Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

The knowledge of heart and respiratory rates (HRs and RRs) is essential in assessing human body static. This has been associated with many applications, such as survivor rescue in ruins, lie detection, and human emotion detection. Thus, the vital signal extraction from radar echoes after pre-treatments, which have been applied using various methods by many researchers, has exceedingly become a necessary part of its further usage. In this review, we describe the variety of techniques used for vital signal extraction and verify their accuracy and efficiency. Emerging approaches such as wavelet analysis and mode decomposition offer great opportunities to measure vital signals. These developments would promote advancements in industries such as medical and social security by replacing the current electrocardiograms (ECGs), emotion detection for survivor status assessment, polygraphs, etc.

In this work, we propose a new light detection and ranging (LiDAR) super-resolution method for indoor and outdoor scenes in urban environments without training, converting to images and auxiliary sensors. To generate high-resolution (high-res) point clouds from the low-resolution (low-res) measurement of a sparse LiDAR, we deconstruct the whole problem into three sequential modules. First, to avoid feature interference, the raw data are segmented into the ground and nonground points. Then, these two types of points are reorganized into a more regular representation of the coordinates. Finally, the high-res point clouds are generated via a new geometric method. We present abundant synthetic and real data based on testing and evaluation of the proposed method. Qualitative and quantitative comparisons show that our method is effective and robust.

Time delay is a novel celestial navigation measurement that can provide the distance information of the spacecraft relative to the nearby celestial body. Combining time delay measurement with traditional star angle measurement can greatly improve navigation performance. However, the navigation accuracy will be affected by the ephemeris error of the nearby celestial body. To solve this problem, this article adds the position and velocity of the nearby celestial body to the state vector and estimates them online. The estimated values are used to replace the ephemeris data of the nearby celestial body in the measurement model. Besides, an event-triggered implicit unscented Kalman filter (IUKF) is proposed to reduce unnecessary calculation and shorten the running time. Simulation results indicate that the position error and velocity error of the proposed method are reduced by about 61% and 67% compared with that of the traditional time delay/star angle integrated navigation method (TDSA), respectively. The running time of the proposed method is reduced by about 88% compared with that of the TDSA with the augmented state (AS). In a word, the proposed method can greatly reduce the running time while maintaining high navigation accuracy.

A novel sequential convex (SCvx) optimization scheme via the Chebyshev pseudospectral method is proposed for efficiently solving the hypersonic reentry trajectory optimization problem which is highly constrained by heat flux, dynamic pressure, normal load, and multiple no-fly zones. The Chebyshev-Gauss Legend (CGL) node points are used to transcribe the original dynamic constraint into algebraic equality constraint; therefore, a nonlinear programming (NLP) problem is concave and time-consuming to be solved. The iterative linearization and convexification techniques are proposed to convert the concave constraints into convex constraints; therefore, a sequential convex programming problem can be efficiently solved by convex algorithms. Numerical results and a comparison study reveal that the proposed method is efficient and effective to solve the problem of reentry trajectory optimization with multiple constraints.