US 12,352,890 B2
Method and system for low-probability-of-intercept radar signal waveform recognition
Hui Huang, Germantown, MD (US); Yi Li, Germantown, MD (US); Erik Blasch, Arlington, VA (US); Khanh Pham, Kirtland AFB, NM (US); Jiaoyue Liu, Germantown, MD (US); Nichole Sullivan, Germantown, MD (US); Dan Shen, Germantown, MD (US); and Genshe Chen, Germantown, MD (US)
Assigned to INTELLIGENT FUSION TECHNOLOGY, INC., Germantown, MD (US)
Filed by Intelligent Fusion Technology, Inc., Germantown, MD (US)
Filed on Jul. 22, 2021, as Appl. No. 17/382,931.
Claims priority of provisional application 63/055,110, filed on Jul. 22, 2020.
Prior Publication US 2024/0402298 A1, Dec. 5, 2024
Int. Cl. G01S 7/41 (2006.01); G01S 7/02 (2006.01); G01S 7/40 (2006.01)
CPC G01S 7/417 (2013.01) [G01S 7/021 (2013.01); G01S 7/4021 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for recognizing a low-probability-of-interception (LPI) radar signal waveform, comprising:
obtaining, by a radar signal receiver, an LPI radar signal s(t), s(t) varying with time t;
extracting, by a radar signal processor, an adaptive feature and a pre-defined analytical feature from the LPI radar signal s(t), wherein the pre-defined analytical feature includes a Wigner-Ville Distribution (WVD) feature, a Choi-William Distribution (CWD) feature, and a wavelet feature;
combining, by the radar signal processor, the adaptive feature with the pre-defined analytical feature to generate a constructed adaptive feature according to:
F=ψ{G1(FAD), G2(FWVD), G3(FCWD), G4(FWL)}, wherein F is the constructed adaptive feature, FAD is the adaptive feature, FWVD is the WVD feature, FCWD is the CWD feature, FWL is the wavelet feature, G1, G2, G3, G4, are linear or non-linear operations, and ψ is a data fusion operation; and
applying, by the radar signal processor, a convolutional neural network (CNN) model to classify the constructed adaptive feature to recognize the LPI radar signal waveform.