Method and system for low-probability-of-intercept radar signal waveform recognition

Patent metadata

Field	Value
Publication number	US-12352890-B2
Application number	US-202117382931-A
Country	US
Kind code	B2
Filing date	Jul 22, 2021
Priority date	Jul 22, 2020
Publication date	Jul 8, 2025
Grant date	Jul 8, 2025

Abstract

A method for recognizing a low-probability-of-interception (LPI) radar signal waveform includes: 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); combining, by the radar signal processor, the adaptive feature with the pre-defined analytical feature to generate a constructed adaptive feature; 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.

First claim

What is claimed is:

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{G 1 (F AD ), G 2 (F WVD ), G 3 (F CWD ), G 4 (F WL )}, wherein F is the constructed adaptive feature, F AD is the adaptive feature, F WVD is the WVD feature, F CWD is the CWD feature, F WL is the wavelet feature, G 1 , G 2 , G 3 , G 4 , 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.

2. The method according to claim 1 , wherein:
the adaptive feature includes one or more of an empirical mode decomposition (EMD) feature and a variational mode decomposition (VMD) feature.

3. The method according to claim 2 , wherein extracting the EMD feature includes:
identifying all extrema of s(t);
interpolating all local maxima to form an upper envelop u(t);
interpolating all local minima to form a lower envelop l(t);
calculating a mean envelop m(t), wherein m(t)=(u(t)+l(t))/2;
extracting the mean envelop m(t) from s(t) to obtain h(t), wherein h(t)=s(t)-m(t);
determining whether h(t) is an intrinsic mode function (IMF); and
in response to h(t) being an IMF and a number of obtained IMFs being less than a pre-configured number, iterating all above steps on a residue signal r(t), wherein r(t)=s(t)−h(t), otherwise iterating all above steps on h(t).

4. The method according to claim 2 , wherein extracting the VMD feature includes:
solving:

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wherein u k (t) is a decomposed IMF of s(t) with its center frequency ω k , δ(t) is a Dirac delta function, 1/πt is an impulse response of Hilbert transform,

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is an analytic signal, a real part of the analytic signal is s(t), an imaginary part of the analytic signal is a Hilbert transform of s(t).

5. The method according to claim 1 , wherein:
the WVD feature is calculated by:

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Assignees

• Intelligent Fusion Tech Inc

Inventors

• Huang Hui
• Li Yi
• Blasch Erik
• Pham Khanh
• Liu Jiaoyue
• Sullivan Nichole
• Shen Dan
• Chen Genshe

Classifications

• G01S7/021 Primary

  Auxiliary means for detecting or identifying radar signals or the like, e.g. radar jamming signals · CPC title

• G01S7/4021

  of receivers · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/02

  Neural networks · CPC title