Self-adaptive cancellation method based on deep neural network

A deep neural network and self-adaptive technology, applied in the field of self-interference cancellation, can solve problems such as poor cancellation effect

Active Publication Date: 2021-08-31
HARBIN ENG UNIV
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Problems solved by technology

[0005] The present invention proposes a method of self-adaptive cancellation based on deep neural network, which is used to solve the problem of traditional self-a

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  • Self-adaptive cancellation method based on deep neural network
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  • Self-adaptive cancellation method based on deep neural network

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Embodiment Construction

[0055] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0056] The technical solution of this invention is a DNN-based adaptive cancellation algorithm, which includes the following steps:

[0057] 1) Define the signal model received by the receiving antenna, including the transmitted signal power P f , power amplifier nonlinear distortion function G[ ] and carrier center frequency f c .

[0058] 2) Define the model of the nonlinear power amplifier.

[0059] 3) The target signal is nonlinearly modeled, and a large amount of data is used to train the DNN network.

[0060] 4) The signal generated after the original reference signal passes through the trained network is input into the adaptive filter as a new reference signal.

[0061] 5) Compare the signals before and after the adaptive filter cancellation.

[0062] Step 1: Define the signal model received by the receiving antenna, inclu...

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Abstract

The invention provides a self-adaptive cancellation method based on a deep neural network, wherein the method comprises the steps: 1), defining a signal model received by a receiving antenna, wherein the signal model comprises transmitting signal power Pf, a nonlinear distortion function G[.] of a power amplifier and carrier center frequency fc; 2) defining a model of a nonlinear power amplifier; 3) performing nonlinear modeling on a target signal, and training a DNN network by using a large amount of data; 4) taking a signal generated after an original reference signal passes through a trained network as a new reference signal and inputting the new reference signal into a self-adaptive filter; and 5) comparing the signals before and after cancellation of the self- adaptive filter. According to the method, a large amount of training prior information is used for simulating the nonlinear characteristic of a radar jammer power amplifier, the interference problem is solved, the amplitude of the signal is directly estimated, and algorithm steps are reduced through a large amount of data.

Description

technical field [0001] The invention belongs to the field of self-interference cancellation of radar jammers, in particular to an adaptive cancellation method based on a deep neural network. Background technique [0002] The elimination of self-coupling interference in radar jammers has always been a key technology and hot topic. In recent years, adaptive algorithms have been widely used to eliminate self-coupling interference. With the increasing complexity of the electromagnetic environment, self-interference signals are also increasingly difficult to estimate. It is difficult for traditional algorithms to adapt to the nonlinear signals generated by the power amplifier of radar jammers and effectively eliminate self-interference signals. [0003] With the deepening of people's research on adaptive algorithms, the application of adaptive algorithms in radar jammers is becoming more and more mature. At present, some adaptive filtering algorithms are mainly applied to elimin...

Claims

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Application Information

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IPC IPC(8): G01S7/38
CPCG01S7/38
Inventor 蒋伊琳李小钰王林森陈涛郭立民赵忠凯刘鲁涛
Owner HARBIN ENG UNIV
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