Occupancy Grid Mapping for Personal Radar Applications

Next fifth generation (5G) of mobile wireless communication foresees the use of mm-wave technology to boost communication at an unprecedented scale, thanks to the large available bandwidth [1] . In addition, the move-up in the frequency spectrum allows to include a large number of antennas into a small area, thus enabling their integration into portable devices [2 , 3] . In this way, such a technological perspective can be exploited to add new functionalities in addition to communication. For example, the laser-like beamsteering allowed by massive arrays at mm-wave can be used to automatically scan and reconstruct the topology of the surrounding environment. Such an idea, namely personal radar, has been recently proposed in theory and its feasibility assessed by experiments [4 \u2013 [5]6] . In these works, the performance has been investigated through the adoption of a grid-based mapping approach relying on an extended Kalman-Filter (EKF): the environment has been discretized in a grid of cells whose root-radar cross section (RCS) values constitute the state vector to be estimated starting from the backscattered radar response [7 , 8] . To simplify the analysis, the state of the system has been modeled as a Gaussian random vector whose mean vector and covariance matrix are updated during the mapping process as soon as new measurements are collected [5 , 9] . The main limitation of this model is that the Gaussian assumption does not capture the underlaying bimodal nature of the phenomenon, i.e., each cell is empty or occupied. In laser-based mapping systems, occupancy grid (OG) methods are usually considered to model this bi-modality by exploiting the basic assumption that laser beam illuminates only one cell per time [10] . This is not the case in radio-based radars where the shape of the radiation pattern is such to illuminate an area composed of several cells, thus making existing OG methods not appropriate due to the inherent cross-correlation between cells that is not zero [11]

Direct position estimation from wavefront curvature with single antenna array

In this paper we investigate the possibility to perform direct positioning by retrieving information from the wavefront curvature. Despite such an approach has been considered in the past at microwave and acoustic frequencies using extremely large antennas, it is of interest to investigate its potential exploitation at mm-wave with practical size antennas in the context of next 5G systems. Thus, here we first consider a dedicated model to gather the source position information from the wavefront curvature for different array architectures, i.e., traditional and lens-based arrays, and successively we derive the maximum likelihood estimator to investigate the attainable performance. Results, obtained for different number of antennas, i.e., for different array apertures, confirm the possibility to achieve interesting positioning performance using a single antenna array with limited dimensions

AOA estimation with EM lens-embedded massive arrays

Recently, EM lens-embedded massive array antennas have been proposed for next 5G mobile wireless communications, as the adoption of a lens allows to discriminate the AOA of signals in the analog domain, with the possibility to preserve the processing complexity lower with respect to traditional massive arrays. In fact, in such a way, complex ADC chains can be avoided and the number of required antennas can be decreased. By exploiting these advantages, in this paper we study the possibility to use a single EM lens massive array at mm-wave for the AOA estimation of the received signal. In this perspective, ML estimator and practical approaches, tailored for the considered scenario, are derived. Results, obtained for different number of antennas, confirm the possibility to achieve interesting AOA estimation performance with an extremely compact architecture

Millimeter-wave backscattering measurements with transmitarrays for personal radar applications

The concept of personal radar has recently emerged as an interesting solution for next 5G applications. In fact the high portability of massive antenna arrays at millimeter-waves enables the integration of a radar system in pocket-size devices (i.e. tablets or smartphones) and enhances the possibility to map the surrounding environment by guaranteeing accurate localization together with high-speed communication capabilities. In this paper we investigate for the first time the capability of such personal radar solution using real measured data collected at millimeter-waves as input for the mapping algorithm

5G mmwave positioning for vehicular networks

5G technologies present a new paradigm to provide connectivity to vehicles, in support of high data-rate services, complementing existing inter-vehicle communication standards based on IEEE 802.11p. As we argue, the specific signal characteristics of 5G communication turn out to be highly conducive for vehicle positioning. Hence, 5G can work in synergy with existing on-vehicle positioning and mapping systems to provide redundancy for certain applications, in particular automated driving. This article provides an overview of the evolution of cellular positioning and discusses the key properties of 5G as they relate to vehicular positioning. Open research challenges are presented

The Fundamentals of Radar with Applications to Autonomous Vehicles

Radar systems can be extremely useful for applications in autonomous vehicles. This paper seeks to show how radar systems function and how they can apply to improve autonomous vehicles. First, the basics of radar systems are presented to introduce the basic terminology involved with radar. Then, the topic of phased arrays is presented because of their application to autonomous vehicles. The topic of digital signal processing is also discussed because of its importance for all modern radar systems. Finally, examples of radar systems based on the presented knowledge are discussed to illustrate the effectiveness of radar systems in autonomous vehicles