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Radar intelligent anti-composite interference method and device

An intelligent and radar technology, applied in the field of radar, can solve the problems such as the difficulty of extracting pure interference samples, the inability to completely suppress interference and realize target detection, etc., and achieve rapid interference suppression and target detection, real-time end-to-end processing Effect

Inactive Publication Date: 2022-06-07
AIR FORCE EARLY WARNING ACADEMY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the MVDR method needs to extract pure interference samples, which is difficult to achieve in practice
Especially now that the interfering party will release a variety of combined interferences at the same time in the future, that is, composite interference, then there is a problem that it is difficult to extract pure interference samples
If the extracted interference samples are impure, the MVDR method cannot completely suppress the interference and achieve target detection

Method used

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  • Radar intelligent anti-composite interference method and device

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Experimental program
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Embodiment 1

[0034] We first introduce the input and label data of the deep learning network. Assuming that there is 1 target, 1 main lobe suppressing jammer and 1 side lobe spoofing jammer in the space, the array is a M A uniform linear array composed of array elements, the array element spacing d is half wavelength, the received data of the array is R means:

[0035]

[0036] of which X Indicates that the array receives data, L represents the number of snapshots, represents the conjugate transpose, respectively represent the covariance matrix of the target signal, the covariance matrix of the interference and the covariance matrix of the noise. will R vectorize to vector r , extract its imaginary and real parts to reconstruct a new vector:

[0037]

[0038] of which means to take the value of the imaginary part, Represents the real part value. the above vector It is the training sample data of the deep learning network.

[0039] Ideal weights for MVDR methods w ...

Embodiment 2

[0062] The method of the present invention is described below in conjunction with a specific embodiment. The method of the present invention is given specific training sample data and label data, and given specific network training parameters, and compared the implementation effect of the present invention and the prior art. ground, the method includes:

[0063] (1) Collect training sample data and label data required for neural deep learning network training: The training sample data is a vectorized array receiving data covariance matrix, and the label data is the ideal MVDR optimal weight;

[0064] Here, computer simulation is used to generate corresponding training sample data and label data, assuming the number of array elements M =16, its half-power beamwidth is approximately , the main beam is directed to , the direction of arrival of the main lobe suppression interference is , the direction of arrival of the sidelobe spoofing jamming is , the interference-to-noi...

Embodiment 3

[0078] Further, the present invention also provides a radar intelligent anti-composite interference device: comprising at least one processor and a memory, the at least one processor and the memory are connected through a data bus, and the memory can store data that can be stored by the at least one memory. Instructions executed by the processor, after being executed by the processor, the instructions are used to complete the above-mentioned radar intelligent anti-complex interference method.

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Abstract

The invention discloses a radar intelligent anti-composite interference method. The method comprises the following steps: collecting training sample data and label data required by neural deep learning network training; constructing a deep learning network, and determining the size of the deep learning network, a gradient descent optimization algorithm, an activation function and a loss function; inputting the training sample data and the label data into a deep learning network for training to obtain a trained deep learning network; generating a vectorized array receiving data covariance matrix again, inputting the vectorized array receiving data covariance matrix into the trained deep learning network for testing, and outputting to obtain a weight vector; and multiplying the weight vector obtained by the test output by the tested array receiving data, namely, the interference suppression process. The method provided by the invention has the advantages that a sample selection process is not required to be interfered, interference and target detection can be quickly suppressed, processes with large calculation amount, such as matrix multiplication and inversion, are not required, end-to-end processing is realized, and the like. The invention also provides a corresponding radar intelligent anti-composite interference device.

Description

technical field [0001] The invention relates to the technical field of radar, in particular to a radar intelligent anti-composite interference method and device, which is suitable for all types of phased array radars and can suppress compound interference and extract targets. Background Art [0002] Radar is the "clairvoyant" in modern warfare, capable of acquiring battlefield information and situational awareness. Because of this, the enemy tries to destroy the detection performance of the radar by releasing jamming. In order to maximize the detection efficiency of radar, technicians have explored various anti-jamming methods and achieved great results. These anti-jamming methods are derived based on signal models, and when errors occur in the radar system, different methods will have different degrees of performance degradation. In general, the Minimum Variance Distortionless Response (MVDR) method is widely used in practice and can more robustly suppress sidelobe interf...

Claims

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Application Information

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IPC IPC(8): G01S7/41G01S7/36G06N3/04G06N3/08
CPCG01S7/417G01S7/36G06N3/08G06N3/045Y02T10/40
Inventor 李槟槟林志凯陈辉许霄龙李锡武杜庆磊刘维建王永良
Owner AIR FORCE EARLY WARNING ACADEMY
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