Two-Dimensional Sparse Arrays with Hole-Free Coarray and Reduced Mutual Coupling

Two-dimensional sparse arrays with hole-free difference coarrays, like billboard arrays and open box arrays, can identify O(N^2) uncorrelated source directions (DOA) using N sensors. These arrays contain some dense ULA segments, leading to many sensor pairs separated by λ/2. The DOA estimation performance often suffers degradation due to mutual coupling between such closely-spaced sensor pairs. This paper introduces a new 2D array called the half open box array. For a given N, this array has the same hole-free coarray as an open box array. At the same time, the number of sensor pairs with small separation is significantly reduced

Applications of Antenna Technology in Sensors

During the past few decades, information technologies have been evolving at a tremendous rate, causing profound changes to our world and to our ways of living. Emerging applications have opened u[ new routes and set new trends for antenna sensors. With the advent of the Internet of Things (IoT), the adaptation of antenna technologies for sensor and sensing applications has become more important. Now, the antennas must be reconfigurable, flexible, low profile, and low-cost, for applications from airborne and vehicles, to machine-to-machine, IoT, 5G, etc. This reprint aims to introduce and treat a series of advanced and emerging topics in the field of antenna sensors

Sparse Linear Antenna Arrays: A Review

Linear sparse antenna arrays have been widely studied in array processing literature. They belong to the general class of non-uniform linear arrays (NULAs). Sparse arrays need fewer sensor elements than uniform linear arrays (ULAs) to realize a given aperture. Alternately, for a given number of sensors, sparse arrays provide larger apertures and higher degrees of freedom than full arrays (ability to detect more source signals through direction-of-arrival (DOA) estimation). Another advantage of sparse arrays is that they are less affected by mutual coupling compared to ULAs. Different types of linear sparse arrays have been studied in the past. While minimum redundancy arrays (MRAs) and minimum hole arrays (MHAs) existed for more than five decades, other sparse arrays such as nested arrays, co-prime arrays and super-nested arrays have been introduced in the past decade. Subsequent to the introduction of co-prime and nested arrays in the past decade, many modifications, improvements and alternate sensor array configurations have been presented in the literature in the past five years (2015–2020). The use of sparse arrays in future communication systems is promising as they operate with little or no degradation in performance compared to ULAs. In this chapter, various linear sparse arrays have been compared with respect to parameters such as the aperture provided for a given number of sensors, ability to provide large hole-free co-arrays, higher degrees of freedom (DOFs), sharp angular resolutions and susceptibility to mutual coupling. The chapter concludes with a few recommendations and possible future research directions

The influence of random element displacement on DOA estimates obtained with (Khatri-Rao-)root-MUSIC

Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case

Antenna Systems

This book offers an up-to-date and comprehensive review of modern antenna systems and their applications in the fields of contemporary wireless systems. It constitutes a useful resource of new material, including stochastic versus ray tracing wireless channel modeling for 5G and V2X applications and implantable devices. Chapters discuss modern metalens antennas in microwaves, terahertz, and optical domain. Moreover, the book presents new material on antenna arrays for 5G massive MIMO beamforming. Finally, it discusses new methods, devices, and technologies to enhance the performance of antenna systems

Array imperfection calibration for wireless channel multipath characterisation

As one of the fastest growing technologies in modern telecommunications, wireless networking has become a very important and indispensable part in our life. A good understanding of the wireless channel and its key physical parameters are extremely useful when we want to apply them into practical applications. In wireless communications, the wireless channel refers to the propagation of electromagnetic radiation from a transmitter to a receiver. The estimation of multipath channel parameters, such as angle of depature (AoD), angle of arrival (AoA), and time difference of arrival (TDoA), is an active research problem and its typical applications are radar, communication, vehicle navigation and localization in the indoor environment where the GPS service is impractical. However, the performance of the parameter estimation deteriorates significantly in the presence of array imperfections, which include the mutual coupling, antenna location error, phase uncertainty and so on. These array imperfections are hardly to be calibrated completely via antenna design. In this thesis, we experimentally evaluate an B matrix method to cope with these array imperfection, our results shows a great improvement of AoA estimation results