Ultra wide band (UWB) life signal data processing method and device based on singular value decomposition

Abstract

The invention provides an ultra wide band (UWB) life signal data processing method and device based on singular value decomposition, and the method comprises the steps: transmitting a continuous pulse sequence through an ultra wide band radar life detector, carrying out the detection, obtaining a plurality of continuous groups of echo signals containing vital sign signals, and carrying out the preprocessing of an echo signal matrix; performing singular value decomposition on the preprocessed echo signal matrix to obtain a multi-order singular value and a singular matrix component; extracting a spatial feature vector and a time feature vector corresponding to each order of singular value from the singular matrix component; and performing compression storage processing on the singular values of each order, the spatial feature vectors and the time feature vectors, so that the problems of a large amount of clutter and noise interference, spatial-temporal feature coupling, overlarge data volume, occupation of a large amount of storage space and the like of the echo signals of the ultra-wideband radar life detector can be reduced, and meanwhile, the singular values of the signals are processed, so that the accuracy of the echo signals of the ultra-wideband radar life detector is improved. And the timeliness of vital sign signal processing is improved.

Description

technical field [0001] The present application relates to the technical field of signal processing, in particular to a method and device for processing ultra-wideband UWB vital signal data based on singular value decomposition. Background technique [0002] Ultra-Wideband Radar (UWB) radar has the advantages of high distance resolution, strong penetrating ability, low power consumption, and strong anti-interference ability. It has been widely used in all aspects, and has great potential in communication, detection, medical treatment, remote sensing and other aspects. Among them, the use of ultra-wideband radar for life detection is an important application in this field at present. Human life signals, including respiration and heartbeat, are extremely weak low-speed signals. During the detection process of life signals, body surface micro-movements brought by human activities, environmental noise, linear drift caused by equipment, detector noise, thermal noise, etc. If the...

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Problems solved by technology

Median filtering is a nonlinear signal processing technology that can effectively suppress noise based on sorting statistics theory. The basic principle is to replace the value of a point in the digital sequence with the median value of each point in a neighborhood of the point, thereby eliminating Isolated noise points, this method is simple and fast, but it is easy to cause discontinuity, and it is difficult to remove multi-coupling interference signals

Gaussian filtering is a linear smoothing filter, suitable for eliminating Gaussian noise, but it is necessary to select the appropriate Gaussian filter function width and Gaussian filter template length, it is difficult to find a suitable method to determine the optimal parameters in the actual process

Wavelet transform denoising is a new mathematical theory and method developed in the past ten years. It has the characteristics of multi-resolution analysis and can focus on any details of the signal for multi-resolution time-frequency domain analysis. The algorithm is simple and clear, and the calculation The speed is fast, but its scope of application is not very wide. It is very effective when the frequency range of the noise is known in a specific situation and the frequency bands of the signal and noise are separated from each other. For the white noise that exists widely in practical applications, its denoising effect is better. At the same time, if the fundamental wave parameters are not selected properly, it may cause problems such as signal distortion

The empirical mode decomposition method is a linear and steady-state spectrum analysis method based on Fourier transform, which can decompose complex signals into a limited number of intrinsic mode functions. Compared with wavelet transform, this method is based on the time scale characteristics of the data itself. Signal decomposition does not need to pre-set any basis functions. However, the AM and FM signals obtained by empirical mode decomposition have modal mixing phenomenon, and the terminal effect will also affect the decomposition effect, which greatly reduces the denoising effect.

[0004] In this way, there are problems such as a large amount of clutter and noise interference in the echo signal of the ultra-wideband radar life detector, coupling of time and space features, excessive data volume, and a large amount of storage space. At the same time, the timeliness of vital sign signal processing is not high.

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