Reconfigurable intelligent surfaces (RISs) are considered as an enabling technology for the upcoming sixth generation of wireless systems, exhibiting significant potential for radio localization and sensing. An RIS is usually treated as an anchor point with known position and orientation when deployed to offer user localization. However, it can also be attached to a user to enable its localization in a semi-passive manner. In this paper, we consider a static user equipped with an RIS and study the RIS localization problem (i.e., joint three-dimensional position and orientation estimation), when operating in a system comprising a single-antenna transmitter and multiple synchronized single-antenna receivers with known locations. We present a multi-stage estimator using time-of-arrival and spatial frequency measurements, and derive the Cramér-Rao lower bounds for the estimated parameters to validate the estimator's performance. Our simulation results demonstrate the efficiency of the proposed RIS state estimation approach under various system operation parameters.

Owing to large spectrum availability, millimeter wave (mmWave) communications are being proposed for 5th generation and beyond networks. However, the mmWaves are easily obstructed by objects, resulting in complete link blockage, which is more common in indoor communication. In this study, we propose to use the existing infrastructure of passive backscatter devices to establish links between the access point and a blocked legacy mobile user, with uncertain coordinates, in an indoor mmWave communication system. We set up backscatter devices in retro-reflective mode for the user to estimate the direction of arrival from them. To estimate the angles, we propose an estimator and derive its Cramer-Rao lower bound. Furthermore, while accounting for the antenna array configuration at both the user and the backscatter devices, we maximize the rate support at the user by optimizing the backscatter device reflection coefficient and the user's steering angle. The simulation results show that, by exploiting the backscatter devices already present in the environment with a sufficient number of antenna elements at the user, higher capacity is achieved as compared to that achieved by using a fixed re-configurable intelligent surface.

This paper presents experimental results of GPS free drone location using two antenna arrays. The experimental platform contains two synchronized stationary 4-by-4 rectangular antenna arrays. The arrays receive the signal emitted from a single antenna mounted on a drone. A Multiple signal classification (MUSIC) algorithm is applied to estimate the angle of arrival (AOA) at each array. The estimated AOA from both arrays is combined by the triangulation technique. We further employed the Extended Kalman Filter (EKF) to mitigate the estimated 3D location errors from the triangulation. To evaluate the performance of the AOA estimation, we conducted several measurements with different types of trajectories using a test-bed. The location estimation results of the proposed method are quantitatively compared and evaluated with the GPS trajectory of the drone. The estimation results have verified that the proposed experimental approach can localize and track the drone with reasonable accuracy.

Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure. For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation. However, with increased array size in extra-large multiple-input-multiple-output (XL-MIMO) systems and increased bandwidth at upper mmWave bands, the effect of channel spatial non-stationarity (SNS), spherical wave model (SWM), and beam squint effect (BSE) cannot be ignored. In this case, localization performance will be affected when an inaccurate channel model deviating from the true model is adopted. In this work, we employ the misspecified Cramer-Rao lower bound to lower bound the localization error using a simplified mismatched model while the observed data is governed by a more complex true model. The simulation results show that among all the model impairments, the SNS has the least contribution, the SWM dominates when the distance is small compared to the array size, and the BSE has a more significant effect when the distance is much larger than the array size.

We address the localization of a reconfigurable intelligent surface (RIS) for a single-input single-output multi-carrier system using bi-static sensing between a fixed transmitter and a fixed receiver. Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work. We first derive the Cramér-Rao bounds (CRBs) on the estimation error of the RIS position and orientation and the time of arrival (TOA) for the path transmitter-RIS-receiver. We propose a multi-stage low-complexity estimator for RIS localization purposes. In this proposed estimator, we first perform a line search to estimate the TOA. Then, we use the far-field approximation of the NF signal model to implicitly estimate the angle of arrival and the angle of departure at the RIS center. Finally, the RIS position and orientation estimate are refined via a quasi-Newton method. Simulation results reveal that the proposed estimator can attain the CRBs. We also investigate the effects of several influential factors on the accuracy of the proposed estimator like the RIS size, transmitted power, system bandwidth, and RIS position and orientation.

Wireless visual sensor networks (WVSN) have recently seen dramatic growth with technology development. These networks consist of a number of smart visual sensors (VSes) that can collect visual information, i.e., images and video captured in a network area. Optimizing the energy consumption and coverage are important contradictory challenges in WVSNs since increase in coverage leads to increase in energy consumption. Therefore, optimization of energy consumption by maintaining image quality defined by user or operator located in sink [quality of experience (QoE)] to be used in target tracking applications is addressed in this paper. The target coverage as well as the quality of the received image of the target are considered as the desired QoE. The novel two-dimensional target coverage model is also presented mathematically. This model is described as a function of the VS inherent parameters, the target position and the visual sensor position. Based on a convex optimization framework, a heuristic approach for the VS selection and the focal length adjustment is suggested to solve the optimization problem while maintaining high image quality. Simulation results are presented to verify the capability and efficiency of the proposed method in comparison with the optimal method (Exhaustive search method).

Nowadays, by emerging new technologies, demand for a wireless visual sensor networks (WVSN) has been significantly increased. By providing the vital visual data from such networks, they play a substantial role in the surveillance applications. The wireless visual sensors (VSes) are the main component of these networks, which are equipped with camera and transceiver module. Energy optimization and satisfying the quality of the captured visual data, are two main contradictable issues in this area of research, especially in the target tracking applications. Therefore, these two issues have been simultaneously investigated in this paper. The coverage of the tracked targets and the quality of the captured visual data are considered as the quality of experienced (QoE). The desired threshold of QoE is defined by user. To optimize energy consumption with regard to QoE constraints in a multi target tracking scenario, an effective VSes selection is proposed. This method has been developed based on the convex optimization framework. Besides, the focal length of the VSes is set optimally by executing the proposed algorithm. Simulation results show efficiency of the proposed method compared with the optimal method (exhaustive search).