Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods

Abstract

Remarkable progress in radio frequency and micro-electro-mechanical systems integrated circuit design over the last two decades has enabled the use of wireless sensor networks with thousands of nodes. It is foreseen that the fifth generation of networks will provide significantly higher bandwidth and faster data rates with potential for interconnecting myriads of heterogeneous devices (sensors, agents, users, machines, and vehicles) into a single network (of nodes), under the notion of Internet of Things. The ability to accurately determine the physical location of each node (stationary or moving) will permit rapid development of new services and enhancement of the entire system. In outdoor environments, this could be achieved by employing global navigation satellite system (GNSS) which offers a worldwide service coverage with good accuracy. However, installing a GNSS receiver on each device in a network with thousands of nodes would be very expensive in addition to energy constraints. Besides, in indoor or obstructed environments (e.g., dense urban areas, forests, and canyons) the functionality of GNSS is limited to non-existing, and alternative methods have to be adopted. Many of the existing alternative solutions are centralized, meaning that there is a sink in the network that gathers all information and executes all required computations. This approach quickly becomes cumbersome as the number of nodes in the network grows, creating bottle-necks near the sink and high computational burden. Therefore, more effective approaches are needed. As such, this work presents a survey (from a signal processing perspective) of existing distributed solutions, amalgamating two radio measurements, received signal strength (RSS) and angle of arrival (AOA), which seem to have a promising partnership. The present article illustrates the theory and offers an overview of existing RSS-AOA distributed solutions, as well as their analysis from both localization accuracy and computational complexity points of view. Finally, the article identifies potential directions for future research.

Multi-Target Localization Based on Unidentified Multiple RSS/AOA Measurements in Wireless Sensor Networks

“…Most existing studies regarding multi-target detection in WSNs assume that the targets are sparsely located over networks and, therefore, each anchor node contains a single set of RSS/AOA measurements from a single target. Various multi-target localization methods that exploit the network topology [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ] or apply compressive sensing [ 25 , 26 , 27 ] have been proposed. Some existing studies assume that each anchor node can identify the origin of the RSS/AOA measurements using, for example, code division multiplexing access codes [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], therefore solving multiple single-target localization problems.…”

An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks

“…Recent increased interest in wireless sensor networks (WSNs) in many fields is due to their ease of implementation, ability to operate in harsh environments, and relatively low cost [ 1 , 2 ]. Mainly for these reasons, many researchers gave rise to various investigation projects [ 3 , 4 , 5 , 6 ].…”

Asymptotic Performance Analysis of the MUSIC Algorithm for Direction-of-Arrival Estimation

“…In [22], a survey on RSS and AOA-based localization scheme is presented. For the localization scheme to be successful, accurate estimation of RSS and AOA is very important.…”

“…In Table 1, algorithms, applications, main ideas and contributions of the recently published works are tabulated. In Table 2, it is tabulated how the schemes presented in [17][18][19][20][21][22][23][24][25][26] are validated. In Table 2, it is also described how the explicit expressions of the azimuth estimate and the MSE of the azimuth estimation error derived in this paper can be applied to the problems in [17][18][19][20][21][22][23][24][25][26].…”

“…How to validate the schemes presented in [17][18][19][20][21][22][23][24][25][26], and how to apply the derivations of this paper to the problems in [17][18][19][20][21][22][23][24][25][26]. [17][18][19][20][21][22][23][24][25][26] to the Problems in [17][18][19][20][21][22][23][24][25][26] [17] Agreement between analytic MSE [17]: Performance analysis of amplitude and simulation-based MSE comparison monopulse algorithm. This paper: Performance analysis of the MUSIC algorithm [18] Reduction in estimation error of the The analytic expression of the MSE of motion compensated conventional motion-compensated Bartlett beamforming beamforming algorithm in comparison with algorithm can be obtained by modifying the estimation error of original the derivation in this paper conventional beamforming algorithm without motion compensation in the DOA spectrum [19] Superiority of performance of SCC-The analytic expression of the MSE of MVDR algorithm over MVDR algorithm SCC-MVDR algorithm can be o...…”