Through-wall ultra-wideband radar human body respiration signal detection method based on Faster-RCNN network

An ultra-wideband radar and respiratory signal technology, applied in the evaluation of respiratory organs, measuring devices, diagnostic recording/measurement, etc., can solve the problems of unrecognizable vital signals, relying on denoising and enhanced algorithms, etc.

Active Publication Date: 2018-12-18
XI AN JIAOTONG UNIV
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Problems solved by technology

These methods can effectively remove non-static clutter interference, and successfully extract the number of living entities and their corresponding position and frequency

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  • Through-wall ultra-wideband radar human body respiration signal detection method based on Faster-RCNN network
  • Through-wall ultra-wideband radar human body respiration signal detection method based on Faster-RCNN network
  • Through-wall ultra-wideband radar human body respiration signal detection method based on Faster-RCNN network

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[0098] The present invention provides a method for detecting human breathing signal of through-wall ultra-wideband radar based on Faster-RCNN network. First, the original radar echo signal is preprocessed for denoising and weak signal enhancement, which mainly includes simple background clutter removal and adaptive Background removal, Advance Normalization (AN) method, signal enhancement, linear trend removal, automatic gain control method, and distance band pass filtering are six steps. Then, signal enhancement and denoising are continued for the echo processed in the first step to improve the signal-to-noise ratio. The main step is AN, slow-time moving average, and further adopts AN. After that, MATLAB software was used to image the echo signals in the previous preprocessing process into grayscale images. Finally, the grayscale images obtained in the previous step are screened, the data set is labeled, the Faster-RCNN network model is trained, the recognition test is performe...

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Abstract

The invention discloses a through-wall ultra-wideband radar human body respiration signal detection method based on a Faster-RCNN network. The method comprises the following steps: performing denoising and weak signal enhancement preprocessing on an original slow time-distance two-dimension ultra-wideband radar echo signal; and then continuously performing signal enhancement and denoising on the processed echo by adopting an Advance Normalisation method; imaging the echo signal in the preprocessing process as a gray image by adopting MATLAB, wherein the image width is corresponding to the echoslow time direction, and the image height is corresponding to the echo distance direction; and screening the obtained gray image, annotating a data set, and training a Faster-RCNN network model to perform human body respiration weak signal identification. The method disclosed by the invention is high in identification accuracy rate, high in detection speed, and capable of providing better technical support for the radar echo target detection, the life detection and practical technologies in actual applications.

Description

Technical field [0001] The invention belongs to the technical field of radar signal detection, and in particular relates to a method for detecting human breathing signals of a through-wall ultra-wideband radar based on a Faster-RCNN network. Background technique [0002] Ultra-wideband UWB: Ultra Wideband radar life electromagnetic detection uses ultra-wideband electromagnetic waves to detect micro-motion signs such as breathing or heartbeat. In recent years, it has been more and more widely used in actual post-earthquake rescue or safety detection of disaster environments. However, the human body sign micro-motion signal in the radar echo is generally a weak, narrow-band, quasi-periodic signal, and the signal is easily interfered. The time-varying and spatial-varying characteristics of the disaster relief scene and the diversity of noise causes also make Human target recognition has become very complicated, and radar signal recognition technology based on low-dimensional feature...

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Application Information

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IPC IPC(8): G01S13/88A61B5/08A61B5/113
CPCA61B5/0816A61B5/1135G01S13/888
Inventor 侯兴松王小瑞
Owner XI AN JIAOTONG UNIV
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