ANGLE: ANGular Location Estimation Algorithms

Abstract

In this paper, we present two localization algorithms that exploit the Angle of Arrival (AoA) parameters of the received signal. The proposed ANGular Location Estimation (ANGLE) algorithms utilize a probabilistic model to describe the angular response of the received signal. Consequently, the ANGLE algorithms can estimate the location of a transmitter using a single step Hadamard product. The first algorithm utilizes a Single Sample of the received signal (ANGLE-SS). The second algorithm, on the other hand, employs the signal Subspace Decomposition technique (ANGLE-SD). The localization capabilities of the ANGLE algorithms have been experimentally investigated in an office environment. The performances of the ANGLE algorithms have been validated against the performances of several AoA-based localization systems. The experimental results show that the ANGLE-SD algorithm outperforms all the studied AoA-based localization systems. The ANGLE-SS algorithm, on the other hand, outperforms every localization system that utilizes less than 50 samples of the received signal. The ANGLE algorithms are flexible, generic and computationally very efficient. These features allow the ANGLE algorithms to be easily deployed in any existing AoA-based localization system. INDEX TERMS Angle of arrival, AoA, direction of arrival, DoA, AoA-based localization systems, indoor localization systems.

AoA-Based Localization System Using a Single IoT Gateway: An Application for Smart Pedestrian Crossing

“…Over the past decades, several techniques have been developed and deployed to provide localization solutions. These techniques depend on either the Received Signal Strength (RSS), the Time of Arrival (ToA), the Time Difference of Arrival (TDoA) or the Angle of Arrival (AoA) parameters of the received signals [14]. RSS and time-based approaches estimate the distance between the transmitter and the receiver by measuring the signal strength and the travel time of the received signal, respectively.…”

“…Combining the direction information can provide a location estimate of the transmitting device. Accordingly, AoA-based localization solutions based on multiple AoA estimation systems have been discussed, thoroughly, in the literature [14], [17]- [21]. For instance, Tomic et al [21] provided a solution to localize outpatients in a hospital.…”

“…Over the years, several AoA estimation algorithms have been proposed [14]. In general, there are four main kinds of algorithms; algorithms that are based on an optimal beamforming, algorithms that rely on signal and noise subspaces' decomposition, algorithms that employ the parametric search using maximum likelihood (ML) estimator, and algorithms that apply the sparse representation of the space.…”

“…The resampling algorithm constitutes three nested loops. The first while loop (lines [4][5][6][7][8][9][10][11][12][13][14] ensures the update of all the particles. The for loop (lines 5-13), on the other hand, calculates the cumulative sum value β i of the particles' weight.…”

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“…The traditional indoor positioning algorithms are mainly based on time of arrival (TOA) ranging [6], time difference of arrival (TDOA) ranging [7], received signal strength indicator (RSSI) ranging [8], angle of arrival (AOA) ranging [9], fingerprint matching [10], etc. In 2017, Yang et al proposed a KFL-TOA indoor positioning method based on ultra-wideband (UWB) for the positioning problem in complex indoor environments.…”