In response to the pressing requirement for prompt and precise heart rate acquisition during neonatal resuscitation, an adaptive motion artifact filter (AMF) is proposed in this study, which is based on the continuous wavelet transform (CWT) approach and takes advantage of the gradual, time-based changes in heart rate. This method is intended to alleviate the pronounced interference induced by random body movement (RBM) on radar detection in neonates. The AMF analyzes the frequency components at different time points in the CWT results. It extracts spectral peaks from each time slice of the frequency spectrum and correlates them with neighboring peaks to identify the existing components in the signal, thereby reducing the impact of RBM and ultimately extracting the heartbeat component. The results demonstrate a reliable estimation of heart rates. In practical clinical settings, we performed measurements on multiple neonatal patients within a hospital environment. The results demonstrate that even with limited data, its accuracy in estimating the resting heart rate of newborns surpasses 97%, and during infant movement, its accuracy exceeds 96%.

UAV-borne interferometric SAR has unique advantages and has attracted the attention of key research institutions in recent years. Baseline is a crucial parameter in InSAR processing, which is directly related to the accuracy of InSAR measurements. Compared with space-borne InSAR, UAV platform is difficult to keep the flight at the same track, and the flight attitude is unstable, which brings great difficulties to the baseline estimation of UAV-borne InSAR. In this paper, an improved baseline estimation method is proposed. This method derives the relationship between the interference phase period, the slant range, the distance between two ground points, and the phase difference through the interference geometric relationship. The distance between the two points is then substituted into the known parameters for baseline estimation using the least squares method. Compared with the original method, the proposed method can fully exploit the potential of the interferogram data by applying the least square method to solve the formula between flight parameters and baseline parameter to achieve baseline estimation. The simulated results and the flight test results of the original method and the proposed method are Compared. The absolute error, RMSE and standard deviation of both methods are calculated. It shows that the accuracy and robustness of the proposed method are significantly improved.

Intelligent Reflecting Surface (IRS) has emerged as a promising technology to significantly enhance the performance of wireless communication systems. This paper presents a comprehensive study of a double-IRS assisted multi-user communication system, proposing a novel joint optimization method. A joint optimization approach for passive beamforming in dual IRSs and active beamforming at the base station is applied to maximize the system sum rate. The problem is formulated as a non-convex optimization challenge, addressed through a proposed low-complexity iterative algorithm based on alternating optimization with semi-closed-form solutions. Simulation results validate our theoretical analysis and demonstrate the significant performance advantages of the proposed scheme.

A skeletal pose estimation method, named RVRU-Pose, is proposed to estimate the skeletal pose of vulnerable road users based on distributed non-coherent mmWave radar. In view of the limitation that existing methods for skeletal pose estimation are only applicable to small scenes, this paper proposes a strategy that combines radar intensity heatmaps and coordinate heatmaps as input to a deep learning network. In addition, we design a multi-resolution data augmentation and training method suitable for radar to achieve target pose estimation for remote and multi-target application scenarios. Experimental results show that RVRU-Pose can achieve better than 2 cm average localization accuracy for different subjects in different scenarios, which is superior in terms of accuracy and time compared to existing state-of-the-art methods for human skeletal pose estimation with radar. As an essential performance parameter of radar, the impact of angular resolution on the estimation accuracy of a skeletal pose is quantitatively analyzed and evaluated in this paper. Finally, RVRU-Pose has also been extended to the task of estimating the skeletal pose of a cyclist, reflecting the strong scalability of the proposed method.

A novel mmWave radar SLAM method is proposed to integrate multi-dimensional information, including velocity, spatial, RCS, and semantics to enable autonomous navigation in subterranean tunnel environments, which are characterized by low visibility and degraded conditions. By combining doppler odometry, scan matching, and feature matching modules, the proposed method effectively mitigates environmental degradation. Experimental results from various subterranean tunnel trajectories show that the proposed method achieves superior localization and mapping accuracy compared to existing radar SLAM methods, and even outperforms state-of-the-art Lidar SLAM methods. The achieved accuracy can reach the decimeter level in numerous subterranean tunnel tracks covering a total distance of over 500 meters.

A multiview fusion automotive radar simultaneous localization and mapping (SLAM) method is proposed to solve the problem of automotive radar point cloud mismatch caused by targets’ radar cross section (RCS) glint. The proposed method suppresses the RCS glint by fusing the images from multiple radars with different views. Meanwhile, to fully exploit the intensity information provided by the radar point cloud, a radar point cloud matching algorithm is proposed, which significantly improves the point cloud matching accuracy. The effect of factors, such as the number of radars and the radar layout on the suppression of RCS glint, is also analyzed. Finally, radar SLAM experiments were conducted in underground garages and outdoor roads. It is verified that the proposed method can improve the localization accuracy by an order of magnitude compared to existing radar SLAM methods, providing decimeter-level localization accuracy in seconds and constructing maps that are consistent with the real environment.

