A technique for measuring the linearity of a linearly frequency-modulated continuous wave (LFM-CW) signal is presented. It uses a delay-line and a mixer to sense the slope of the output of a sweep oscillator, so that ...A technique for measuring the linearity of a linearly frequency-modulated continuous wave (LFM-CW) signal is presented. It uses a delay-line and a mixer to sense the slope of the output of a sweep oscillator, so that the original form of frequency function deviated from idealized linear slope is retrieved by means of spectrum analysis. Consequently,the linearity of the LFM signal is determined. The formulation is performed based on the principle that an angle-modulated signal can be approximated by an amplitude-modulated signal if the modulation coefficient is sufficiently small. To examine the validity of the procedure and to study the effect of each parameter on the accuracy of measurement, a number of computer simulations has been made. The results of simulation show that the error of the measurement is less than 2%.展开更多
We demonstrate a high-resolution frequency-modulated continuous-wave dual-frequency LIDAR system based on a monolithic integrated two-section(TS) distributed feedback(DFB) laser. In order to achieve phase locking of t...We demonstrate a high-resolution frequency-modulated continuous-wave dual-frequency LIDAR system based on a monolithic integrated two-section(TS) distributed feedback(DFB) laser. In order to achieve phase locking of the two lasers in the TS-DFB laser, the sideband optical injection locking technique is employed. A high-quality linear frequency-modulated signal is achieved from the TS-DFB laser. Utilizing the proposed LIDAR system, the distance and velocity of a target can be measured accurately. The maximum relative errors of distance and velocity measurement are 1.6% and 3.18%, respectively.展开更多
Gesture recognition plays an increasingly important role as the requirements of intelligent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detectio...Gesture recognition plays an increasingly important role as the requirements of intelligent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detection algorithm with limited computational resources,this study improves the detection performance in terms of optimized features and interference filtering.The accuracy of the algorithm is improved by refining the combination of gesture features using a self-constructed dataset,and biometric filtering is introduced to reduce the interference of inanimate object motion.Finally,experiments demonstrate the effectiveness of the proposed algorithm in both mitigating interference from inanimate objects and accurately recognizing gestures.Results show a notable 93.29%average reduction in false detections achieved through the integration of biometric filtering into the algorithm’s interpretation of target movements.Additionally,the algorithm adeptly identifies the six gestures with an average accuracy of 96.84%on embedded systems.展开更多
The frequency-modulated continuous wave (FMCW) radar, known for its high range resolution, has garnered significant attention in the field of non-contact vital sign monitoring. However, accurately locating multiple ta...The frequency-modulated continuous wave (FMCW) radar, known for its high range resolution, has garnered significant attention in the field of non-contact vital sign monitoring. However, accurately locating multiple targets and separating their vital sign signals remains a challenging research topic. This paper proposes a scene-differentiated method for multi-target localization and vital sign monitoring. The approach identifies the relative positions of multiple targets using Range FFT and determines the directions of targets via the multiple signal classification (MUSIC) algorithm. Phase signals within the range bins corresponding to the targets are separated using bandpass filtering. If multiple targets reside in the same range bin, the variational mode decomposition (VMD) algorithm is employed to decompose their breathing or heartbeat signals. Experimental results demonstrate that the proposed method accurately localizes targets. When multiple targets occupy the same range bin, the mean absolute error (MAE) for respiratory signals is 3 bpm, and the MAE for heartbeat signals is 5 bpm.展开更多
文摘A technique for measuring the linearity of a linearly frequency-modulated continuous wave (LFM-CW) signal is presented. It uses a delay-line and a mixer to sense the slope of the output of a sweep oscillator, so that the original form of frequency function deviated from idealized linear slope is retrieved by means of spectrum analysis. Consequently,the linearity of the LFM signal is determined. The formulation is performed based on the principle that an angle-modulated signal can be approximated by an amplitude-modulated signal if the modulation coefficient is sufficiently small. To examine the validity of the procedure and to study the effect of each parameter on the accuracy of measurement, a number of computer simulations has been made. The results of simulation show that the error of the measurement is less than 2%.
基金This work was supported in part by the National Key R&D Program of China(No.2018YFA0704402)National Natural Science Foundation of China(Nos.61974165 and 61975075)+1 种基金National Natural Science Foundation of China for the Youth(No.62004105)Science and Technology Project,and Natural Science Foundation of Jiangsu Province(No.BE2019101)。
文摘We demonstrate a high-resolution frequency-modulated continuous-wave dual-frequency LIDAR system based on a monolithic integrated two-section(TS) distributed feedback(DFB) laser. In order to achieve phase locking of the two lasers in the TS-DFB laser, the sideband optical injection locking technique is employed. A high-quality linear frequency-modulated signal is achieved from the TS-DFB laser. Utilizing the proposed LIDAR system, the distance and velocity of a target can be measured accurately. The maximum relative errors of distance and velocity measurement are 1.6% and 3.18%, respectively.
基金supported by the National Natural Science Foundation of China(No.12172076)。
文摘Gesture recognition plays an increasingly important role as the requirements of intelligent systems for human-computer interaction methods increase.To improve the accuracy of the millimeter-wave radar gesture detection algorithm with limited computational resources,this study improves the detection performance in terms of optimized features and interference filtering.The accuracy of the algorithm is improved by refining the combination of gesture features using a self-constructed dataset,and biometric filtering is introduced to reduce the interference of inanimate object motion.Finally,experiments demonstrate the effectiveness of the proposed algorithm in both mitigating interference from inanimate objects and accurately recognizing gestures.Results show a notable 93.29%average reduction in false detections achieved through the integration of biometric filtering into the algorithm’s interpretation of target movements.Additionally,the algorithm adeptly identifies the six gestures with an average accuracy of 96.84%on embedded systems.
文摘The frequency-modulated continuous wave (FMCW) radar, known for its high range resolution, has garnered significant attention in the field of non-contact vital sign monitoring. However, accurately locating multiple targets and separating their vital sign signals remains a challenging research topic. This paper proposes a scene-differentiated method for multi-target localization and vital sign monitoring. The approach identifies the relative positions of multiple targets using Range FFT and determines the directions of targets via the multiple signal classification (MUSIC) algorithm. Phase signals within the range bins corresponding to the targets are separated using bandpass filtering. If multiple targets reside in the same range bin, the variational mode decomposition (VMD) algorithm is employed to decompose their breathing or heartbeat signals. Experimental results demonstrate that the proposed method accurately localizes targets. When multiple targets occupy the same range bin, the mean absolute error (MAE) for respiratory signals is 3 bpm, and the MAE for heartbeat signals is 5 bpm.
