Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform

Figures

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  Figure 1. An illustration of the received radar pulses.

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  Figure 2. The time-range matrix with (a) only the respiration signal, and (b) the signal with additive white Gaussian noise (AWGN) at an SNR of −10 dB.

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  Figure 3. Flowchart of the proposed detection method.

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  Figure 4. The (a) excess kurtosis, and (b) the kurtosis to SD (KSD) with a human subject, the (c) KSD without a human subject, (d) KSD in the target area, and (e) the spectrum of (d).

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  Figure 5. The time-frequency matrix using the wavelet transform (WT) decomposition (a) with a human subject at 9 m and (b) without a subject.

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  Figure 6. Signals in the time-frequency matrix (a) one in the region of interest (ROI), (b) one not in the ROI, (c) ten in the ROI, (d) ten not in the ROI and (e) all in the ROI.

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  Figure 7. The Welch power spectrum of (a) d1, (b) d2, (c) d3, (d) d4, (e) d5, (f) d6, (g) d7, and (h) d8.

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  Figure 8. The time windows with (a) increasing length, and (b) fixed length.

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  Figure 9. (a) The UWB radar and (b) the data for the first experiment with a human subject at a distance of 9 m from the radar.

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  Figure 10. The experimental setup for (a) subjects in front of the radar, (b) subjects at an angle to the radar, (c) through-wall detection outdoors, (d) through-wall detection indoors and (e) the actuator.

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  Figure 11. The results for the first experiment after (a) linear trend suppression (LTS), (b) range (fast time) filtering and (c) slow time filtering.

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  Figure 12. The KSD for the first experiment at distances of (a) 6 m, (b) 9 m, (c) 11 m and (d) 14 m from the radar.