In this letter, a joint velocity ambiguity resolution and ego-motion estimation radar odometry method is proposed to solve the radar odometry algorithm invalidation caused by radar velocity ambiguity. First, a signal model for the radar odometry is developed for vehicle ego-motion estimation in the presence of velocity ambiguity. Then, a guided radar angle-velocity point cloud clustering algorithm is designed and the solution of the established signal model is presented. The estimation performance of radar odometry before and after velocity ambiguity resolution is quantitatively analyzed, which provides ideas for improving the estimation performance of the radar odometry. Finally, the radar-based vehicle pose estimation algorithms were compared in the presence of velocity ambiguity in real-world road environments. By applying the proposed algorithm to radar SLAM, a high-precision localization with an average translation error of less than 0.28 meters and an average rotation error of less than 0.97 degrees was achieved in a circular road track with a total mileage of more than 500 meters.

A millimeter-wave radar imaging mode combining transmit scanning and receive digital beamforming (TSCAN-RDBF) is proposed to solve the problems of strong target covering weak target and multipath interference in the practical application of MIMO mode. In the TSCAN-RDBF mode, spatial filtering is achieved by scanning the space in the transmission stage using a phased array. In the reception stage, the full high-resolution radar image is obtained by compressed sensing of the echo signal at the position of each transmit beam to achieve angle super-resolution within the transmit beam. The effect of MIMO mode and TSCAN-RDBF mode in weak object detection and indoor scene reconstruction are compared in the same millimeter-wave radar system. The experimental results show that the TSCAN-RDBF mode can efficiently detect weak targets with RCS of -82dBsm under interference from targets with RCS of 140dBsm. At the same time, the TSCAN-RDBF mode can effectively suppress multipath interference in the scene.

Micro SAR outperforms other Technologies in radar system study since it is small, light and concealable. Airborne dual-antenna micro synthetic aperture radar (SAR) system collects ground digital elevation information (DEM) from two different SAR images. Across-track interferometer mode based on UAV SAR reduces the mounted platform’s requirement, thus making it more convenient to perform flight experiments due to its flexible deployment. This type of advanced technology is highly demanding, and it is a hit trend of study field among most researchers. Real-time interferometric measurement is required to deal with a massive amount of complex data and process it within a short period. This feature also plays a significant role in multiple fields such as battlefield surveillance, unmanned fighter navigation, and blind landing of helicopters. However, the traditional way of serial processing can no longer satisfy the real-time processing requirements due to its massive amount of algorithm steps and complex computations. Therefore, this paper proposes a real-time InSAR processing algorithm and applied multi-core and multi-thread acceleration to boost the three most time-consuming modules: Flat-earth Phase Removal Module, Phase Filtering Module, and Phase to Height Conversion and Geocoding Module. In addition, using data block and parallel processing methods while doing calculations can significantly increase signal processing efficiency. Finally, this paper validates the effectiveness of the algorithm by testing it on the whole system. The results show that using an 8-thread computer, the parallel processing module’s time is only 15% compared to the original module. Moreover, the overall system operation speed is accelerated two times more than the previous traditional technique, the relative error of elevation is 0.3911 m. In conclusion, this paper validates that parallel processing is an effective way for real-time processing.

Foreign object debris (FOD) radar systems are often used to detect foreign materials that appear on the pavement that can pose as a threat to aircraft and personnel. Scan-mode millimeter-wave (MMW) radar with a fixed installation position is widely used as a leading solution. Small target detection is a puzzling challenge that requires a breakthrough for practical application. The conventional matched filter (MF) process along the range would encounter frequency leakage when strong scattering targets generate high sidelobe and mutual shield interference to object detection. The compressed sensing (CS) approach used for detection achieves efficiency by suppressing sidelobes and preventing small targets from being submerged by larger targets. The compressed imaging is introduced to retrieve the image scene with higher resolution and a small detectable object, such as 2-cm-diameter metal ball that equals −35-dBsm radar cross section (RCS). The long stripes scattering phenomenon can be reduced, which is beneficial to further detection processing. Furthermore, we demonstrate the concept of the MMW radar detection approach based on compressed imaging with the actual data. It validates the improved efficiency in at least two conditions: a small interval between multiple targets and strong scattering coverage of small targets. Nevertheless, we highlight some previously unrealized benefits of the FOD radar system application. There is no prior detection approach based on compressed imaging to enhance the performance of an FOD radar, as we present in this study.

