Radar distance super-resolution calculation method based on sparse Bayesian learning algorithm

A sparse Bayesian, learning algorithm technique for radar range super-resolution computing

Active Publication Date: 2021-09-17
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of radar range resolution processing, and propose a radar range super-resolution calculation method based on sparse Bayesian learning algorithm

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  • Radar distance super-resolution calculation method based on sparse Bayesian learning algorithm
  • Radar distance super-resolution calculation method based on sparse Bayesian learning algorithm
  • Radar distance super-resolution calculation method based on sparse Bayesian learning algorithm

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Embodiment Construction

[0064] Embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0065] like figure 1 As shown, the present invention provides a radar distance super-resolution calculation method based on sparse Bayesian learning algorithm, comprising the following steps:

[0066] S1: Perform pulse compression on the echo signal of the radar to determine the radar signal segment of the group target;

[0067] S2: Perform frequency deskewing processing on the group target radar signal segment to obtain a single frequency signal;

[0068] S3: Use the sparse Bayesian learning algorithm to perform super-resolution processing on the single-frequency signal to obtain the frequency point position of the group target radar signal;

[0069] S4: Determine the radar distance according to the frequency point position of the group target radar signal.

[0070] In the embodiment of the present invention, in step S1, performing pulse compression ...

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Abstract

The invention discloses a radar distance super-resolution calculation method based on a sparse Bayesian learning algorithm. The method comprises the following steps of S1, carrying out the pulse compression of an echo signal of a radar, and determining a group target radar signal segment, S2, performing frequency dechirp processing on the group target radar signal segment to obtain a single-frequency signal, S3, performing super-resolution processing on the single-frequency signal by using a sparse Bayesian learning algorithm to obtain a frequency point position of a group target radar signal, and S4, determining a radar distance according to the frequency point position of the group target radar signal. Compared with a traditional pulse compression processing algorithm, the resolution effect of the radar distance super-resolution algorithm based on the sparse Bayesian learning algorithm can be improved by more than one time.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a radar distance super-resolution calculation method based on a sparse Bayesian learning algorithm. Background technique [0002] In conventional processing, the radar range resolution depends on the bandwidth of the baseband signal transmitted by the radar. Under the conditions of modern warfare, there are dense multi-targets or a single extended target, including formation targets formed by aircraft formation flying, ballistic missile group targets flying in a vacuum segment containing decoys, fragments, and warheads, and bombers and other larger targets. To expand etc. How to distinguish group targets with small spacing, or cover targets at separation points, or effectively identify target features, has important value and significance in military command decision-making. Therefore, under the condition of existing radar measurement accuracy (or ba...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/295G01S7/41G01S13/28G06N7/00G06T3/40
CPCG01S7/295G01S7/41G01S13/282G06T3/4053G06N7/01
Inventor 曹建蜀陈岁新于昕凝
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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