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Beamforming
Published in S. Sitharama Iyengar, Richard R. Brooks, Distributed Sensor Networks, 2016
Beamforming is a space–time operation in which a waveform originating from a given source but received at spatially separated sensors is coherently combined in a time-synchronous manner. If the propagation medium preserves sufficient coherency among the received waveforms, then the beam-formed waveform can provide an enhanced signal-to-noise ratio (SNR) compared with a single sensor system. Beamforming can be used to determine the direction-of-arrival(s) (DOAs) and the location(s) of the source(s), as well as perform spatial filtering of two (or more) closely spaced sources. Beamforming and localization are two interlinking problems, and many algorithms have been proposed to tackle each problem individually and jointly (i.e. localization is often needed to achieve beamforming and some localization algorithms take the form of a beamformer). The earliest development of space–time processing was for enhancing SNR in communicating between the United States and the United Kingdom dating back before the World War II [1]. Phase-array antennas based upon beamforming for radar and astronomy were developed in the 1940s [2]. Since then, phase-array antennas utilizing broad ranges of radio frequencies (RFs) have been used for diverse military and civilian ground, airborne, and satellite applications. Similarly, sonar beamforming arrays have been used for more than 50 years.
Vehicle Localization in GNSS-Denied Environments
Published in Chao Gao, Guorong Zhao, Hassen Fourati, Cooperative Localization and Navigation, 2019
Ramtin Rabiee, Ian Bajaj, Wee Peng Tay
Angle of arrival (AoA), sometimes called direction of arrival (DoA), is another source of information available for a wireless communication setup. The most common method to obtain AoA is by using an antenna array, the details of which are not covered in the scope of this chapter. But for completeness, if the orientation (or reference direction) is known, the receiver can find the absolute direction from which the transmitted signal from a known source/transmitter has been received. Therefore, as depicted in Figure 11.4a, the two absolute AoAs from two transmitters with known positions form a triangle and converge at a third vertex, which is the position of the receiver.
Direction-of-Arrival Estimation in Mobile Communication Environments
Published in Lal Chand Godara, Handbook of Antennas in Wireless Communications, 2018
Mats Viberg, Thomas Svantesson
The underlying physical principle of all antenna array algorithms is the fact that a transmitted signal propagates along some path and then arrives at the receiving antenna. Typically, several versions of the transmitted signal impinge on the receiving antenna from different directions because of multipath. In fact, the directions from which the signals arrive, the direction of arrival (DOA) is an important property when characterizing the channel as well as designing receiver algorithms. For instance, the wireless channel changes very rapidly resulting from movement of both the users as well as changes in the surrounding environment. However, the main directions of arrival do not change nearly as rapidly. Thus, characterization of the channel in terms of DOA is an interesting alternative to standard models. Another important channel property that determines the quality of the communication link is the angular spread that is closely connected to the DOA. The angular spread, among others, essentially determines the diversity gain by using an antenna array. Also, it has been proposed to employ antenna arrays to reduce the co-channel interference by transmitting energy only in the direction of a specific user and essentially no energy in the directions of other users. In these types of systems, estimating DOA forms an integral part of the system. For example, it may assist to form beams in both uplink and downlink processing — see [76, 154, 162]. Furthermore, as the mobile phone becomes more ubiquitous, the interest of employing the phone for personal locating services increases. Here, the DOA can be used for obtaining the location of the mobile phone. Thus, there are many reasons for employing DOA estimation in wireless communications.
Use of real time localization systems (RTLS) in the automotive production and the prospects of 5G – A literature review
Published in Production & Manufacturing Research, 2022
Christoph Küpper, Janina Rösch, Herwig Winkler
The Angle of Arrival estimation (AOA – also called Direction of Arrival DOA) allows base stations to calculate the angle of arrival of the transmitted signal. For this, the base stations need antenna arrays (Xiong and Jamieson 2013). These antenna arrays can use the incoming or outgoing signal to determine the direction from which this signal was transmitted. Thus, the position in the plane can already be determined with two base stations (three for the determination in space) (Peng and Sichitiu 2006). The position of the base stations must be known (see Figure 7). Although the base stations and the target object do not have to be synchronized, the base stations are characterized by higher investment and energy costs. The accuracy is also influenced by noise, NLOS and multipath (Ma et al. 2018). The same concept can be used with the Angle of Departure (AOD). (Al-Kadi and Zorn 2020)
A novel displaced coprime parallel array for two-dimensional direction of arrival estimation
Published in International Journal of Electronics, 2022
Direction-of-arrival (DOA) estimation plays an important role in array signal processing, and has been widely applied to the field of radar, sonar, communication (He et al., 2012; Li et al., 2018a; Malioutov et al., 2005; Zeng et al., 2006). Angle information can be identified utilising various technologies. If simply one-dimensional (1D) linear array is used, it will not obtain the accurate three-dimensional (3D) angle location and only acquire 1D angle, azimuth. DOA estimation based on two-dimensional (2D) arrays, such as rectangular arrays (RA), L-shaped arrays (LA), and parallel arrays (PA), is required to achieve more precise angle information, containing azimuth and elevation, in 3D space. However, limited degree of freedom (DOF) caused by compact elements distribution, and engineering cost resulted from massive dense sensors in uniform linear array (ULA), encourages the development of sparse arrays with fewer sensors to realise better estimation.
Design of novel sparse arrays for DOA estimation of noncircular sources
Published in International Journal of Electronics, 2021
Fuhong Zeng, Weijian Si, Zhanli Peng
Direction of arrival (DOA) estimation is an important topic in the field of array signal processing, which has wide applications in radar, sonar, and wireless communications, etc. (Krim & Viberg, 1996; Vanderveen et al., 1998). Recently, sparse arrays, e.g. nested arrays (NAs) (Pal & Vaidyanathan, 2010) and coprime arrays (CAs) (Pal & Vaidyanathan, 2011), have attracted much attention due to their superior DOA estimation performance. Normally, NA consists of two nested uniform linear subarrays, of which the dense one suffers from heavy mutual coupling due to the small inter-element spacing. Hence, CA is more attractive than NA owing to its larger inter-sensor spacing. However, the difference coarray (DCA) of CA contains holes, which greatly limits the number of estimable sources. To address this problem, many modifications of CA were presented, e.g. generalised CA (Qin et al., 2015), thinned CA (Raza et al., 2019), and -times extended CA (X. Wang & Wang, 2019), etc.