Gesture recognition technology via millimeter wave radar is an efficient human-computer interaction (HCI) solution with a more significant environmental adaptive capability compared with optical sensors. Gesture segmentation is an essential step in gesture recognition process, and most of the previous work has focused on segmenting fixed-length discrete gestures, while less research has been done on segmentation algorithm for a continuous sequence of variable-length gestures. A gesture segmentation method for variable-length continuous gesture sequences based on micro-Doppler feature is proposed. Particularly, the Bidirectional One-Sided CFAR Algorithm (BOS-CFAR) which is derived from Constant False Alarm Rate Algorithm (CFAR) is skillfully designed to detect valid gesture frames. Then the valid frames is set as growth points and clustering operation is implemented to obtain the approximate segmentation parts of each gesture. Finally, the gesture domain is fine-tuned according to the maximum and minimum micro-Doppler values of neighborhood to achieve the segmentation task of variable-length continuous gestures with over 96.6% recall and nearly 100% precision of six gestures.

Multi-sensor fusion perception is one of the key technologies to realize intelligent automobile driving, and it has become a hot issue in the field of intelligent driving. However, because of the limited resolution of millimeter-wave radars, the interference of noise, clutter, and multipath, and the influence of weather on LiDAR, the existing fusion algorithm cannot easily achieve accurate fusion of the data of two sensors and obtain robust results. To address the problem of accurate and robust perception in intelligent driving, this study proposes a robust perception algorithm that combines millimeter-wave radar and LiDAR. Using a new method of spatial correction based on feature-based two-step registration, the precise spatial synchronization of the 3D LiDAR and 2D radar point clouds is realized. The improved millimeter-wave radar filtering algorithm is used to reduce the influence of noise and multipath on the radar point cloud. Then, according to the novel fusion method proposed in this study, the data of the two sensors are fused to obtain accurate and robust sensing results, which solves the problem of the influence of smoke on LiDAR performance. Finally, we conducted multiple sets of experiments in a real environment to verify the effectiveness and robustness of our method. Even in extreme environments such as smoke, we can still achieve accurate positioning and robust mapping. The environment map established by the fusion method proposed in this study is more accurate than that established by a single sensor. Moreover, the location error obtained can be reduced by at least 50%.

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

Radar SLAM has attracted wide attention due to its all-day and all-weather working characteristics in the last decade. The existing radar SLAM systems mainly adopt mechanically pivoting radar with simple principle and high resolution, but this kind of radar has disadvantages such as low frame rate, distortion of the radar image, and high cost. Although array snapshot radar has the advantages of high frame rate and low cost, its low azimuth resolution, multipath reflection, and angular glint limit its application in SLAM. This paper proposes a SLAM system developed on array snapshot radar. The system realizes angular super-resolution radar imaging through compressed sensing, which effectively solves the problems of poor azimuth resolution and multipath reflection of array snapshot radar. We also propose the corresponding point cloud extraction method and scan matching method, this method performs a centroid iterative closest point algorithm between the submaps, thereby effectively improving the interference of noise and angular glint. Experimental results show that our proposed array snapshot radar SLAM system can reduce the mean absolute trajectory error by more than 3 times compared with the existing system, and can show accuracy and robustness in various environments.

Robust and accurate localization and mapping are essential for autonomous driving. The traditional SLAM methods generally work under the assumption that the environment is static, while in dynamic environment the performance will be degenerate. In this paper, we propose an efficient and effective method to eliminate the influence of dynamic environment on SLAM by fusing LiDAR and mmW-radar, which significantly improves the robustness and accuracy of localization and mapping. The method fully utilizes the advantages of different measurement characteristics of two sensors, efficient moving object detection based on Doppler effect by radar and accurate object segmentation and localization by LiDAR, to remove the moving objects and uses the resulting filtered point cloud as the input of SLAM towards enhanced performance. The proposed approach is evaluated through experiments in various real world scenarios, and the results demonstrate the effectiveness of the method to improve the robustness and accuracy of SLAM in dynamic environments.

Synthetic aperture radar interferometry (InSAR), which is a combination of synthetic aperture radar (SAR) technique and interferometry technique, has been developed rapidly over several decades in Earth surface topography mapping and deformation detection. For conventional across-track InSAR, the baseline is a contradictory, because a large baseline will cause a high system sensitivity, but with a serious baseline decorrelation. Vortex electromagnetic (EM) waves with Orbital Angular Momentum (OAM) have been widely studied in the past decade for radar applications. Vortex EM wave presents to be a brand-new degree of freedom in SAR. In this paper, the OAM-based InSAR technique is proposed to obtain three-dimensional target information accurately without the existence of physical baseline. The OAM-based InSAR employs the interferometric phase of two OAM beams to derive the 3D information of the target. Because only one single OAM antenna is used to transmit and receive the two OAM beams, the baseline no longer exists, which could reduce the requirement of InSAR platform and eliminate the effect of baseline decorrelation. Moreover, without the steps of image registration and interferogram flattening, the process of the OAM-based InSAR is simpler. Simulation results demonstrate the effectiveness of the proposed technique and indicate that vortex EM waves can be exploited to obtain three-dimensional target information with high accuracy.