, can be simultaneously monitored over instantaneous continental-shelf scale regions >10,000 km 2 via passive ocean acoustic waveguide remote sensing. A large-aperture densely-sampled coherent hydrophone array system is employed in the Norwegian Sea in Spring 2014 to provide directional sensing in 360 degree horizontal azimuth and to significantly enhance the signal-to-noise ratio (SNR) of ship-radiated underwater sound, which improves ship detection ranges by roughly two orders of magnitude over that of a single hydrophone. Here, 30 mechanized ocean vessels spanning ranges from nearby to over 150 km from the coherent hydrophone array, are detected, localized and classified. The vessels are comprised of 20 identified commercial ships and 10 unidentified vehicles present in 8 h/day of Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) observation for two days. The underwater sounds from each of these ocean vessels received by the coherent hydrophone array are dominated by narrowband signals that are either constant frequency tonals or have frequencies that waver or oscillate slightly in time. The estimated bearing-time trajectory of a sequence of detections obtained from coherent beamforming are employed to determine the horizontal location of each vessel using the Moving Array Triangulation (MAT) technique. For commercial ships present in the region, the estimated horizontal positions obtained from passive acoustic sensing are verified by Global Positioning System (GPS) measurements of the ship locations found in a historical Automatic Identification System (AIS) database. We provide time-frequency characterizations of the underwater sounds radiated from the commercial ships and the unidentified vessels. The time-frequency features along with the bearing-time trajectory of the detected signals are applied to simultaneously track and distinguish these vessels.

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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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Face–Iris Multimodal Biometric Identification System

Electronics ◽

10.3390/electronics9010085 ◽

2020 ◽

Vol 9 (1) ◽

pp. 85 ◽

Cited By ~ 3

Author(s):

Basma Ammour ◽

Larbi Boubchir ◽

Toufik Bouden ◽

Messaoud Ramdani

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Gabor Filter ◽

Singular Spectrum Analysis ◽

Recognition Rate ◽

Evaluation Process ◽

Relevant Information ◽

Recognition System ◽

Identification System ◽

New Feature

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

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Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

Frequenz ◽

10.1515/freq-2015-0253 ◽

2016 ◽

Vol 70 (9-10) ◽

Author(s):

W. L. Lu ◽

J. W. Xie ◽

H. M. Wang ◽

C. Sheng

Keyword(s):

Frequency Analysis ◽

Simulated Data ◽

Radar Signal ◽

Electronic Warfare ◽

Time Frequency Analysis ◽

Time Frequency ◽

Frequency Domains ◽

Stationary Signals ◽

Radar Signals ◽

Electronic Intelligence

AbstractModern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time–frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time–frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

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

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00820-9 ◽

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.

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Selection of window for inter-pulse analysis of simple pulsed radar signal using the short time Fourier transform

International Journal of Engineering & Technology ◽

10.14419/ijet.v4i4.5139 ◽

2015 ◽

Vol 4 (4) ◽

pp. 531 ◽

Cited By ~ 1

Author(s):

Ashraf Adamu Ahmad ◽

Abdullahi Daniyan ◽

David Ocholi Gabriel

Keyword(s):

Fourier Transform ◽

Signal To Noise Ratio ◽

Additive White Gaussian Noise ◽

Side Lobe ◽

Radar Signal ◽

Short Time Fourier Transform ◽

Time Frequency ◽

Window Functions ◽

Pulse Analysis ◽

Short Time

The electronic intelligence (ELINT) system is used by the military to detect, extract information and classify incoming radar signals. This work utilizes short time Fourier transform (STFT) - time frequency distribution (TFD) for inter-pulse analysis of the radar signal in order to estimate basic radar signal time parameters (pulse width and pulse repetition period). Four well-known windows functions of different and unique characteristics were used for the localization of STFT to determine their various effects on the analysis. The window functions are Hamming, Hanning, Bartlett and Blackman window functions. Monte Carlo simulation is carried out to determine the performance of the signal analysis in presence of additive white Gaussian noise (AWGN). Results show that the lower the transition of main lobe width and higher the peak side lobe, the better the performance of the window function irrespective of time parameter being estimated. This is because 100 percent probability of correct estimation is achieved at signal to noise ratio of about -2dB for Bartlett, 4dB for both Hamming and Hanning, and 9dB for Blackman.

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Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Sensors ◽

10.3390/s18093103 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3103 ◽

Cited By ~ 11

Author(s):

Xuebao Wang ◽

Gaoming Huang ◽

Zhiwen Zhou ◽

Wei Tian ◽

Jialun Yao ◽

...

Keyword(s):

Fourier Transform ◽

Signal To Noise Ratio ◽

Recognition Rate ◽

Deep Belief Network ◽

Short Time Fourier Transform ◽

Belief Network ◽

Time Frequency ◽

Low Snr ◽

Emitter Recognition ◽

Short Time

To cope with the complex electromagnetic environment and varied signal styles, a novel method based on the energy cumulant of short time Fourier transform and reinforced deep belief network is proposed to gain a higher correct recognition rate for radar emitter intra-pulse signals at a low signal-to-noise ratio. The energy cumulant of short time Fourier transform is attained by calculating the accumulations of each frequency sample value with the different time samples. Before this procedure, the time frequency distribution via short time Fourier transform is processed by base noise reduction. The reinforced deep belief network is proposed to employ the input feature vectors for training to achieve the radar emitter recognition and classification. Simulation results manifest that the proposed method is feasible and robust in radar emitter recognition even at a low SNR.

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