Figure 4 - uploaded by Abdallah Alma'aitah

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# Three-dimensional (3D) two-sheeted hyperbola around a horizontal antenna array (in red). The asymptotes provide a two cones around the two-sheeted hyperbola.

Source publication

Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a...

## Contexts in source publication

**Context 1**

... red line represents a hyperbola with its foci antenna 1 and antenna 2. Note that the angle between the hyperbola's asymptotes is denoted by θ h = π − ϕ h . To adapt the above relation between the tag location and the horizontal angle to a 3D space, for every measured θ h there exists a 3D two-sheeted hyperbola on which the tag can reside as shown in Figure 4 by the red two sheets (which is the rotation of the 2D hyperbola and its asymptotes in Figure 3). The outer cones around the two red hyperbola sheets in Figure 4 are the asymptotes sheets of that given hyperbola. ...

**Context 2**

... adapt the above relation between the tag location and the horizontal angle to a 3D space, for every measured θ h there exists a 3D two-sheeted hyperbola on which the tag can reside as shown in Figure 4 by the red two sheets (which is the rotation of the 2D hyperbola and its asymptotes in Figure 3). The outer cones around the two red hyperbola sheets in Figure 4 are the asymptotes sheets of that given hyperbola. For any given reader R n with a known position at (x n , y n , z n ), the two focal points of any hyperbola are the two antennas, as shown in Figure 3, with a difference of 2c = λ 2 The equation of the resulting two-sheeted hyperbola for a tag at a location (x k , y k , z k ) is given by: ...

## Citations

... In [12], a novel Kalman filter based on the maximum correntropy criterion was designed for the ToA applications, which achieved higher accuracy compared with the traditional Kalman filter. Moreover, in terms of the AoA schemes, an empirical location system in a three-dimensions (3D) space was proposed with fixed readers (antennas) placement [13]. What is more, with sensor position error, a constrained eigenspace technique was implemented in the AoA system using a modified polar representation to reduce the computation or the bias [14]. ...

Among many prevalent acoustic location techniques, the location problems can be modelled as solving a linear equation. Although many mature algorithms have been developed to solve the linear equation in acoustic location applications, few of them consider the inevitable noises in a real computing system that might degrade the convergence and accuracy of the algorithms or even lead to failure. Thus, to achieve promising performance when solving a linear equation in a noisy environment, a robust Newton iterative (RNI) algorithm is proposed in this paper based on control theory. Theoretical analyses indicated that the RNI algorithm can not only suppress the constant noise to zero but also maintain convergence against an increasing linear noise and random noise. In addition, extensive simulation results compared with the classic algorithms and their last variants are provided. Among these algorithms, the RNI algorithm achieves the best robustness and accuracy in the presence of noises, while it requires a longer convergence time.

... These systems can be applied to a wide variety of areas, such as military applications [1,2], target tracking, electronic intelligence systems [3], perimeter protection systems [4], or location-based services [5]. The most commonly used approaches, or techniques, for measuring a target position are Time of Arrival (TOA) [6], Time Difference of Arrival (TDOA) [7,8], Received Signal Strength (RSS) [9], Doppler Difference (DD) [10], Angle of Arrival (AOA) [11], and combinations of these techniques [12,13]. The localization techniques can be described by a number of different features, such as accuracy, complexity, ambiguity, cost, etc. ...

... cos (12) Similarly, the center , of the circle C2 and its radius r2 can be determined. Note that in this case, the sensor coordinates are * 0, 0 and * , 0 in the new coordinate system and the angle tan is used in transformation Equations (6), (7), (11), and (12). Finally, the target position can be found as an intersection of the circles C1 and C2. ...

In this article, a new technique for determination of 2D signal source (target) position is proposed. This novel approach, called the Inscribed Angle (InA), is based on measuring the time difference of sequential irradiation by the main beam of the target antenna’s radiation pattern, using Electronic Support Measures (ESM) receivers, assuming that the target antenna is rotating and that its angular velocity is constant. In addition, it is also assumed that the localization system operates in a LOS (Line of Sight) situation and that three time-synchronized sensors are placed arbitrarily across the area. The main contribution of the article is a complete description of the proposed localization method. That is, this paper demonstrates a geometric representation and an InA localization technique model. Analysis of the method’s accuracy is also demonstrated. The time of irradiation of the receiving station corresponds to the direction in which the maximum received signal strength (RSS) was measured. In order to achieve a certain degree of accuracy of the proposed positioning technique, a method was derived to increase the accuracy of the irradiation time estimation. Finally, extensive simulation was conducted to demonstrate the performance and accuracy of our positioning method.

... Additionally, the paper looks at RFID signals, while we look at acoustic signals, which are more ubiquitous but also prone to more noise. (Alma'aitah et al., 2019). Another paper looks at doing acoustic based angle of arrival estimation for robotics. ...

In this paper, we primarily explore the improvement of single stream audio systems using Angle of Arrival calculations in both simulation and real life gathered data. We wanted to learn how to discern the direction of an audio source from gathered signal data to ultimately incorporate into a multi modal security system. We focused on the MUSIC algorithm for the estimation of the angle of arrival but briefly experimented with other techniques such as Bartlett and Capo. We were able to implement our own MUSIC algorithm on stimulated data from Cornell. In addition, we demonstrated how we are able to calculate the angle of arrival over time in a real life scene. Finally, we are able to detect the direction of arrival for two separate and simultaneous audio sources in a real life scene. Eventually, we could incorporate this tracking into a multi modal system combined with video. Overall, we are able to produce compelling results for angle of arrival calculations that could be the stepping stones for a better system to detect events in a scene.

... There are several techniques to estimate the target position based on different information available from measurements performed on received radio frequency (RF) signals. This means that RF based localization systems may use a multitude of different techniques, which include the angle of arrival (AoA) [4,5], received signal strength (RSS), time of arrival (ToA), time difference of arrival (TDoA) [6][7][8], Doppler difference (DD) [9,10], or hybrid location methods [11]. While some localization techniques usually come at a low cost but a lower accuracy, others require complex synchronization schemes, which usually make them more expensive [12][13][14][15][16]. ...

In this paper, we propose a new approach to passively locate the 3D position of a signal source. This novel technique, called the power gain difference (PGD), is based only on measuring the received signal strength (RSS) with multiple sensors deployed in the area of interest, while the target transmit power or the equivalent isotropic radiated power (EIRP) is assumed to be unknown. Next, the signal source position is estimated using the knowledge of the ratios of RSS measured on different sensors. First, this article presents the geometric representation and the analytical solution of the model of the PGD technique. Second, the PGD dilution of precision was analyzed in order to gauge the accuracy of measuring the RSS. Finally, a numerical simulation of the performance of the proposed method was carried out and the results are discussed. It seems that the PGD technique has the potential to be a simple and effective solution of the 3D localization problem.