This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9–5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%.

Non-contact vital sign monitoring, based on mm-wave Frequency Modulated Continuous Wave (FMCW) radar, can be widely applied in elderly care, clinical diagnoses, and in-vehicle occupant detection. The radar can monitor the Doppler signal reflected by the chest displacement, then analyze the phase change of echo to get the respiration and heartbeat signals. This paper proposes a frequency-tracking method based on an improved Birth-Death strategy to calculate vital sign rate in real-time. There are two main steps to estimate vital sign rate from frames within a second. Firstly, the M-Rife algorithm is employed to interpolate the frequency of each frame. Secondly, find the matching frequency points between the adjacent frames in a second by using an improved Birth-Death strategy and calculate the frequency ratio for each matching points link. Then, choose the longest link whose initial frequency is closer to the frequency at the last second. The value of the link tail will be a vital sign rate at the current second. The proposed method is simulated in a public non-contact vital dataset to verify its feasibility and accuracy. To further demonstrate the method, this paper carried out a series of experiments. The experiment results show that the method has an accuracy rate of 98% for the heartbeat rate in real-time, and reduces the effect of clutter and respiratory harmonics on the heartbeat rate.

This paper investigates the prospective n-point (PnP) problem and provides an effective, dependable, and fast optimal solution. The challenging pose estimation problem is changed into the ideal circumstance for solving the transition matrix by estimating the position and orientation of the camera based on known reference points. This method meets both the geometric optimality and the statistical optimal solution by accounting for observation and propagation uncertainty in the solution process. In addition, in order to further optimize the error caused by the mapping process, this paper introduces the L-BFGS algorithm for optimal processing. It saves time and cost because it can iterate the optimal transition matrix and systematic error synchronously. In this paper, the algorithm simulation tests are carried out in the standard case, the quasi-singular case, and the planar case. The results of the experiments demonstrate that the strategy suggested in this paper can successfully address the issue of camera position estimation. Compared with the existing advanced technology, the accuracy is improved by about 8% - 13%, and the time cost is low.

As an important technology in 3D vision, point-cloud registration has broad development prospects in the fields of space-based remote sensing, photogrammetry, robotics, and so on. Of the available algorithms, the Iterative Closest Point (ICP) algorithm has been used as the classic algorithm for solving point cloud registration. However, with the point cloud data being under the influence of noise, outliers, overlapping values, and other issues, the performance of the ICP algorithm will be affected to varying degrees. This paper proposes a global structure and adaptive weight aware ICP algorithm (GSAW-ICP) for image registration. Specifically, we first proposed a global structure mathematical model based on the reconstruction of local surfaces using both the rotation of normal vectors and the change in curvature, so as to better describe the deformation of the object. The model was optimized for the convergence strategy, so that it had a wider convergence domain and a better convergence effect than either of the original point-to-point or point-to-point constrained models. Secondly, for outliers and overlapping values, the GSAW-ICP algorithm was able to assign appropriate weights, so as to optimize both the noise and outlier interference of the overall system. Our proposed algorithm was extensively tested on noisy, anomalous, and real datasets, and the proposed method was proven to have a better performance than other state-of-the-art algorithms.

Target tracking is an important field of investigation in wireless sensor networks. When multiple targets are closely spaced, their measurement points are mixed together due to the insufficient accuracy of the sensor. This may bring some difficulties in determining the positions of observation points required by subsequent algorithms and applications. Most of the known tracking algorithms are derived from the Kalman filter, extended Kalman filter, and particles filter. In this paper, a novel measurement data classification algorithm based on support vector machine (SVM) is provided. SVM and Kalman filter are combined to obtain the updated classification line at each sampling period, and the sampling points would be classified by the updated classification line to calculate the coordinates of the corresponding observation points, which are then used to estimate the precise positions of two targets. A series of simulations and experiments are carried out to validate the presented algorithm on classifying and tracking two targets moving closely. Simulation results for a maneuvering targets classification example illustrate the feasibility of the new algorithm, as well as experimental and quantitative results from the practical data validate the effectiveness and stability of our proposal in contrast with existing methods.