{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:25:08Z","timestamp":1774023908213,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,27]],"date-time":"2019-05-27T00:00:00Z","timestamp":1558915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qiang Guo","award":["No.HEUCFG201832"],"award-info":[{"award-number":["No.HEUCFG201832"]}]},{"name":"Qiang Guo","award":["No.2016YFC0101700"],"award-info":[{"award-number":["No.2016YFC0101700"]}]},{"name":"Qiang Guo","award":["No.GX16A007"],"award-info":[{"award-number":["No.GX16A007"]}]},{"name":"Qiang Guo","award":["No.702SKL201720"],"award-info":[{"award-number":["No.702SKL201720"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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\u2013frequency transform on the LPI radar signal to obtain a two-dimensional time\u2013frequency image. Then, the time\u2013frequency image is preprocessed (binarization and size conversion). The preprocessed time\u2013frequency 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 \u22124 dB.<\/jats:p>","DOI":"10.3390\/sym11050725","type":"journal-article","created":{"date-parts":[[2019,5,27]],"date-time":"2019-05-27T11:19:27Z","timestamp":1558955967000},"page":"725","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["LPI Radar Waveform Recognition Based on CNN and TPOT"],"prefix":"10.3390","volume":"11","author":[{"given":"Jian","family":"Wan","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1474-9851","authenticated-orcid":false,"given":"Xin","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Qiang","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"30342","DOI":"10.1109\/ACCESS.2018.2845102","article-title":"LPI Radar Waveform Recognition Based on Multi-Branch MWC Compressed Sampling Receiver","volume":"6","author":"Chen","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1109\/TAES.2017.2667142","article-title":"Automatic intrapulse modulation classification of advanced LPI radar waveforms","volume":"53","author":"Kishore","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1049\/iet-rsn.2018.5271","article-title":"Reduced complexity and near optimum detector for linear-frequency-modulated and phase-modulated LPI radar signals","volume":"13","author":"Dezfuli","year":"2019","journal-title":"IET Radar Sonar Navig."},{"key":"ref_4","first-page":"63","article-title":"An Antenna for a Mast-Mounted Low Probability of Intercept Continuous Wave Radar","volume":"61","author":"Jenn","year":"2019","journal-title":"J. Abbr."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zilberman, E.R., and Pace, P.E. (2006, January 8\u201311). Autonomous time-frequency morphological feature extraction algorithm for LPI radar modulation classification. Proceedings of the 2006 International Conference on Image Processing, Atlanta, GA, USA.","DOI":"10.1109\/ICIP.2006.312851"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1049\/iet-rsn.2013.0088","article-title":"Robust radar waveform recognition algorithm based on random projections and sparse classification","volume":"8","author":"Ma","year":"2013","journal-title":"IET Radar Sonar Navig."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/JSTSP.2007.897055","article-title":"Automatic radar waveform recognition","volume":"1","author":"Lunden","year":"2007","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11074","DOI":"10.1109\/ACCESS.2017.2716191","article-title":"Convolutional Neural Networks for Automatic Cognitive Radio Waveform Recognition","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_9","unstructured":"Ming, Z., Ming, D., Lipeng, G., and Lutao, L. (2017). Neural Networks for Radar Waveform Recognition. Symmetry, 9."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"59","DOI":"10.3390\/electronics7050059","article-title":"Radar Waveform Recognition Based on Time-Frequency Analysis and Artificial Bee Colony-Support Vector Machine","volume":"7","author":"Lutao","year":"2018","journal-title":"Electronics"},{"key":"ref_11","unstructured":"Zhang, M., Liu, L., and Diao, M. (2017). LPI Radar Waveform Recognition Based on Time-Frequency Distribution. Sensors, 16."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/TEC.2012.2236557","article-title":"Improvement of the Hilbert method via ESPRIT for detecting rotor fault in induction motors at low slip","volume":"28","author":"Xu","year":"2013","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.ymssp.2013.01.017","article-title":"Recent advances in time \u0170 frequency analysis methods for machinery fault diagnosis: A review with application examples","volume":"38","author":"Feng","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1002\/acs.2864","article-title":"Adaptive time-frequency representation for weak chirp signals based on Duffing oscillator stopping oscillation system","volume":"32","author":"Hou","year":"2018","journal-title":"Int. J. Adapt. Control Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"43874","DOI":"10.1109\/ACCESS.2018.2864347","article-title":"Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network","volume":"6","author":"Qu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1080\/00207217.2019.1576232","article-title":"An efficient inexact Full Adder cell design in CNFET technology with high-PSNR for image processing","volume":"106","author":"Ataie","year":"2019","journal-title":"Int. J. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1080\/01431161.2018.1547450","article-title":"SRAD-CNN for adaptive synthetic aperture radar image classification","volume":"40","author":"Zhang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","article-title":"Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.patrec.2019.02.016","article-title":"Classification of myocardial infarction with multi-lead ECG signals and deep CNN","volume":"122","author":"Baloglu","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1109\/TIP.2018.2881830","article-title":"A Local Metric for Defocus Blur Detection Based on CNN Feature Learning","volume":"28","author":"Zeng","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.measurement.2019.02.078","article-title":"Operational modal parameter identification based on PCA-CWT","volume":"139","author":"Zhang","year":"2019","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.jclepro.2018.11.226","article-title":"Maximization of extraction of Cadmium and Zinc during recycling of spent battery mix: An application of combined genetic programming and simulated annealing approach","volume":"218","author":"Yun","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.nucengdes.2019.03.025","article-title":"Judgement of critical state of water film rupture on corrugated plate wall based on SIFT feature selection algorithm and SVM classification method","volume":"347","author":"Wang","year":"2019","journal-title":"Nucl. Eng. Des."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/5\/725\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:53:54Z","timestamp":1760187234000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/5\/725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,27]]},"references-count":23,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["sym11050725"],"URL":"https:\/\/doi.org\/10.3390\/sym11050725","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,27]]}}}