The signal output by the array sensor is generally very weak, with a large dynamic range and a wide range of signal frequencies. In order to solve the problem of accurate measurement of weak signals with wide frequency and large dynamic range, this paper proposes a design method of sub-band filtering and variable gain amplifying circuit based on the analog switch, divides the signal into four frequency bands, and designs four groups of second-order voltage control filter, and adjust the magnification for different frequency signals, and only need to switch the corresponding resistance and capacitance to realize the switching of signal processing circuits of different frequency bands, which greatly optimizes the circuit structure. In order to reduce the interference in the transmission process, a single-ended differential circuit is designed to transmit the processed signal to the subsequent acquisition system for acquisition. After the simulation test, the signal conditioning circuit can effectively improve the signal-to-noise ratio of the detection signal and improve the measurement accuracy.
Radar-based dynamic gesture recognition has great potential in
human-computer interaction (HCI) applications. With the development of
wideband radar, the radar signal of hand gestures is often represented
by the three-dimensional (3D) range-Doppler-time cube, which is mainly
processed by multichannel two-dimensional (2D) CNNs and 3D CNNs.
However, the utilization and fusion of different kinds of features in
existing networks is simple and not well optimized. In this paper, an
efficient attention method named three-dimensional united attention
(3D-UA) module in 3D space is proposed. The 3D-UA module applies a
multi-scale pyramid convolution spatially, extracts channel attention
weights on feature maps and captures the global temporal cues
simultaneously. Furthermore, a network named 3D-UANet is proposed by
replacing the 3x3x3 convolution with the 3D-UA module in the 3D-ResNet.
3D-UANet can efficiently extract the range-Doppler-time features of
gestures. Experimental results show that the proposed method has good
generalization performance on data from subjects in complex scenes.
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