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A Single Wavelength Airborne Sounding Radar Waveform Signal Extraction Method

A radar waveform and signal extraction technology, applied in the field of signal processing, can solve the problems of waveform signal detection model time-consuming, waveform confusion, low efficiency, etc., and achieve the effect of fast classification, fast identification, and simplified description

Active Publication Date: 2022-06-07
WUHAN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

In summary, the detection of single-wavelength ALB waveform signal has the following problems: (1) The waveform is mixed, and it is not easy to separate water and land; (2) The waveform signal detection model cannot be adapted to all waveforms; (3) The traditional waveform signal detection model Time-consuming and inefficient; (4) Traditional waveform signal detection models are often not suitable for all waveform signals

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  • A Single Wavelength Airborne Sounding Radar Waveform Signal Extraction Method
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  • A Single Wavelength Airborne Sounding Radar Waveform Signal Extraction Method

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Embodiment Construction

[0066] like figure 1 As shown, a method for extracting waveform signals of single-wavelength airborne sounding radar provided by the present invention specifically includes the following steps:

[0067] Step 1: Build a convolutional neural network for waveform classification, delete unnecessary waveforms, and further process valuable waveform signals;

[0068] Step 1:1: Based on statistical analysis, the full waveforms are divided into five categories: abnormal waveforms; supersaturated waveforms; land waveforms; surface waveforms and underwater waveforms.

[0069] Step 1.2: Model construction of waveform classification based on convolutional neural network, converting one-dimensional waveform data into two-dimensional data as input data. First, the input of low-level feature information is passed through the convolution module, and then the features of different scales are obtained through three pooling layers of different scales, which are used to reduce the dimensional rep...

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Abstract

The invention discloses a waveform signal extraction method of single-wavelength airborne sounding radar, and constructs a waveform classification model based on convolutional neural network to identify the full waveform of single-wavelength airborne sounding radar, including abnormal waveforms, oversaturated waveforms, land Waveforms, Sea Waveforms, and Underwater Waveforms. Eliminate water surface echoes and abnormal waveforms, obtain waveform segments through waveform segmentation, build a waveform segment classification model SAPSO-ELM to identify waveform segments, and detect the maximum peak value of waveform segments containing peaks. Waveforms without correct peak identification are subjected to the Lucy-Richardson Lucy deconvolution algorithm for signal extraction. Experiments show that the method is efficient, accurate and adaptable in waveform signal detection.

Description

technical field [0001] The invention belongs to the field of signal processing, and in particular relates to a waveform signal extraction method of a single-wavelength airborne sounding radar. Background technique [0002] Coastal management is a complex issue facing policymakers and scientists around the world. Monitoring the coast can be difficult due to the vast body of water that needs to be covered. Obtaining accurate, high-density bathymetric and topographic measurements in coastal environments is a challenging task. Deep and shallow water bathymetry data are mainly derived from echo sounders (single and multi-beam), although in coastal areas with low energy 0-5m and small depths, ships cannot method, and classical measurement techniques such as total station (TS) are usually used. ) or a real-time kinematic global navigation satellite system (RTK-GNSS), or by deploying an unmanned surface vessel (USV). In addition, satellite bathymetry can provide medium resolution...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/292G01S7/35G01S7/41G01S7/48G01S7/487
CPCG01S7/2923G01S7/354G01S7/417G01S7/4802G01S7/487Y02A90/30
Inventor 杨必胜纪雪
Owner WUHAN UNIV
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