This paper proposes a real-time heart rate detection method based on 77 GHz FMCW radar. Firstly, the method establishes a new motion model according to respiratory and heartbeat rules, and extracts the motion signals of the chest and the abdomen; then, the random body motion (RBM) signal is eliminated by a combination of polynomial fitting and recursive least squares (RLS) adaptive filtering; lastly, multi-detection-point adaptive harmonics cancellation (AHC) is used to eliminate respiratory harmonics. In addition, the method introduces a spectrum analysis algorithm based on linear predictive coding (LPC). The experimental results show that the method can effectively eliminate the RBM signal and respiratory harmonics, and that the average real-time heart rate detection error rate is 2.925%.

Currently, Low-Rate Denial of Service (LDoS) attacks are one of the main threats faced by Software-Defined Wireless Sensor Networks (SDWSNs). This type of attack uses a lot of low-rate requests to occupy network resources and hard to detect. An efficient detection method has been proposed for LDoS attacks with the features of small signals. The non-smooth small signals generated by LDoS attacks are analyzed employing the time–frequency analysis method based on Hilbert–Huang Transform (HHT). In this paper, redundant and similar Intrinsic Mode Functions (IMFs) are removed from standard HHT to save computational resources and to eliminate modal mixing. The compressed HHT transformed one-dimensional dataflow features into two-dimensional temporal–spectral features, which are further input into a Convolutional Neural Network (CNN) to detect LDoS attacks. To evaluate the detection performance of the method, various LDoS attacks are simulated in the Network Simulator-3 (NS-3) experimental environment. The experimental results show that the method has 99.8% detection accuracy for complex and diverse LDoS attacks.

As the world’s population ages, technologies that enable long-term non-contact monitoring of patients are of great research significance. For this purpose, we propose a multi-person 2-D positioning method based on a 77 GHz FMCW radar. In this method, we first perform beam scanning processing on the data cube acquired by the radar and obtain the distance–Doppler–angle data cube. Then, we eliminate interfering targets through a multi-channel respiratory spectrum superposition algorithm. Finally, we obtain the distance and angle information of the target by the target center selection method. The experimental results show that the proposed method can detect the distance and angle information of multiple people.