In this letter, we perform a tightly-coupled fusion of a monocular camera, a 6-DoF IMU, and multiple position-unknown Ultra-wideband (UWB) anchors to construct an indoor localization system with both accuracy and robustness. Prior to this, there have been several works that have achieved satisfactory results by fusing UWB ranging measurements with visual-inertial system. However, these approaches still have some limitations: 1) these approaches either require the UWB anchor position to be calibrated in advance or the UWB anchor position estimation method used is not robust enough; 2) these approaches do not allow for dynamic changes to the number of UWB anchors in a tightly coupled estimator. Our approach uses visual object detection algorithm to provide UWB anchor initial position and refine it in the factor graph, using chi-square test algorithm to identify UWB ranging outliers. Based on the above two ideas, we implement a tightly coupled estimator that dynamically adjusts the number of UWB anchors, i.e. adding them to the factor graph when their ranging measurements are available and discarding them when their ranging measurements are outliers. These ideas improve the efficiency and robustness of the fusion about UWB ranging measurements with the visual-inertial system, as well as the easy setup of UWB anchors. Experimental results show that the proposed method outperforms previous methods in terms of estimating anchor position and improving localization accuracy.

Lens antenna array (LAA), as a promising architecture in millimeter-wave (mmWave) massive MIMO systems, which can considerably reduce the number of required radio-frequency (RF) chains. In this paper, we propose a high-resolution channel or angle-of-arrival (AoA) estimation scheme while taking into account the power leakage problem. Specifically, the criterion function is constructed based on DFT beam difference (DBD) to infer the real position of arrived signal. Subsequently, the novel antenna selection and path number estimation algorithms are proposed without prior knowledge of channel. A few groups of antennas are selected to connect to the RF chains, where each group contains only a small number of antennas. Thus, the beamspace multiple signal classification (MUSIC) algorithm can be implemented in low dimensional space to estimate the AoA(s) for each group. Then the channel estimation can be obtained by least square method after the reconstructions of steering vectors using the AoA estimations. Furthermore, the corresponding spatial smoothing algorithm is leveraged to tackle rank deficient problem at the scenario of constant path gains. Simulation results verify the effectiveness of the proposed scheme.

In emergency situations, individuals often experience panic and may struggle to find their way to safety. In such cases, the presence of trained leaders is crucial to provide guidance for evacuation and ensure the safety of those evacuating. However, determining the optimal positions of leaders in complex environments to guide the maximum number of pedestrians presents a significant challenge. To address this problem, we propose an Entropy-Based Maximum Coverage Model(EMCM) to enhance evacuation guidance. Initially, we employ the social force model to describe pedestrian movement characteristics, while the environmental features of the evacuation area are delineated using a navigation network. Subsequently, we introduce an urgency model grounded in evacuation entropy to quantify the urgency experienced by pedestrians. Thirdly, we frame the problem of leader positioning as an optimization challenge focused on maximizing entropy coverage, thereby increasing the number of individuals a leader can effectively guide. Finally, we employ AnyLogic to construct simulation scenarios that visualize the leadership process. This innovative method holds the potential to offer valuable support in the formulation of effective evacuation strategies during emergency situations.

In this paper, a novel mainlobe interference suppression method via eigen-projection processing and covariance matrix sparse reconstruction is proposed, which is able to work when the desired signal is present in the training data. Firstly, the proposed method uses the spatial spectrum algorithm to estimate the direction of arrival (DOA) of sources and the power of sources can be estimated by compressive sensing (CS). Then, the eigen-projection matrix is calculated via the result of DOA to suppress the mainlobe interference in echo data. Finally, adaptive weight vector is obtained by SINCM reconstruction. Compared with other methods, the proposed method can achieve better performance and stronger robustness.